CSQE reality - Certified Software Quality Engineer Certification (CSQE) Updated: 2024 | ||||||||
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Exam Code: CSQE Certified Software Quality Engineer Certification (CSQE) reality January 2024 by Killexams.com team | ||||||||
CSQE Certified Software Quality Engineer Certification (CSQE) The Certified Software Quality Engineer (CSQE) understands software quality development and implementation, software inspection, testing, and verification and validation; and implements software development and maintenance processes and methods.CSQEComputer Delivered – The CSQE examination is a one-part, 175-question, four-and-a-half-hour test and is offered in English only. One hundred and sixty questions are scored and 15 are unscored.Paper and Pencil – The CSQEexamination is a one-part, 160-question, four-hour test and is offered in English only. Work experience must be in a full-time, paid role. Paid intern, co-op or any other course work cannot be applied toward the work experience requirement. Candidates must have eight years of on-the-job experience in one or more of the areas of the Certified Software Quality Engineer Body of Knowledge. A minimum of three years of this experience must be in a decision-making position. ("Decision-making" is defined as the authority to define, execute, or control projects/processes and to be responsible for the outcome. This may or may not include management or supervisory positions.) For candidates who were certified by ASQ as a quality auditor, reliability engineer, supplier quality professional, quality engineer or quality manager, the experience used to qualify for certification in these fields applies to certification as a software quality engineer. Here are the minimum expectations of a Certified Software Quality Engineer. Must possess a fundamental understanding of quality philosophies, principles, methods, tools, standards, organizational and team dynamics, interpersonal relationships, professional ethics, and legal and regulatory requirements. Must evaluate the impact of software quality management principles on business objectives and demonstrate comprehensive knowledge of developing and implementing software quality programs, which include tracking, analyzing, reporting, problem resolution, process improvement, training, and supplier management. Must have a basic understanding of how and when to perform software audits including audit planning, approaches, types, analyses, reporting results and follow-up. Must understand systems architecture and be able to implement software development and maintenance processes, quantify the fundamental problems and risks associated with various software development methodologies, and assess, support, and implement process and technology changes. Must be able to apply project management principles and techniques as they relate to software project planning, implementation and tracking. Must be able to evaluate and manage risk. Must select, define and apply product and process metrics and analytical techniques, and have an understanding of measurement theory and how to communicate results. Must have a thorough understanding of verification and validation processes, including early software defect detection and removal, inspection, and testing methods (e.g., types, levels, strategies, tools and documentation). Must be able to analyze test strategies, develop test plans and execution documents, and review customer deliverables. Must have a basic understanding of configuration management processes, including planning, configuration identification, configuration control, change management, status accounting, auditing and reporting. Must assess the effectiveness of product release and archival processes. Certification from ASQ is considered a mark of quality excellence in many industries. It helps you advance your career, and boosts your organizations bottom line through your mastery of quality skills. Becoming certified as a Software Quality Engineer confirms your commitment to quality and the positive impact it will have on your organization. ExaminationEach certification candidate is required to pass an examination that consists of multiple-choice questions that measure comprehension of the body of knowledge. I. General Knowledge (16 questions)A. Benefits of Software Quality Engineering Within the OrganizationDescribe the benefits that software quality engineering can have at the organizational level. (Understand)B. Ethical and Legal Compliance 1. ASQ code of ethics for professional conductDetermine appropriate behavior in situations requiring ethical decisions, including identifying conflicts of interest, recognizing and resolving ethical issues, etc. (Evaluate)2. Regulatory and legal issuesDescribe the importance of compliance to federal, national, and statutory regulations on software development. Determine the impact of issues such as copyright, intellectual property rights, product liability, and data privacy. (Understand) C. Standards and ModelsDefine and describe the ISO 9000 and IEEE software standards, and the SEI Capability Maturity Model Integration (CMMI) for development, services, and acquisition assessment models. (Understand)D. Leadership Skills1. Organizational leadershipUse leadership tools and techniques (e.g., organizational change management, knowledge transfer, motivation, mentoring and coaching, recognition). (Apply)2. Facilitation skillsUse facilitation and conflict resolution skills as well as negotiation techniques to manage and resolve issues. Use meeting management tools to maximize meeting effectiveness. (Apply)3. Communication skillsUse various communication methods in oral, written, and presentation formats. Use various techniques for working in multicultural environments, and identify and describe the impact that culture and communications can have on quality. (Apply)E. Team Skills1. Team managementUse various team management skills, including assigning roles and responsibilities, identifying the classic stages of team development (forming, storming, norming, performing, adjourning), monitoring and responding to group dynamics, working with diverse groups and in distributed work environments, and using techniques for working with virtual teams. (Apply)2. Team toolsUse decision-making and creativity tools such as brainstorming, nominal group technique (NGT), and multi-voting. (Apply) II. Software Quality Management (22 questions)A. Quality Management System1. Quality goals and objectivesDesign software quality goals and objectives that are consistent with business objectives. Incorporate software quality goals and objectives into high-level program and project plans. Develop and use documents and processes necessary to support software quality management systems. (Create)2. Customers and other stakeholdersDescribe and analyze the effect of various stakeholder group requirements on software projects and products. (Analyze)3. OutsourcingDetermine the impact that outsourced services can have on organizational goals and objectives, and identify criteria for evaluating suppliers/vendors and subcontractors. (Analyze)4. Business continuity, data protection, and data managementDesign plans for business continuity, disaster recovery, business documentation and change management, information security, and protection of sensitive and personal data. (Analyze) B. Methodologies1. Cost of quality (COQ) and return on investment (ROI)Analyze COQ categories (prevention, appraisal, internal failure, external failure) and return on investment (ROI) metrics in relation to products and processes. (Analyze)2. Process improvement Define and describe elements of benchmarking, lean processes, the Six Sigma methodology, and use define, measure, act, improve, control (DMAIC) model and the plan-do-check-act (PDCA) model for process improvement. (Apply)3. Corrective action procedures Evaluate corrective action procedures related to software defects, process nonconformances, and other quality system deficiencies. (Evaluate)4. Defect prevention Design and use defect prevention processes such as technical reviews, software tools and technology, and special training. (Evaluate)C. Audits1. Audit typesDefine and distinguish between various audit types, including process, compliance, supplier, and system. (Understand)2. Audit roles and responsibilitiesIdentify roles and responsibilities for audit participants including client, lead auditor, audit team members, and auditee. (Understand)3. Audit processDefine and describe the steps in conducting an audit, developing and delivering an audit report, and determining appropriate follow-up activities. (Apply)III. System and Software Engineering Processes (32 questions)A. Life Cycles and Process Models1. Waterfall software development life cycleApply the waterfall life cycle and related process models, and identify their benefits and when they are used. (Apply)2. Incremental/iterative software development life cyclesApply the incremental and iterative life cycles and related process models, and identify their benefits and when they are used. (Apply) Agile software development life cycleApply the agile life cycle and related process models, and identify their benefits and when they are used. (Apply)B. Systems ArchitectureIdentify and describe various architectures, including embedded systems, client-server, n-tier, web, wireless, messaging, and collaboration platforms, and analyze their impact on quality. (Analyze)C. Requirements Engineering1. Product requirements Define and describe various types of product requirements, including system, feature, function, interface, integration, performance, globalization, and localization. (Understand)2. Data/information requirements Define and describe various types of data and information requirements, including data management and data integrity. (Understand)3. Quality requirements Define and describe various types of quality requirements, including reliability and usability. (Understand) 4. Compliance requirementsDefine and describe various types of regulatory and safety requirements. (Understand)5. Security requirementsDefine and describe various types of security requirements including data security, information security, cybersecurity, and