# QA Published articles for QA. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CoreOS 45 Test Week DevFeed: [CoreOS 45 Test Week]() Original publisher: [Read original article]() Author: Lukáš Růžička Published: 2026-09-16T08:00:00Z Content type: news Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Testing](), [Wiki]() Tags: [2026](), [bugs](), [community](), [contribution](), [coreos](), [download](), [event](), [fedora-linux-45](), [fedora-project-community](), [qa](), [release](), [test-week](), [testing]() ### AI overview Fedora CoreOS and QA teams are organizing a Test Week beginning September 21, 2026, to help the community test Fedora CoreOS based on Fedora 45 content before it reaches the testing and stable streams. ### Source excerpt The Fedora CoreOS and QA teams are gearing up for Fedora 45, and we need your help! We are organizing a Fedora CoreOS Test Week starting on September 21, 2026. This event is a great opportunity for the community to test Fedora CoreOS (FCOS) based on Fedora 45 content before it officially reaches the testing [...] ## The Thumb That Ran Ahead of the Finger: Anatomy of a Compose Slider Bug DevFeed: [The Thumb That Ran Ahead of the Finger: Anatomy of a Compose Slider Bug]() Original publisher: [Read original article]() Author: Vitaliy Gribko Published: 2026-09-14T04:19:34Z Content type: article Language: en Sources: [ProAndroidDev - Medium]() Topics: [bug](), [Compose](), [Android](), [Design system](), [Code]() Tags: [android](), [bug](), [code](), [compose](), [debugging](), [design-system](), [jetpack-compose](), [kotlin](), [qa](), [race-condition](), [ui]() ### AI overview This article examines a deterministic bug in a custom Compose slider where the thumb moves ahead of the user's finger during a drag. It traces the issue to a design-system slider forked from the AOSP Material implementation and discusses debugging and code simplification. ### Source excerpt A bug report sat in the backlog for seven months. The fix took a day, deleted a third of the slider's logic, and was almost shipped with a second bug inside -- until a machine read my diff better than I did. This is the whole story: the ported behavior that never made sense, the race nobody reported, and why the best fixes remove code. The report The bug report was seven months old when it reached me. A QA engineer had filed it in December, with a screen recording and three steps: Open the people search filters. Grab the right-hand thumb of the age range slider. Drag it slowly to the left. The recording showed the problem in two seconds. The thumb does not follow the finger. It sprints ahead -- you are still crossing the gap between two ticks, and the thumb is already standing on the next one, waiting for you, like a dog that runs to the park while you are still tying your shoes. Expected: the thumb moves with the finger. Actual: it escapes. On a budget phone, on two Android versions, every time. Not a race condition, not a flake -- a perfectly deterministic lie, filmed in December, assigned to a fix-day in July. Seven months is not negligence. It is the natural half-life of a visual glitch filed as minor against a screen nobody dies on. The age filter works. You tap a tick, the value applies. Only the drag -- the one gesture the slider exists for -- feels wrong in a way that is hard to screenshot and easy to deprioritize. When the ticket finally landed on my desk, a colleague glanced at it and asked whether it had already been fixed once. It had not. But I understood his confusion later, when I opened the slider's source and found a comment that explained everything -- including why this bug felt so old. The comment that confessed The slider is not the framework's slider. Our design system ships its own Compose slider, forked from the AOSP Material one, because design needed three knobs the framework does not expose: a custom thumb radius, a custom track height, a differ ## AI x QA Leadership Summit 2026: Building the AI-Native QA Engine DevFeed: [AI x QA Leadership Summit 2026: Building the AI-Native QA Engine]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-09-11T06:34:59Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [AI Strategy](), [Artificial Intelligence]() Tags: [2026](), [ai](), [events](), [free](), [governance](), [leadership](), [practitioner](), [qa](), [quality-engineering](), [september-2026](), [summit](), [virtual-event](), [workflow](), [workshops]() ### AI overview BrowserStack's 11th QA Leadership Summit is a free virtual event on 23 September 2026. It offers separate tracks on AI strategy and governance for QA leaders and applied AI testing skills for practitioners, with the theme "Building the AI-Native QA Engine." ### Source excerpt BrowserStack's 11th QA Leadership Summit runs 23 September 