data privacy. (Understand)6. Requirements elicitation methodsDescribe and use various requirements elicitation methods, including customer needs analysis, use cases, human factors studies, usability prototypes, joint application development (JAD), storyboards, etc. (Apply)7. Requirements evaluationAssess the completeness, consistency, correctness, and testability of requirements, and determine their priority. (Evaluate)D. Requirements Management1. Requirements change managementAssess the impact that changes to requirements will have on software development processes for all types of life-cycle models. (Evaluate)2. Bidirectional traceabilityUse various tools and techniques to ensure bidirectional traceability from requirements elicitation and analysis through design and testing. (Apply)E. Software Analysis, Design, and Development1. Design methodsIdentify the steps used in software design and their functions, and define and distinguish between software design methods. (Understand)2. Quality attributes and designAnalyze the impact that quality-related elements (safety, security, reliability, usability, reusability, maintainability) can have on software design. (Analyze)3. Software reuseDefine and distinguish between software reuse, reengineering, and reverse engineering, and describe the impact these practices can have on software quality. (Understand)4. Software development toolsAnalyze and select the appropriate development tools for modeling, code analysis, requirements management, and documentation. (Analyze)F. Maintenance Management1. Maintenance typesDescribe the characteristics of corrective, adaptive, perfective, and preventive maintenance types. (Understand)2. Maintenance strategyDescribe various factors affecting the strategy for software maintenance, including service-level agreements (SLAs), short- and long-term costs, maintenance releases, and product discontinuance, and their impact on software quality. (Understand)3. Customer feedback managementDescribe the importance of customer feedback management including quality of product support and post-delivery issues analysis and resolution. (Understand)IV. Project Management (22 questions)A. Planning, Scheduling, and Deployment1. Project planningUse forecasts, resources, schedules, task and cost estimates, etc., to develop project plans. (Apply)2. Work breakdown structure (WBS) Use work breakdown structure (WBS) in scheduling and monitoring projects. (Apply)3. Project deploymentUse various tools, including milestones, objectives achieved, and task duration to set goals and deploy the project. (Apply) B. Tracking and Controlling1. Phase transition controlUse various tools and techniques such as entry/exit criteria, quality gates, Gantt charts, integrated master schedules, etc., to control phase transitions. (Apply)2. Tracking methodsCalculate project-related costs, including earned value, deliverables, productivity, etc., and track the results against project baselines. (Apply)3. Project reviewsUse various types of project reviews such as phase-end, management, and retrospectives or post-project reviews to assess project performance and status, to review issues and risks, and to discover and capture lessons learned from the project. (Apply)4. Program reviewsDefine and describe various methods for reviewing and assessing programs in terms of their performance, technical accomplishments, resource utilization, etc. (Understand)C. Risk Management1. Risk management methodsUse risk management techniques (e.g., assess, prevent, mitigate, transfer) to evaluate project risks. (Evaluate)2. Software security risksEvaluate risks specific to software security, including deliberate attacks (hacking, sabotage, etc.), inherent defects that allow unauthorized access to data, and other security breaches. Plan appropriate responses to minimize their impact. (Evaluate)3. Safety and hazard analysisEvaluate safety risks and hazards related to software development and implementation and determine appropriate steps to minimize their impact. (Evaluate)V. Software Metrics and Analysis (19 questions)A. Process and Product Measurement1. Terminology Define and describe metric and measurement terms such as reliability, internal and external validity, explicit and derived measures, and variation. (Understand)2. Software product metricsChoose appropriate metrics to assess various software attributes (e.g., size, complexity, the amount of test coverage needed, requirements volatility, and overall system performance). (Apply)3. Software process metricsMeasure the effectiveness and efficiency of software processes (e.g., functional verification tests (FVT), cost, yield, customer impact, defect detection, defect containment, total defect containment effectiveness (TDCE), defect removal efficiency (DRE), process capability). (Apply)4. Data integrity Describe the importance of data integrity from planning through collection and analysis and apply various techniques to ensure data quality, accuracy, completeness, and timeliness. (Apply)B. Analysis and Reporting Techniques1. Metric reporting tools Using various metric representation tools, including dashboards, stoplight charts, etc., to report results. (Apply)2. Classic quality toolsDescribe the appropriate use of classic quality tools (e.g., flowcharts, Pareto charts, cause and effect diagrams, control charts, and histograms). (Apply) 3. Problem-solving toolsDescribe the appropriate use of problem solving tools (e.g., affinity and tree diagrams, matrix and activity network diagrams, root cause analysis and data flow diagrams [DFDs]). (Apply)VI. Software Verification and Validation (29 questions)A. Theory1. V&V methods Use software verification and validation methods (e.g., static analysis, structural analysis, mathematical proof, simulation, and automation) and determine which tasks should be iterated as a result of modifications. (Apply)2. Software product evaluationUse various evaluation methods on documentation, source code, etc., to determine whether user needs and project objectives have been satisfied. (Analyze)B. Test Planning and Design1. Test strategies Select and analyze test strategies (e.g., test-driven design, good-enough, risk-based, time-box, top-down, bottom-up, black-box, white-box, simulation, automation, etc.) for various situations. (Analyze) 2. Test plansDevelop and evaluate test plans and procedures, including system, acceptance, validation, etc., to determine whether project objectives are being met and risks are appropriately mitigated. (Create)3. Test designsSelect and evaluate various test designs, including fault insertion, fault-error handling, equivalence class partitioning, and boundary value. (Evaluate)4. Software testsIdentify and use various tests, including unit, functional, performance, integration, regression, usability, acceptance, certification, environmental load, stress, worst-case, perfective, exploratory, and system. (Apply)5. Tests of external products Determine appropriate levels of testing for integrating supplier, third-party, and subcontractor components and products. (Apply)6. Test coverage specificationsEvaluate the adequacy of test specifications such as functions, states, data and time domains, interfaces, security, and configurations that include internationalization and platform variances. (Evaluate)7. Code coverage techniquesUse and identify various tools and techniques to facilitate code coverage analysis techniques such as branch coverage, condition, domain, and boundary. (Apply)8. Test environmentsSelect and use simulations, test libraries, drivers, stubs, harnesses, etc., and identify parameters to establish a controlled test environment. (Analyze)9. Test toolsIdentify and use test utilities, diagnostics, automation, and test management tools. (Apply)10. Test data managementEnsure the integrity and security of test data through the use of configuration controls. (Apply)C. Reviews and InspectionsUse desk checks, peer reviews, walk-throughs, inspections, etc., to identify defects. (Apply)D. Test Execution DocumentsReview and evaluate test execution documents such as test results, defect reporting and tracking records, test completion metrics, trouble reports, and input/output specifications. (Evaluate) VII. Software Configuration Management (20 questions)A. Configuration Infrastructure1. Configuration management teamDescribe the roles and responsibilities of a configuration management group. (Understand) (NOTE: The roles and responsibilities of the configuration control board [CCB] are covered in area VII.C.2.)2. Configuration management toolsDescribe configuration management tools as they are used for managing libraries, build systems, and defect tracking systems. (Understand)3. Library processes Describe dynamic, static, and controlled library processes and related procedures, such as check-in/check-out, and merge changes. (Understand)B. Configuration Identification 1. Configuration items Describe software configuration items (baselines, documentation, software code, equipment) and identification methods (naming conventions, versioning schemes). (Understand)2. Software builds and baselinesDescribe the relationship between software builds and baselines, and describe methods for controlling builds and baselines (automation, new versions). (Understand)C. Configuration Control and Status Accounting1. Item change and version controlDescribe processes for documentation control, item change tracking, version control that are used to manage various configurations, and describe processes used to manage configuration item dependencies in software builds and versioning. (Understand)2. Configuration control board (CCB)Describe the roles, responsibilities and processes of the CCB. (Understand) (NOTE: The roles and responsibilities of the configuration management team are covered in area VII.A.1.)3. Concurrent developmentDescribe the use of configuration management control principles in concurrent development processes. (Understand)4. Status accountingDiscuss various processes for establishing, maintaining, and reporting the status of configuration items, such as baselines, builds, and tools. (Understand)D. Configuration AuditsDefine and distinguish between functional and physical configuration audits and how they are used in relation to product specification. (Understand) E. Product Release and Distribution 1. Product releaseAssess the effectiveness of product release processes (planning, scheduling, defining hardware and software dependencies). (Evaluate)2. Customer deliverablesAssess the completeness of customer deliverables including packaged and hosted or downloadable products, license keys and user documentation, and marketing and training materials. (Evaluate)3. Archival processesAssess the effectiveness of source and release archival processes (backup planning and scheduling, data retrieval, archival of build environments, retention of historical records, offsite storage). (Evaluate) | ||||||||
| Certified Software Quality Engineer Certification (CSQE) Quality-Assurance Certification reality | ||||||||
Other Quality-Assurance examsCQIA Certified Quality Improvement AssociateCSQA Certified Software Quality Analyst (CSQA) CSQE Certified Software Quality Engineer Certification (CSQE) ICBB IASSC Certified Lean Six Sigma Black Belt ICGB IASSC Certified Lean Six Sigma Green Belt ICYB IASSC Certified Lean Six Sigma Yellow Belt | ||||||||
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| Quality-Assurance CSQE Certified Software Quality Engineer Certification (CSQE) https://killexams.com/pass4sure/exam-detail/CSQE (A) Engineering effort (B) Code coverage (C) Customer surveys (D) Process maturity Answer: A Question: 84 During a functional configuration audit, a software auditors principal responsibility is to verify that the (A) product meets specifications (B) processes used in software development were performed (C) documentation of the product satisfies the contract (D) documentation accurately represents the product Answer: A Question: 85 An architectural model should be used to (A) document design procedures (B) develop a system design (C) verify code (D) deploy a system model Answer: B Question: 86 According to ISO 9001, quality records must be maintained in order to (A) demonstrate achievement of the required quality and the effective operation of the quality system (B) demonstrate progress in accordance with the associated quality plan (C) justify the current funding and staffing of the quality organization 31 (D) demonstrate that the design and coding activities have alleviated the need for unit testing Answer: A Question: 87 Which of the following is NOT a requirement of an effective software environment? (A) Ease of use (B) Capacity for incremental implementation (C) Capability of evolving with the needs of a project (D) Inclusion of advanced tools Answer: D Question: 88 Which of the following is the best resource for validation testing of an object-oriented system? (A) PERT charts (B) Use case scenarios (C) Entity relationship diagrams (D) Decomposition matrices Answer: B Question: 89 A customer satisfaction survey used the following rating scale: 1 = very satisfied 2 = satisfied 3 = neutral 4 = dissatisfied 5 = very dissatisfied This is an example of which of the following measurement scales? (A) Nominal (B) Ordinal (C) Ratio (D) Interval 32 Answer: B 33 For More exams visit https://killexams.com/vendors-exam-list Kill your test at First Attempt....Guaranteed! | ||||||||
detroitnews.com wants to ensure the best experience for all of their readers, so they built their site to take advantage of the latest technology, making it faster and easier to use. Unfortunately, your browser is not supported. Please get one of these browsers for the best experience on detroitnews.com Introduction Climbing a skyscraper for the 25th time in a day quickly erodes the novelty of virtual reality. For over three years, Attila Kardos was one of the quality assurance testers at PlayStation Liverpool, one of the testing centers of Sony Interactive Entertainment Europe. During his tenure, he worked on first-party behemoths like God of War, Horizon: Zero Dawn, and Detroit: Become Human. But today, heʼs talking about a different type of testing involving a headset and physically mimicking movements for multiple hours at your office desk. In games like Blood & Truth, where you take a starring role in action movie-esque setpieces, the fun factor of virtual reality quickly makes itself apparent. However, experiences like this can be taxing. A player can stop at any time. A tester doesnʼt have that luxury. It doesnʼt take long for the monotonous physical effort to take a toll. And then they have to show up at work the next day and do it all again. Job Simulator “Eventually, you start feeling your shoulders,” Kardos laughs. “You feel the extra muscle pain.” On average, he spent five to six hours a day in VR. He says it was “pretty intense,” to the extent that some colleagues asked management to swap to non-VR projects. Eventually, Kardos got acclimatized to working in virtual reality and recalls the experience fondly. However, during the first two weeks, heʼd often feel “detached from reality” whenever he moved his head around after clocking out from work. As companies continue to iterate on past foundations or release new platforms altogether, from PlayStation to Apple, VR is constantly innovating. But the work conditions for QA folks who learn the inside out of these technologies rarely are. Game Informer spoke to testers, artists, and engineers who worked on VR games like Beat Saber, Job Simulator, Cosmonious High, Resident Evil 4 VR, and Blood & Truth about the differences with regular testing, how elements like physicality and hygiene come into play, unorthodox workarounds, and the unsung tradition of ginger candy. Rift Into Another Dimension Into Another DimensionVR leaping into the mainstream meant new considerations for game development. And, as a consequence, testing. In order to understand the differences with traditional QA practices, itʼs important to look back at the technologyʼs origins to understand how impactful of a precedent it would set. Few studios have the institutional knowledge of Owlchemy Labs. Based in Austin, Texas, the team began experimenting with the technology when the HTC Vive and Oculus Rift were only prototypes. There was no documentation or beginnerʼs manual. As promising as this empty virtual canvas was, it was a long time until the studio saw that potential materialized. “We had a very early headset that would shock you if you touched it on the wrong part of the circuit board,” says Graeme Borland, gameplay director at Owlchemy since 2013. “[It was] that level of true prototype.” Multiple new considerations quickly came into play. Comfort, both inside and outside the headset, was paramount. As a principle, it intertwined with everything: locomotion methods, figuring out how elements like framerate and draw distances could make or break the experience, and the fact that visual bugs could lead to immediate discomfort. Cosmonious High During the first few years of the main headset companies, trial and error and experimentation led to different ideas. Unlike Owlchemyʼs Job Simulator, which had sold over a million copies by 2020, not every early project managed to successfully combine its ideas with this emerging technology. Such was the case of Rigs: Mechanized Combat League, developed by Guerrilla Cambridge and published by Sony. “It was the first version of PlayStation VR, so they had resolution problems, sweaty eyes, everything was very fresh,” Kardos says. Rigs was his first project. In it, youʼre sitting in the cockpit of a mech – the movement of the robotʼs head, and as a result, your view direction, was controlled with the VR headset, while the body relied on the controller. Kardos thinks thatʼs one of the things that compromised the end result, as it was “absolutely dreadful” to run around and fight in arenas. As a fast-paced shooter with multiplayer modes, there were many things happening on screen at all times. It didnʼt take long to feel dizzy. According to Kardos, there was a market research report dictating that “there are not many people interested in these kinds of games.” Initial feedback was fairly rough, too, pinpointing the complicated control scheme and proneness to motion sickness as weak points. Sony closed Guerilla Cambridge in 2017, a year after Rigs’ release. Go Move Go MoveIn terms of the testing itself, most of the regular QA processes are compatible. Tasks such as bug reports or running test cases – checklists of specific elements to test in different environments – largely apply to VR. The problem is that even mundane actions such as switching to a second monitor to take notes or sending a Slack message turn cumbersome. Both Borland and Ben Hopkins, principal graphics engineer at Owlchemy, reinforce that the human experience of being inside a game is a significant factor of developing for VR. To try and mitigate constant usage of the headset, the team implemented a debug system, which essentially allows them to play the game with mouse and keyboard. “Itʼs good for a fast iteration, but there are so many things you canʼt actually understand whether they work or not unless youʼre actually in the headset,” he says. When asking the QA testers at Owlchemy if they ever use the debug editor, they reply with laughter. “The approach is very different,” says Jessica Fly, senior QA specialist. Previously, she worked at Cloud Imperium Games testing Star Citizen. All she needed was to boot up the game on a flat screen, and she was set. The routine now involves putting on the headset and ensuring the room is set up properly to avoid tripping over anything. And thatʼs before the real physicality of the job. Blood & Truth A clear example arises when doing full regression testing. During this stage, testers go through the game after an update or new version to ensure nothing is broken. In the case of Cosmonious High, one of the tasks involves testing all 18 recipes, each requiring a mixing-in-a-bowl type of movement. “Itʼs really murder on the arms,” Fly says. “Thereʼs this physical part of VR testing that is honestly one of the things that interest me.” “Before Owlchemy, I was at Blizzard working on Hearthstone, and I canʼt think of a stronger differential between testing a fully immersive VR video game and testing a digital card game,” says Paul Henderson, QA tester 1 at Owlchemy. “You