2026, free and virtual. Two tracks: AI strategy and governance for QA leaders, applied AI testing for practitioners. Speakers from AWS, Amazon, Atlassian, KPMG and S&P Global. ## Cypress Ambassador Spotlight: Maksym Donets DevFeed: [Cypress Ambassador Spotlight: Maksym Donets]() Original publisher: [Read original article]() Published: 2026-09-10T16:12:22Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Testing](), [Cloud](), [Automation]() Tags: [ambassador](), [automation](), [bugs](), [cloud](), [community](), [cypress](), [debugging](), [learning](), [qa](), [testing]() ### AI overview An ambassador spotlight featuring Maksym Donets, an Automation QA Engineer who discusses learning, sharing, helping developers debug failed pipelines with Cypress Cloud, and using cy.session() with cy.request() to simplify session handling and speed up tests. He also shares advice for getting started with Cypress and mentions catching bugs with end-to-end testing. ### Source excerpt Welcome back to another edition of our Ambassador Spotlight series, where we shine a light on the Cypress community members who make testing better for everyone through the blogs they write, the talks they give, and the day-to-day help they offer to fellow developers. Curious about the program? You can learn more about it here. This time, we're catching up with Maksym Donets, an Automation QA Engineer with a straightforward philosophy on testing and an even more straightforward philosophy on li ## Built for agents: Omarchy's pipeline moves to DigitalOcean DevFeed: [Built for agents: Omarchy's pipeline moves to DigitalOcean]() Original publisher: [Read original article]() Author: Paddy Srinivasan Published: 2026-09-09T15:30:15Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Linux](), [Open Source](), [Maintainers](), [Pull Request](), [Testing]() Tags: [agentic](), [agents](), [compute](), [digitalocean](), [infrastructure](), [linux](), [maintainers](), [news](), [open](), [open-source](), [qa](), [review](), [upgrade]() ### AI overview DigitalOcean is becoming the agentic compute provider for Omarchy, a keyboard-first Linux desktop built on Arch and Hyprland. Omarchy's production pipeline, packaging builds, pull request review agents, and QA testing now run on DigitalOcean infrastructure. ### Source excerpt Omarchy is a keyboard-first Linux desktop built on Arch and Hyprland by David Heinemeier Hansson (DHH), and its production pipeline, packaging builds, PR review agents, and QA testing, now run on DigitalOcean. Agent workloads don't behave like a typical web server. They're bursty and parallel: an agent spins up, does one job, and shuts down. Omarchy's pipeline is a real example of that pattern, and DigitalOcean infrastructure complements its needs. A single doctl command can provision a Droplet, run the job, call separately configured inference services, and destroy the Droplet when the work is done. Alongside the infrastructure move, DigitalOcean is joining the Omacom Foundation, the nonprofit that funds Omarchy's infrastructure and the open source projects it depends on, as a Founding Corporate Patron, and will serve as Omarchy's agentic compute provider. "I'm thrilled to have DigitalOcean become a Founding Corporate Patron of the Omacom Foundation, and our new agentic compute provider as well. We have such grand ambitions for Omarchy, and it's time we upgrade our technical infrastructure to match. Whether it's build servers for packaging, agent runners for PR reviews, or Droplets for QA testing, DigitalOcean simply has everything we need. They also have the right mindset for the future and our new age of agents. Couldn't imagine a better fit!" -- David Heinemeier Hansson, creator of Omarchy What's Next Coming soon, DigitalOcean and Omarchy plan to release a 1-click Omarchy Droplet available through the DigitalOcean Marketplace, enabling developers to quickly deploy the same environment used by the maintainers. ## Honoring #IconsOfQuality: Richard Bradshaw DevFeed: [Honoring #IconsOfQuality: Richard Bradshaw]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-09T11:44:44Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Software Engineering](), [Artificial Intelligence](), [Automation]() Tags: [ai](), [ai-agents](), [automation](), [engineering](), [evals](), [icons-of-quality](), [qa](), [quality-engineering](), [software-engineering](), [software-testing](), [telemetry](), [testing]() ### AI overview BrowserStack profiles Richard Bradshaw, a software testing and quality engineering leader, and discusses his views on AI agents, human-centric automation, and evaluating probabilistic AI systems. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Framework Laptop 12 Arrives with Fedora Pre-installed DevFeed: [Framework Laptop 12 Arrives with Fedora Pre-installed]() Original publisher: [Read original article]() Author: Shaun McCance Published: 2026-09-07T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Linux](), [Hardware](), [Wayland](), [Wi-Fi 7](), [intel]() Tags: [desktop](), [driver](), [fedora-project-community](), [framework](), [kde-plasma-pre-installed](), [kernel](), [laptop-12](), [linux](), [modular-hardware](), [qa](), [testing](), [thunderbolt](), [touchscreen](), [user-interface](), [wi-fi-7]() ### AI overview The article describes Framework Laptop 12, a 12.2-inch convertible laptop that can ship with Fedora KDE pre-installed. It explains how Fedora KDE Plasma, Wayland support, touch and pen input, and hardware features such as Intel Core Series 3, Thunderbolt 4, Wi-Fi 7, and fingerprint-reader support were validated through collaboration between Framework, Fedora QA, SIG contributors, and KDE contributors. ### Source excerpt Recently, Framework announced their new Laptop 12, which optionally comes with the KDE Edition of Fedora Linux pre-installed. Framework's modular hardware has long captured the hearts of tinkerers, and many Framework users were already running Fedora Linux. Giving community members the option to unbox a hardware-validated, pre-installed Fedora KDE system is the culmination of months [...] ## Cypress Ambassador Spotlight: Marcelo Costa DevFeed: [Cypress Ambassador Spotlight: Marcelo Costa]() Original publisher: [Read original article]() Published: 2026-09-03T16:46:28Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Testing](), [Automation]() Tags: [ambassador](), [automation](), [community](), [cypress](), [qa](), [tech-lead](), [testing]() ### AI overview This Cypress Ambassador Spotlight introduces Marcelo Costa, a QA Automation Engineer and mentor who shares testing knowledge through community support, articles, and talks. He discusses helping Cypress users, favorite features such as cy.intercept and cy.session, testing practices, and a professional milestone involving a new tech stack. ### Source excerpt Welcome back to our Ambassador Spotlight series! Here, we celebrate the incredible individuals within the Cypress community who go above and beyond, sharing their expertise through insightful blogs, engaging talks, and everyday support. Discover more about our Ambassador program here. This week, we introduce Marcelo Costa, a dedicated QA Automation Engineer and mentor known for tackling real-world testing challenges and sharing those solutions with the world. Join us as we explore what inspired ## Finding three-way API drift with the AI Engineer DevFeed: [Finding three-way API drift with the AI Engineer]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-09-01T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [API](), [Postman](), [OpenAPI Specification](), [Claude](), [Tooling](), [client](), [servers]() Tags: [ai-engineer](), [api](), [api-drift](), [apis](), [ci](), [fintech](), [general](), [openapi](), [postman-ai-engineer](), [production](), [qa]() ### AI overview The article explains how to detect three-way API drift by comparing an OpenAPI specification, the requests in Postman Collections, and the responses returned by a running server. It argues that pairwise checks can miss cases where all three sources disagree, and describes using Postman AI Engineer and the Context Graph to identify these inconsistencies. ### Source excerpt Find three-way API drift with Postman AI Engineer and the Context Graph. Compares your OpenAPI spec, collection requests, and live server responses. The post Finding three-way API drift with the AI Engineer appeared first on Postman Blog. ## Cypress Ambassador Spotlight: Sehani Chathurangi DevFeed: [Cypress Ambassador Spotlight: Sehani Chathurangi]() Original publisher: [Read original article]() Published: 2026-08-28T16:06:18Z Content type: article Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [Testing](), [Test automation](), [selectors]() Tags: [ambassador](), [automation](), [best-practices](), [community](), [cypress](), [knowledge-sharing](), [qa](), [selectors](), [testing]() ### AI overview An Ambassador Spotlight article introduces Sehani Chathurangi, a Senior Software Quality Assurance Engineer, CypressConf speaker, and blogger. She discusses her Cypress advocacy, community knowledge sharing, custom commands, and the use of data-* attributes to create stable selectors in automated tests. ### Source excerpt Welcome back to the Ambassador Spotlight series! Each edition, we shine a light on one of the testers and builders shaping the Cypress community and the people in it sharing their knowledge through blogs, talks, and everyday community support. Curious about the program itself? Learn more here. This week, meet Sehani Chathurangi, a Senior Software Quality Assurance Engineer, CypressConf