go from a game that is the most basic inputs; you drag a card to the slot, you drag it to your opponent to how can you move? Thatʼs your inputs! Go move.” Through the Motions Through the MotionsMayank Lal joined Keywords Studios in Gurgaon, India, back in early 2017. He started as a QA games tester. Almost seven years later, heʼs a quality assurance test lead. During this time, Lal witnessed VR gaining more relevance in the studioʼs portfolio. This, thankfully, led to better conditions for the 40 dedicated VR testers out of around 200 employees. In the beginning, things were messy. It started with a partnership with Oculus back when Meta (formerly Facebook) wasnʼt involved. Testers quickly realized some issues needed to be addressed. “One of the most common things that they observed as testers in that particular time was feeling nausea in longer periods of use,” he says. “Headsets also heat up and that actually affects the skin on the face. Sometimes headsets are shared between testers, which increases the probability of getting infections.” One of the policies that came up was about breaks. For every 20 minutes using the headset, testers are encouraged, not mandated, to take a 10-minute “break” to file any bugs, report issues, and so on. Over time, they realized the platform just isnʼt for everyone. Employees can present a doctorʼs note to HR so theyʼre no longer deployed into VR projects. Nowadays, the studio doesnʼt immediately assign a tester to VR. After six months with other platforms, theyʼre given training and, eventually, see if theyʼre comfortable with virtual reality. Beat Saber “People actually leave here,” Lal says. “When it comes to health, people donʼt think, ‘I will not have another opportunity,ʼ because in this company youʼre already learning things and adding them to your CV. So if they feel weʼre exerting them, theyʼd not even supply a letter of resignation. Next day theyʼd call and not come back.” During the pandemic, the studio got each tester a separate foam attachment, which mitigated the infection problem. Now, testers are back in the office – only people at a managerial level can opt for a hybrid model – where they have a floor dedicated solely to VR projects. On average, they spend half of their eight-hour shifts in the headset. All these elements vary depending on the studioʼs internal rules or even across different branches. In the Québec, Montreal, branches of Keywords Studios, former functional QA tester Sammy Fox says you were expected to do “as much VR as you could tolerate,” as long as it didnʼt take time from real bug reporting or assigned tasks for the day. That being said, breaks were regulated, with two paid short breaks and one unpaid but longer lunch break. Another former tester, who requested anonymity, says even though everyone is legally an employee on file, almost all testers were on an “on-call basis.” Basically, theyʼd find out if theyʼre working the next day through email, receiving a new notice on a daily basis. While they were lucky enough to get fairly consistent work, pay was a letdown. Doing the conversion from Canadian to American dollars, the source says they peaked at around $10.50 USD an hour, back when the local minimum wage was around $9 USD an hour. They add that, for comparison, test leads were making more than $30,000 USD a year around the same time. During busy periods, it was common for a single tester to work on multiple projects per day. While the source didnʼt do much overtime, there were morning, afternoon, and, on occasion, so-called “graveyard” shifts that occurred overnight during the week and weekends. Employees would receive bonus pay for doing so. “The worst memory was once a stress test over the weekend, and someone projectile vomited all over a busy shared bathroom,” the source says. “It wasnʼt cleaned until Monday morning because maintenance wasnʼt available on the weekend. You could imagine the smell.” Resident Evil 4 VR Waving WavingIn a 2017 report from CNN, experts say that the cause behind eye strain, headaches, and nausea is due to the way VR affects the eye-brain connection. In real life, the eyes and brain simultaneously focus on a point in space. In virtual reality, the responses arenʼt coupled together, but separated. “The way they look and interact is changed because they may be projecting onto the eyes something that looks far away, but in reality, it’s only a few centimeters from the eye,” behavioral neuroscientist Walter Greenleaf said in the report. These conditions are aggravated when testing games still in development. While the teams try to ensure that builds are hitting a stable frame‑ rate before involving QA, often opting for excluding some assets entirely in the early stages of a project, it can be challenging. “I remember a racing game I tested where the camera went haywire and I almost fell to the ground,” says Fox. In the office, there was always a supply of ginger and anti-nausea pills to help with motion sickness and in-game “accidents” like this. “When that happened, I would remove the headset, go ask my team lead for a Gravol, and then do desk work until nausea subsides and you feel fine enough to resume doing physical VR testing,” he adds. Eric Kozlowsky, former senior environment artist at Armature Studio, recalls a similar tradition when working on Resident Evil 4 VR. “I get motion sickness pretty easily in real life, let alone in VR, so working with in-development software was challenging,” he says. “I did get nauseous, but that was mitigated by limited time spent in the headset and using homeopathic remedies. So if I ever got motion sick, ginger candy would help a bit.” Even though testers usually spend the most time using headsets, the job leads them to find creative workarounds in their routines. Archie Crampton, lead QA technician at Universally Speaking and gingernut biscuit advocator, finds changing his height in-game from two to 15 feet tall, as hilarious as it is, helpful. In addition, he says most people in the studio test Beat Saber seated. As theyʼve gotten quite good at it, theyʼre able to complete levels with less movement than youʼd expect. Hopkins says there have been many random helpful items in the Owlchemy office. This includes Bob, a mannequin head attached to the top of a camera tripod. Instead of pushing a real person in a chair, the team would put the headset on Bob and rotate it around to get different shots of a scene. “You have to be open-minded to things like that if youʼre gonna figure out solutions,” Borland adds. Alongside sheer creativity, almost everyone I spoke to shares excitement about VRʼs future. Sure, new technologies also bring new processes – Owlchemy, in particular, found not everybody waves equally while testing hand-tracking – but also keep QA fresh. Next time you play a VR game with comfortable controls and a lack of visual glitches, you can thank the testers. After all, theyʼre the ones who crouched, waved, mixed, and climbed a skyscraper dozens of times to ensure the best result. “Towards the end of Cosmonious High, developers would come to me as an expert on the game cause I spent so much time in there,” Fly says. “It makes me feel really good that they recognize that QA is extremely important and knowledgeable.” This article originally appeared in Issue 360 of Game Informer By Ambuj Nandanwar, Softnautics In this rapidly evolving technology, embedded systems have become the backbone of the modern world. From the subtle intelligence of smart home devices to the critical operations within healthcare and automotive industries, embedded systems are the quiet architects of their technological landscape. The seamless and error-free operation of these intricate systems is ensured by the meticulous application of Quality Assurance (QA). QA emerges as a paramount force in the development of embedded systems. In this article, they dissect the significance of QA in embedded systems, where precision and reliability are not just desired but mandatory. Join us as they navigate through various aspects of QA, exploring how QA shapes the robust functionality of embedded systems. Embedded systems are specialized computing systems that are designed to perform dedicated functions or tasks within a larger system. Unlike general-purpose computers, embedded systems are tightly integrated into the devices they operate, making them essential components in various industries. They are the brains behind smart home devices, medical equipment, automotive systems, industrial machinery, and more. These systems ensure seamless and efficient operation without drawing much attention to themselves. Significance of Quality Assurance in Embedded Systems In embedded systems, QA involves a systematic process of ensuring that the developed systems meet specified requirements and operate flawlessly in their intended environments. The importance of QA for embedded systems can be emphasized by the following factors:
Evolution of QA in Embedded Systems The technological landscape is dynamic, and embedded systems continue to evolve rapidly. Consequently, QA practices must also adapt to keep pace with these changes. Some key aspects of the evolution of QA in embedded systems include
Automated Testing in Embedded Systems As embedded systems fall in complexity, traditional testing methods fall short of providing the speed and accuracy required for efficient development. This is where test automation steps in. Automated testing in embedded systems streamlines the verification process, significantly reducing time-to-market and enhancing overall efficiency. Also, incorporating machine learning algorithms to enhance and modify testing procedures over time, machine learning testing is an important aspect of automated testing. This helps to identify possible problems before they become more serious and increases efficiency.
Testing approaches for Embedded systems Testing Approaches for Embedded Systems The foundation of quality control for embedded systems is device and embedded testing. This entails an in-depth assessment of embedded devices to make sure they meet safety and compliance requirements and operate as intended. Embedded systems demand various testing approaches to cover diverse functionalities and applications.
Leveraging machine learning in testing (ML testing) Machine Learning (ML) is becoming more and more popular as a means of optimizing and automating testing procedures for embedded systems. AIML algorithms are used in test automation. Test time and effort are greatly reduced with ML-driven test automation. It can create and run test cases, find trends in test data, and even forecast possible problems by using past data. ML algorithms are capable of identifying anomalies and departures from typical system behavior. This is particularly helpful in locating minor problems that conventional testing might ignore. As technology advances, so does the landscape of embedded systems. The future of Quality Assurance in embedded systems holds exciting prospects, with a continued emphasis on automation, machine learning, and agile testing methodologies. In conclusion, the role of QA in the development of embedded systems is indispensable. It not only guarantees the reliability and safety of these systems but also evolves alongside technological advancements to address new challenges and opportunities in the ever-changing landscape of embedded technology. England’s chief medical officer recently recommended certification of doctors to strengthen professional regulation. Specialist certification is a well established process in the United States that allows doctors to demonstrate achievements and competencies beyond the minimum acceptable standards required for licensing purposes. Certified status must be renewed every six to 10 years. Advertisement But does certification Excellerate medical standards?Kim Sutherland and Sheila Leatherman reviewed data on the effect of certification in the US on quality of care. A review of studies published between 1966 and 1999 found that over half showed positive and statistically significant associations between certification and superior outcomes. Since 1999, four well conducted studies have concluded that certification is associated with provision of higher quality care across a range of specialties. Recent studies have also found that a lack of certification is associated with an increased risk of disciplinary action. So, most of the available evidence seems to support rigorously conducted certification as a good method to Excellerate quality of care, say the authors. Renewable certification also provides a more transparent process for assessing skills, knowledge, and competence than the opaque principles of professionalism. Advertisement Adopting certification as a key regulatory instrument in the UK will have important implications, they add. In the US much of the cost is borne by doctors themselves who are likely to benefit from the process. However, there may be an argument for some of the costs to be offset by the NHS.As the NHS strives to Excellerate quality of care, it is important to consider the central part played by the professions, they write. Individual professional conduct, along with collective professional values, will always provide a patient with the best quality assurance. Certification, or validation within the UK context, provides a way to strengthen and bolster that vital protection and reassurance. Advertisement Because lectures are still the most common instructional modality, many online learning providers have completion rates as low as 5%. To create high-quality online education, learning platforms must incorporate the three elements listed below. Principles for Improving Online Learning Several online learning systems have evolved in the last 15 years. Salman Kahn posted a video to YouTube in 2006, one year after the site’s inception, to explain algebra to his cousin. The video went viral, and he created the well-known Khan Academy two years later. Udemy, the first massive open online course provider, was launched in 2010, and other online course providers like EdX, Coursera, and Udacity quickly followed. In 2015, Masterclass popularized online learning, and in recent years, cohort-based course providers like Godin’s AltMBA and the Maven platform have grown in popularity. Working as an EdTech entrepreneur for the past decade has given me a unique view of multiple online learning platforms and long-term trends that lead me to believe that online education is still in its infancy. While more people have acquired access to technology, connection, and educational content, the learning process has yet to be altered. Learning Science To begin, learning platforms will need to contain learning science, which has advanced significantly in recent years. According to research, brains do not function like recording devices. Learning consists of three steps: acquisition, encoding, and retrieval. Evidence-based learning strategies have been investigated by learning scientists such as Barbara Oakley, Roediger et al., and Lieberman. We learn best when they connect new material to past knowledge, comprehend its broader implications (elaboration), try retrieval at increasing intervals (spacing) and in diverse contexts (variety), use testing as a learning tool, and provide real-time feedback on students’ learning. Nonetheless, most online platforms have yet to incorporate these evidence-based learning methodologies. . Future online education platforms must contain technology that expands on these discoveries. They could include testing as a tool or built-in spaced repetition elements, which would Excellerate students’ learning outcomes and completion rates. Education’s Digitization Traditional teaching has mostly been digitized—for example, Masterclass, which features highly polished videos and well-known professors. Instructors, according to the Masterclass founder, create their classes. However, education experts believe that “masters” may not be the best teachers. They are most likely novices when it comes to Instructional Design and the science of learning. Masterclass and many other online education providers draw on the idea that online low-touch courses are for skill-building. However, because lectures are still the most common instructional style, many online learning providers have completion rates as low as 5%. Online education platforms will need to expand further, focusing on three areas that have previously been overlooked. Pedagogy In Teaching Second, online course providers will recognize the significance of pedagogy in teaching. While online platforms such as Udemy have made it possible for anybody to become a teacher, good instructional design is still in short supply. Future online platforms must consider the reality that being a “master” in a skill does not imply being an expert teacher. They must break with the underlying premise that everyone who’s mastered a talent can become a great teacher since it is faulty. Teachers receive years of training in both didactics and means. A camera and a platform do not transform skilled individuals into excellent teachers. Online platforms will need to include some type of quality assurance or instructional training, as well as be built with pedagogical ideas in mind. eLearning platforms will very certainly incorporate some sort of teacher training to provide teachers with the skills required for efficient course design and delivery. Analytics for Learning Finally, next-generation online learning providers will use learning analytics to assist students in achieving their goals. Although learning analytics is still in its early stages, data will be used to enhance decision-making in teaching and learning. By giving in-depth insights into the learning process, it has the potential to alter how they measure learning outcomes. Furthermore, learning analytics can be used to boost student motivation. Online education has great promise that has yet to be completely realized. To create high-quality online education, learning platforms must incorporate evidence-based learning technology, recognize the relevance of instructional design, and capitalize on the promise of learning analytics. By Dinesh Mohan The future of quality assurance (QA) and engineering (QE) is undergoing a significant transformation. The focus now has shifted from mere cost-effectiveness and product quality to prioritising customer experience (CX). It’s no longer just about whether the software works but about how well it meets the experiential demands of users, necessitating a thorough understanding of the business, its services/products, and the technologies employed. 