speaker, and prolific blogger who shares real-world testing solutions with the community. Find out what drew h ## Using snapshot tests to prevent accessibility regressions in a Jetpack Compose Android app DevFeed: [Using snapshot tests to prevent accessibility regressions in a Jetpack Compose Android app]() Original publisher: [Read original article]() Author: ithersta (Яндекс) Published: 2026-08-27T07:05:12Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Compose](), [Android](), [яндекс]() Tags: [android](), [compose](), [jetpack](), [jetpack-compose](), [qa](), [tag-4004cf5948d3](), [tag-e69b694e3ce2](), [tag-f538878e20ff]() ### AI overview The article explains how the Yandex Mail Android team uses snapshot tests with Jetpack Compose to detect accessibility regressions. It describes Compose semantics, TalkBack, and why manual accessibility checks are insufficient as screens and shared components grow. ### Source excerpt Почта -- массовый продукт, которым пользуются самые разные люди, и поэтому он должен быть максимально доступен. Нам важно, чтобы приложение Яндекс Почты было удобно каждому, в том числе людям с особенностями зрения. На телефонах с Android за доступность для людей с особенностями зрения отвечает TalkBack -- функция озвучивания экрана, которая зачитывает текст, роль и состояние всех размеченных элементов на экране и позволяет с ними взаимодействовать. Если разметка сломается, приложением будет пользоваться сильно сложнее или вообще невозможно. Читать далее ## Test Days for Fedora 45: Help Us Test the Big Changes DevFeed: [Test Days for Fedora 45: Help Us Test the Big Changes]() Original publisher: [Read original article]() Author: Petr Sklenar Published: 2026-08-14T17:22:24Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [boot](), [ci](), [virtualization](), [Internationalization (i18n)](), [Confidential Computing](), [dnf](), [TLS (Transport Layer Security)](), [Matrix]() Tags: [article](), [community](), [developers](), [dnf](), [events](), [fedora-project-community](), [graphics](), [kernel](), [new-in-fedora](), [python](), [qa](), [test-day](), [testing](), [tls](), [virtualization]() ### AI overview This article invites the Fedora community to test Fedora 45 system-level changes on real hardware and virtual machines. It highlights testing for GNOME 51, RPM 6.1, OpenSSL 4.0, rebuilt boot and live media, kmscon, Python 3.15, GRUB EFI for Confidential Computing, and internationalization changes. Community testing is intended to find regressions that automated tests may miss and to support reproducible bug reports. ### Source excerpt Fedora 45 has several system-level changes that need testing on real hardware. The first test day up is GNOME 51, starting on 17 August, with more events planned for RPM 6.1, installation media and others. You can participate with a VM for most tests; some hardware-specific testing is more useful on a real machine. Details [...] ## How Zapier transformed core marketing processes with ChatGPT Work DevFeed: [How Zapier transformed core marketing processes with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Processes](), [dashboards](), [Optimization]() Tags: [building](), [chatgpt](), [executive](), [optimization](), [processes](), [qa](), [qc](), [quality-assurance](), [quality-control](), [sales](), [work]() ### AI overview Zapier's enterprise marketing team uses ChatGPT Work to automate lead-funnel QA/QC, identify and fix lead problems, build campaign assets, and report recurring pipeline issues. The system reduces manual effort, accelerates campaign execution, and helps generate pipeline value for sales. ### Source excerpt The enterprise marketing team at Zapier uses ChatGPT Work to reduce the number of drop-offs in its lead funnel, build campaign assets, and automate reporting. ## Dogfooding vs. QA vs. User Research DevFeed: [Dogfooding vs. QA vs. User Research]() Original publisher: [Read original article]() Author: Therese Fessenden Published: 2026-08-07T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [dogfooding](), [FIRST]() Tags: [article](), [bugs](), [development](), [dogfooding](), [qa](), [qc](), [quality-assurance](), [quality-control](), [user-research](), [user-testing]() ### AI overview Dogfooding--using a company's own products internally--can reveal bugs, broken flows, crashes, and missing features while building empathy and shared ownership. The article explains that it complements but cannot replace user research, because insiders understand the product too well to represent real users. It also distinguishes genuine dogfooding from performative displays of loyalty. ### Source excerpt Dogfooding, or using your own products internally, helps catch bugs, but it can't replace user research: your team knows too much to represent real users. ## Meet