Recent findings from the Business Transformation Index 2023 reveal a concerning trend. 76% of firms are falling short on their business transformation initiatives with an alarming 66% missing the mark when it comes to criteria like—staying within budget, timely delivery, or solution reliability. This uptick from 50% in 2022, is a wake-up call highlighting the critical need for robust QA/QE practices, irrespective of the industry. Enter AI in QA—the game changer. How can AI offer a turnaround? No longer seen as just a final checkpoint, Quality Assurance and Engineering is now an integral part of the entire software development lifecycle. The narrative now is shift left or get left behind emphasising the importance of early and frequent testing. And AI is enabling this shift by automating various tasks. AI-powered tools analyse code, generate test cases, and execute tests automatically, freeing up testers to focus on more complex activities. The integration of AI into QA engineering is empowering testers to: • Automate repetitive tasks and focus on more strategic testing activities • Move beyond reactive testing towards predictive analysis—predicting and preventing defects from occurring in the first place • Optimise test data management and Excellerate test coverage • Generate insightful test reports and provide actionable recommendations • Collaborate effectively and share knowledge across teams Artificial Intelligence (AI) as a transformative force also helps overcome the challenges of:
However, building a competent QA/QE practice isn’t just about ticking boxes; it’s also about fostering talent. The big question is: In this era of digital transformation- Will AI-driven automation eclipse the need for human QA/QE professionals? We’re witnessing a blurring of boundaries between industries. Innovations in one sector are rapidly influencing others. This crossover, while improving customer experience, challenges quality engineers to adapt to diverse technological landscapes. The fear that AI might render human QA professionals obsolete is, in my opinion, unfounded. Instead, AI is redefining career paths in QA/QE. Testers equipped with AI proficiency are today invaluable assets. In fact, the changing dynamics have led many companies to rethink the traditional developer-tester ratio. Consider this, ten years ago, a typical ratio of QA testers to developers might have been 1:10 or 2:10. This means that for every 10 developers, there would be 1 or 2 QA testers. Fast forward to today, this ratio is starting to shift, and it is not uncommon to see ratios of 3:10 or 5:10. This means that there are now more QA testers for every developer. This ratio is likely to continue to increase in the future due to the increasing complexity of software, the growing importance of quality, and the rise of agile development methodologies. While automation is gaining momentum, the human element in software testing remains irreplaceable. AI can smartly automate processes, but can never wholly replicate the nuanced understanding and decision-making capabilities of a human tester. Manual testing is absolutely necessary in cases such as—exploratory testing, usability testing, security testing, edge-case testing, providing a second layer of validation, identifying new test cases, and providing feedback to developers. The future of QA/QE in the AI-Augmented world It’s true that the comparison between manual and automated testing might currently lean heavily towards automation, yet they can’t overlook the value of manual intervention. The undeniable fact is that AI can revolutionise Technology but the ingenuity, creativity, and intuitive power of People remain the most crucial and irreplaceable parts of the Process. Quick, efficient delivery of high-quality products/services is a hallmark of successful businesses. And a synergistic approach where AI and automation enhance human capabilities rather than replace them is central to this achievement. In a future where AI and human ingenuity coalesce to redefine software testing, how can they use technological advancements as a springboard for innovation and excellence? The author is head of delivery and operations, digital practice, Expleo Nine top executives from Keysight Technologies, provider of market-leading design, emulation and test solutions globally, has assembled its list of predictions that may impact the electronics industry in 2024. Dan Krantz, Chief Information Officer at KeysightImpact of AI in the cloud computing market AI workloads require GPU and memory intensive capacity. In the past, they thought of Cloud Computing as having 3 primary competitors: AWS, Azure, GCP. Generation 2 of the Oracle Cloud Infrastructure (OCI) with its significant price and performance advantage in GenAI training has created a 4-horse race in the cloud computing space now. Trends in the cloud computing industry in 2024 Majority of organizations are multi-cloud, seldom single cloud. Cloud vendors have started to realize this and are now building better multi-cloud interoperability capabilities. For example, the recent Azure/OCI agreement that Larry Ellison struck with Satya Nadella of Microsoft. This leads to organizations needing cloud-agnostic tools for observability, visibility, and quality assurance automation. Emerging sectors of the cloud industry becoming significant in 2024 As traditional cloud capabilities mature, I predict the emergence of Cloud High Performance Computing (HPC) in the next 12-18 months. Current HPC workloads typically utilize on-premises supercomputing infrastructure, but cloud providers will bring to the HPC market supercomputing capabilities wrapped in cloud-native characteristics of elasticity, programmable automation, and metered usage – this will democratize the most compute-intensive scientific and engineering workloads. Roger Nichols, 6G Program Manager at KeysightWhy some versions of AI are not the key to optimizing 6G networks AI has a large role to play in helping optimize 6G. However, it will not be the much-hyped generative AI that relies on large language models and vast data sets. Instead, it will be domain-specific data combined with the power of AI models and wireless domain expertise that will help solve specific industry problems. For example, AI algorithms will make improvements in the air interface, helping optimize the 6G system. Other use cases include advancing how to manage mobility during handovers, cell-site planning, and optimizing MIMO. But, before AI can add value to the development of 6G, it needs to be more reliable, explainable, and much less expensive. Skills silo throttles integration of AI in 6G Domain knowledge and AI expertise are vital to successfully integrating AI into 6G networks. Today, they have either wireless experts or AI specialists, but too few heads that share expertise in both domains. Until these skill sets are blended, it will be tough to find the right resources to deploy AI effectively in support of 6G goals. I believe this workforce capability gap will take more than a decade to resolve. 5G still a work in progress At the end of 2023 there are fewer than 50 commercial standalone 5G networks in the world. Over the next few years, the pace of transition from non-standalone to standalone networks will accelerate as these architectures support a fully programmable 5G network, which in turn enables operators to build services beyond enhanced mobile broadband. The expansion of standalone networks should pick up, as will the use of network slicing and resolving defects and performance challenges. In addition, the 5G ecosystem will grow in order to support capabilities in a broader range of industries beyond gaming and social media activities. This will lay the groundwork for 6G to be used across a wide set of use cases. Mobile gaming turns FR2 from dormant to dominant The wireless industry is exploring the acquisition of new spectrum between seven and 24 GHz. However, FR2 (millimeter-wave, 24-52GHz) is already available with many cases allocated, but it’s too expensive to support current use cases. FR2 will require new mobile gaming/VR applications to drive the economy of scale to overcome this hurdle. Interest from Gen Z and Gen Alpha in the new consumer applications played on VR/AR devices rather than traditional smartphones will drive a surge in the demand for higher bandwidth and capacity with a low-stakes use model. Current networks will be unable to support this and operators will turn to FR2 to support the demand at this scale. Once this milestone occurs, the downward pressure on costs will help applications outside the entertainment and advertising realm use FR2. Mobile subterahertz radio systems will not come to fruition anytime soon Mobile subterahertz (sub-THz) radio systems are at least a decade away. They are not feasible from a mobility standpoint due to immature mobile technology and the associated costs – not to mention power consumption and data-management. The industry’s struggles with FR2 are evidence that mobile sub-THz radio systems will not be viable in the near future. 6G is not going to overhaul the core network 6G will not result in a major overhaul of the core network. It will evolve, but a significant revamp, as happened with network functions in the transition from 4G to 5G, will not occur. The majority of the wireless industry now accepts that this would be a mistake. Spectrum Smorgasbord: A huge challenge for wireless industry Over the next five years the global wireless industry will have to support and manage 2G, 4G, 5G, and 6G networks. This carries significant technical and business challenges. With more than a fifth of the world’s population still relying on 2G, developing regions like Africa and most of Asia will not sunset many legacy networks before the end of the decade. However, India is bucking this trend and has deployed country-wide coverage with 5G standalone networks, making it the largest country in the best position to retire 2G. The only constraint that could slow this shift is the affordability of new devices. India: From wireless laggard to 6G leader India has become a nation of data guzzlers and is the global leader in per-device monthly data consumption. This is driving the demand for more bandwidth and capacity. After being late to adopt 4G, but accelerating 5G in the past 12 months, the country is emphasizing and championing national objectives for 6G and is looking to help shape and drive its rollout. India has launched multiple 6G research initiatives in 2023. They will be a leading voice in establishing 6G standards and ensuring it is fit for purpose for countries with large rural populations. Metaverse: More than an entertainment destination Despite most discussions of the metaverse focusing on gaming, it will evolve to support much broader use cases than those laid out by Meta. By the end of the decade, augmented and virtual reality will be part of their daily lives, and 6G will be pivotal in providing the bandwidth and connectivity to support these synthetic environments and facilitate seamless interactions between the virtual and physical worlds. Sarah LaSelva, Director of 6G Marketing at KeysightRegulation on the radar In 2024, regulation will be on the agenda as the industry looks to provide a framework so that the entire ecosystem, including companies, operators, and countries, can work in unison. Due to the complexity involved, particularly at the geopolitical level, this will take several years to resolve. AI is everyone’s BFF, including 6G The combination of complexity and massive amounts of data makes wireless networks ripe for AI optimization. The technology has started to be integrated and in 2024 this will accelerate. A key part of the process will be understanding where AI can help and, crucially, where it’s not the answer and may actually hinder the rollout of 6G. AI + 6G: A measured approach Unlike other sectors, the wireless industry will take a more measured approach to integrating AI. Operators will focus on thoroughly training the machine learning models on diverse data sets, quantifying the impact, and putting a new test methodology in place. As AI adoption matures, it will transform the wireless industry over the next decade, unleashing new capabilities such as improved beam management and smart spectrum sharing. The drive to net zero With sustainability concerns growing around wireless networks, AI will play a pivotal role in helping reduce the environmental impact of 6G. For example, the technology can determine how to optimize power consumption by turning on and off components based on real-time operating conditions. As 6G networks roll out and more devices and machines become wirelessly connected, it will create an opportunity to optimize operations and reduce carbon footprints. For example, 6G will help autonomous vehicles become more advanced, which will reduce traffic and some of the waste and inefficiencies associated with human-led driving. In farming, IoT devices connected to 6G will monitor soil conditions and help optimize water and fertilizer use. Once 6G becomes ubiquitous, it will usher in a new era of sustainability-driven operations. The industry will look to standardize sustainability measurements in 2024, including measuring the total carbon footprint of a wireless network. This will help avoid greenwashing claims and accelerate the drive to net zero. Spectrum sharing log jam on the horizon 6G will leverage many different bands and tools to meet the growing demands and expectations for cellular communications. The most challenging technical aspect is how to share spectrum. For example, in 6G the upper mid-band (7-24 GHz) is already used by civilians and governments for meteorology, radio astronomy, and maritime radio navigation. Once wireless access is added, it will necessitate learning how to be good frequency citizens. In 2024, there will be a lot of research in this area to find a pathway forward. Global spectrum harmonization on the radar for 6G The World Radio Conference in late 2023 will determine the available frequency bands that 6G will use and put in place a plan to make global spectrum harmonization a reality. This will enable operators to realize economy of scale for components and limit the number of bands to support. Gareth Smith, General Manager, Software Test Automation at KeysightSoftware Testing & AI Predictions AI & Testing: Always on becomes the baseline As AI becomes increasingly embedded in software, the systems will become more autonomous, which increases risk and complexity and makes testing a real challenge. As a result, a fixed set of tests (scripts) will no longer suffice when evaluating intelligent systems. Instead, AI will be needed to automatically and continuously test AI applications. The future of software testing is autonomous test design and execution. Why AI may drive quality down, not up As AI permeates every system and complexity and sophistication soar, there is a risk that quality will go down. This is a result of the sheer number of permutations, which makes testing everything impossible. This means decisions will need to be made around how, what, and when to test to ensure quality is maintained. AI: Regulation needs to be deep and wide There is universal acceptance of the need to regulate AI. However, what the regulation should encompass will be subject to much debate due to the breadth and complexity of the technology involved. It will take a seismic event with significant negative consequences before the necessary funding is available. Only then will clear standards and best practices come into effect. If regulation doesn’t happen in the near future, it increases the risk that it will no longer be possible to rein AI in. AI and Security: Constant vigilance the new norm As the risks associated with AI are recognized, enterprises will need to appoint an AI and security compliance officer to the C-Suite. Over time, this role will merge with the CSO. With live learning, it will be vital to have guardrails in place to keep AI on track. Constant checks and balances will be essential to validate that an intelligent system is behaving and hasn’t gone rogue. Live surveillance will become standard. However, as these systems develop, it will also be necessary to test that they haven’t learned how to look like they are behaving while undertaking nefarious activity. Reinforcement learning and similar techniques can inadvertently drive the AI to cover its tracks to reach its goal and will be a huge challenge to address before the end of the decade. These problems will create a slew of new opportunities for companies that can help clean up, control, and put guardrails in place for AI. Why AI needs a driver’s license and a regular inspection Currently, AI systems are tested by the companies building them. As the risks are increasingly understood, having an independent body to verify that an AI system is compliant is essential. Gaining an AI certification (i.e., AI driver’s license) will be the first step. However, just like your car, it will require a regular test to ensure it remains ethical, responsible, free of bias, and meets the necessary country and industry standards. In the longer term, this may result in an NFT label on each AI system to validate that it’s fit for purpose and meets all the required criteria. Goodbye citizen developer, hello business developer Citizen developers have long been touted as the answer to the IT talent shortage. However, the rapid growth of AI-powered solutions is fueling a new generation of business developers. These domain experts will increasingly be involved in the SDLC as they understand the goals and operations of the enterprise. This will supply rise to a new wave of no-code systems that enable business users to define goals and then have AI technology close the gap. The operational knowledge ensures that the software meets the specific needs of the organization and mitigates the risk. AI and the sustainability quandary There has been significant hype around how AI systems will transform their lives, but little attention has focused on the compute power required. In 2024, AI’s impact on sustainability will enter the spotlight, and organizations will start to monitor the carbon footprint of their entire technology infrastructure as they strive to meet net-zero targets. As a result, companies will need to decide where and how to judiciously use AI rather than thinking it can be deployed everywhere. And when it comes to testing software and applications, businesses will also have to pivot from testing everything to predicting the tests that matter most to reduce the environmental impact. Dan Thomasson, Head of Central Technology and Vice President of Keysight LabsAdvanced semiconductor innovations on the horizon Connecting the digital and physical worlds will require more powerful digital processing and interfaces able to overcome increasingly complex signal physics. An array of advancements in semiconductor technologies will be essential to achieve this and overcome the associated challenges. These issues include increasing data rates that need wider bandwidths, which dictate higher carrier frequencies, extending into the THz regime for wireless. The use of techniques such as extreme MIMO, adds more complexity and density, and networks with diverse topologies, such as the use of non-terrestrial (satellite) links, magnifies the challenge. Innovations to address this will include combining commercial semiconductors, such as GPUs and FPGAs, with custom MMICs and ASICs, and these new solutions will deliver significant improvements in size, weight, performance, and power consumption. Data converters enabling the capture and generation of signals at the widest bandwidths with unsurpassed signal fidelity will be needed. In addition, photonic solutions will be critical to extend the reach and capacity of data transmission technologies. Seamless software solutions for design and test Currently, workflows are a set of loosely connected tools. However, as the virtual and physical worlds merge, a unified design and test workflow where data is shared seamlessly via the cloud between simulation and measurement steps is required. The information will be constantly analyzed to inform the behavior of simulation and measurement, eliminating any gaps in the workflow from concept to final test. The insights from the simulation will be fed into AI-driven tools that will elevate the speed and productivity of the design and test workflow. Digital twins will be used to tightly couple design and test so that only one real build is needed. 6G embraces AI for network optimization 6G will turn to AI for network optimization, which will create testing challenges. It will be vital to develop technologies able to test AI algorithms to ensure training data is free of bias and the models are effective and devoid of anomalous behavior. Bridging the simulation gap with AI Moving forward, AI technologies will underpin simulation models, ushering in a new era of more accurate, capable, and informative models. In addition, the intelligence will provide enhanced insights into measurement data, reduce errors, and help optimize the design and test workflow. Niels Faché, Vice President & General Manager, Design and Simulation at KeysightPredicting performance remains imperative in electronic design In 2024, engineers will continue embracing shift left with their electronic product development cycles. As design moves from the physical into the virtual space, engineers are able to discover and fix problems in the most efficient manner, providing greater insights and performance improvements. The next few years will see a continuing emphasis on connecting design and test workflows to handle rising complexity and more demanding time-to-market requirements for electronic products in wireless, wired, aerospace & defense, and other industries. Emerging Electronic Design Innovations 3DIC and Heterogeneous Chiplets: New standards come into view New standards such as UCIe are emerging for the creation of chiplets and the disaggregation of system-on-chip designs into smaller pieces of intellectual property that can be assembled into 2.5D and 3D integrated circuits using advanced packaging. For designers to accurately simulate die-to-die physical layer interconnect, it will require high-speed, high-frequency channel simulation to UCIe and other standards. EDA Turns to AI: From complexity to clarity The application of AI and ML techniques in EDA is still in the early adopter phase, with design engineers exploring use cases to simplify complex problems. The intelligence is particularly valuable in model development and validation for simulation, where it assists in processing large volumes of data. In 2024, organizations will increasingly adopt both technologies for device modeling of silicon and III-V semiconductor process technologies, as well as system modeling for forthcoming standards such as 6G, where research is well underway. Software automation empowers engineers As Moore’s Law reaches its limits, improving design processes through workflow automation will provide a pathway to increasing the productivity of design engineers. In 2024, software automation techniques, such as Python APIs, will take a more significant role in integrating “best-in-class” tools into open, interoperable design and test ecosystems. Navigating the Digital Shift: Design management essentials With the creation of digital enterprise workflows, many organizations are investing in design management across tool sets, data, and IP. Moving forward, design data and IP management software will play a critical role in the success of complex SoC and heterogeneous chiplet designs supporting large, geographically distributed teams. The creation of digital threads between requirements definition and compliance as well as establishing tighter links with enterprise systems such as PLM will play a role in the digital transformation of product development cycles. Next-Gen Quantum Design: Optimizing system performance Quantum computing is advancing at a rapid pace and is transitioning from predominantly free research tools to commercial products and workflows in quantum design. Next-generation quantum design will require more integrated simulation workflows that provide developers with fast and accurate capabilities to optimize system performance. Silicon photonics fuels data center transformation Data centers are evolving to provide higher compute performance to support the exponential growth in AI and ML workloads, as well as the need for more efficient power utilization and thermal management. Silicon photonics will play a critical role in accelerating the transformation of data centers to meet the appetite for compute performance. As design engineers develop high-speed data center chips that incorporate silicon photonics interconnect, they will need process design kits (PDKs) and accurate simulation models that support the advanced development work. Dr. Philip Krantz, Quantum Customer Success Manager at KeysightFrom Theory to Reality: The quantum potential Quantum technology allows us to harness the fundamental laws of quantum mechanics to solve problems that are extremely challenging or impossible today. With quantum technology, complex simulations and computations, secure communication, and more powerful imaging and sensor techniques will be possible. Navigating the Quantum Landscape: Bridging the talent gap Quantum technologies are expanding beyond the academic realm and into startups, high-tech companies, and the military. This will supply rise to more quantum hubs, incubators, and local and national ecosystems all trying to build a workforce able to seize the quantum opportunity. Solving the talent gap is critical to realizing the potential of quantum in the coming years and decades. From Labs to Lecture Halls: The quantum leap in education The shortage of quantum talent will create an opportunity for higher education to offer new programs to help train the future quantum workforce. By 2030, quantum courses will be commonplace. These programs will involve industry partners so students can access the latest quantum control and readout technologies and obtain the right technical skills. In addition, business schools will offer quantum courses to prepare the next generation of entrepreneurs to enter the quantum ecosystem. Democratizing Quantum: The emergence of quantum-as-a-service (QaaS) Due to the significant cost and resource burden in developing quantum labs, this will supply rise to more quantum-as-a-service (QaaS) providers. Remote cloud access to quantum processors, test beds for device characterization, and foundries that offer fabrication services are examples of services that are available which in turn will help attract startups into the quantum ecosystem. QaaS providers, over time, will help standardize device operation, characterization, and fabrication, which will enable benchmarking of quantum processors and qubit-adjacent enabling technologies. Inclusive Innovation: Quantum community champions gender equality Quantum has the potential to become the first technology sector to achieve gender equality. This will result from an ongoing concerted effort to attract women and ensure a diverse workforce is the norm rather than the exception. Knowledge gaps will throttle the progress of quantum Quantum research and development will continue to attract investment from governments, academia, and industry. However, knowledge gaps and the availability of state-of-the-art technology will limit the pace of progress. For example, if the capability to produce high-quality quantum processor units (QPUs) is missing due to the lack of an advanced and dedicated cleanroom facility, this will slow progress. Marie Hattar, Senior Vice President & Chief Marketing Officer at KeysightAI and Marketing: What the future holds Marketing organizations will increasingly adopt AI to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing campaigns. Customer Engagement: AI in the driving seat By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams must train large language models (LLMs) to fully automate customer content and differentiate their brand. By 2026, this will be commonplace, enabling teams to shift focus to campaign management and optimization. Copyright comes into focus Generative design tools are increasingly being adopted, but one thorny issue is copyright. Many of these AI solutions scrape visual content without being subject to any consequences. In 2024, there will be a lot of energy and effort focused on finding a solution to the copyright problem with AI image creation to clarify ownership. This will allow marketing teams to embrace AI design tools without fear of encountering legal issues, saving precious time and money. AI and Talent: The augmentation era As AI becomes more pervasive, this will inevitably change the fabric of marketing teams. Lower-level admin-centric roles will disappear, and many analytical positions will become redundant. However, it’s not all doom and gloom; the demand for data scientists will explode, making it one of the most sought-after skill sets for the rest of this decade and immune to economic pressures. Humans will continue to drive marketing, but the role of machines will increase each year. This era of AI (with guardrails) augmenting humans will continue for at least another decade in marketing. AI key to scaling personalization efficiently AI will be pivotal as marketing struggles to scale personalization efforts. The intelligence will enable marketing to generate more customer experiences from improved segmentation. In addition, the technology will optimize advertising targeting and marketing strategies to achieve higher engagement and conversion levels. AI Trends AI & Retail – The retail industry has been quick to integrate AI to deliver efficiencies and increase sales. One innovation on the horizon is combining neural networks with a shopper and a product to create a new retail experience. For example, starting in 2024, you can expect an AI assistant to showcase an item of clothing on a model with similar dimensions to you so you can see exactly how it will look in various poses. These immersive, highly personalized experiences are the future of retail. AI & Digital Twins: Changing the face of healthcare – Digital twins are increasingly ubiquitous, and now, with AI-infused, they are creating a new reality in healthcare. The technology will significantly reduce the pressure on the system and provide individuals with more options, helping Excellerate the quality of life. AI-powered digital twins will usher in a new era of caring for an aging population, allowing people to live independently for longer. AI will play a pivotal role in the early diagnosis of potential health issues. For example, full-body MRIs will tap into AI’s ability to identify, predict, and analyze data patterns to help diagnose disease long before it’s visible to the human eye. In addition, AI will take on a more prominent role in assisting medical staff to understand and interpret findings and provide treatment and care recommendations. Jeff Harris, Vice President, Corporate and Portfolio Marketing at KeysightThe Next Frontier in EVs: Prioritizing and predicting battery health As EVs continue to evolve, range anxiety will start to dissipate as 300 miles becomes standard. However, attention will shift to the health of batteries. With cell phones already illustrating how batteries can deteriorate over time, no driver wants to experience a car that quickly loses power, potentially leaving them stranded or, at the very least, requiring multiple charges per day. Battery health will become a factor influencing EV buying decisions, presenting an opportunity for auto manufacturers to visualize a car’s health status to reassure and inform drivers. The information will be more granular and incorporate gamification interfaces so drivers can see how their actions influence keeping the battery management system at peak performance. Additionally, by integrating AI algorithms into the system, it will predict the health and performance of batteries under various conditions, quelling any concerns. The programme was attended by 31 participants from nine different healthcare organisations and hospitals. The programme is a joint initiative with HHQI to build capacity and capability in healthcare quality improvement and patient safety across the healthcare sector in Qatar. It aims to ensure the delivery of high-quality healthcare for everyone living in Qatar. On that note, the ministry expanded its regulatory role to also include building capacity and capability of the healthcare workforce in the field of quality and patient safety to help sustain resilience in the Qatar healthcare system. The programme focused on fundamentals of Quality Improvement and Patient Safety (FQIC), which represents a significant step in enhancing healthcare standards across the nation, aims to support the continuity of service delivery as patients transition between Public and Private healthcare providers. The programme is structured with interactive practical sessions, opportunities for reflection and hands-on exercises. Prior to launching this programme, the MoPH performed a Needs Assessment survey to identify educational and training needs in quality and safety for healthcare staff. The assessment included 1,247 participants from various types of healthcare facilities. The respondents were clinical staff as well as other allied and administrative staff who have patient interactions. The results of this assessment survey assisted in structuring the training course in a way that ensures its success and achieves the desired goal. Huda al-Khtheeri, director of Strategic Planning, Performance & Innovation department at the Ministry of Public Health, stated in this regard that "This programme was designed in order to drive service and clinical excellence and effectiveness with assurance of high quality and patient safety across the whole health system in Qatar. They aim to attain their national health goals by developing a healthcare workforce that is sustainable and capable to meet the population's health needs. Promoting collaboration between the public and private sectors is important for future health system resilience". Nasser al-Naimi, chief of Patient Experience and HHQI director, Hamad Medical Corporation, commented on the programme's launch: "This training programme aligns seamlessly with HHQI's strategic direction, reinforcing its status as the national institute for training and building capacity in Quality Improvement and Patient Safety. It marks a crucial step in fulfilling national demands for higher healthcare standards and contributes significantly to the regional prominence in healthcare quality and value". Nasser emphasised that the initiative goes beyond a mere training programme, and represents a pivotal movement towards fulfilling national and regional aspirations for enhanced healthcare quality and patient safety. By empowering healthcare professionals with essential skills, the programme aims not only to Excellerate current systems but also to set new benchmarks in healthcare excellence. It is expected to have a far-reaching impact, elevating the quality of healthcare services and ensuring safer patient experiences across Qatar. | ||||||||
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