Test Companion: AI That Helps QA Teams Keep Pace with Modern Development DevFeed: [Meet Test Companion: AI That Helps QA Teams Keep Pace with Modern Development]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-07-29T13:05:29Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Artificial Intelligence](), [Test automation](), [MCP Server](), [ide](), [Mobile](), [Accessibility](), [API](), [selectors]() Tags: [accessibility](), [ai](), [ai-agents](), [api](), [ide](), [logs](), [mcp-server](), [mobile](), [qa](), [selectors](), [test-companion](), [testing](), [workflows]() ### AI overview The article introduces BrowserStack Test Companion, an agentic AI test-automation tool embedded in the IDE. It is designed to help SDETs and QA teams author, execute, debug, and maintain web and mobile tests faster while preserving their existing frameworks, tools, standards, and governance. Described capabilities include generating functional, visual, accessibility, and API tests; debugging failures; analyzing logs; suggesting fixes; repairing brittle selectors and assertions; and running exploratory testing. ### Source excerpt Test Companion builds on BrowserStack's growing AI portfolio, bringing intelligent testing directly into the IDE. Combined with our BrowserStack MCP Server and specialised AI agents, it helps QA teams move faster without changing the way they already work. ## From Pass/Fail to Confidence Levels: The Evolution of QA in the AI Era DevFeed: [From Pass/Fail to Confidence Levels: The Evolution of QA in the AI Era]() Original publisher: [Read original article]() Author: Gayatri Panganti Published: 2026-07-22T06:16:20Z Content type: article Language: en Sources: [Helpshift]() Topics: [Testing](), [Software Testing](), [AI Agent](), [Large Language Model]() Tags: [agents](), [ai](), [ai-agents](), [ai-ready-qa-mindset](), [beyond-traditional-qa](), [bug](), [configuration](), [consistency](), [qa](), [qa-beyond-pass-or-fail](), [software-testing](), [testing](), [trust]() ### AI overview This article explains how quality assurance for AI features differs from traditional deterministic software testing. It argues that testing should assess behavior, consistency, safety, policy adherence, and trust across varied prompts and contexts rather than relying only on binary pass/fail results. ### Source excerpt For decades, software testing followed a familiar pattern. A feature either worked or it didn't. A button click resulted in an expected outcome. An API returned a predefined response. A workflow either passed or failed. Large Language Models (LLMs) have changed that equation. 🤖 Modern AI agents don't simply execute the instructions- they interpret, reason, generate and adapt. The same prompt may produce different responses across executions while still being technically correct. As a result, Quality Assurance is evolving from validating deterministic outputs to evaluating confidence in AI features and behaviour. That question is no longer : >> Did the feature work? It has become: >> Can we trust how the AI behaves across a wide range of real world situations? Traditional QA Vs AI QA Traditional applications are deterministic. If the same input is provided ten times, the output should be identical ten times. -- -- -- Input -> Business Logic -> Expected Output -- -- -- AI systems behave differently. Same Prompt -> Correct Answer -> Different (but still correct) Answer -> Hallucination -> Policy Violation -> Escalation -> Rejection/Resolution Every one of these outcomes may be observed from the same feature depending upon the context, prompt wording, conversation history, model behaviour or configuration. Accordingly AI bug can be classified as, Prompt Issue / Model Behaviour / Product Bug / Configuration Issue This does not mean the AI feature is broken, it means the testing strategy must evolve. -- -- -- Pass/Fail Is No Longer Enough Traditional QA measures correctness. AI QA measures behaviour. Instead of asking whether a test passed, QA teams increasingly evaluate questions such as, Was the response helpful? Was it aligned with the expected response? Did it follow the configured instructions? Was sensitive information protected? Was the response safe and comply with organisation policies? Did it performed the configured actions correctly? Was the escalation triggered only w ## Reducing LLM token costs in a code-coverage analysis bot DevFeed: [Reducing LLM token costs in a code-coverage analysis bot]() Original publisher: [Read original article]() Author: iBljad (hh.ru) Published: 2026-07-16T07:25:30Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [Large Language Model](), [ci](), [Java]() Tags: [ci](), [java](), [kotlin](), [llm](), [python](), [qa](), [qa-automation](), [qa-testing](), [sonarqube](), [tag-ad8c0a243366](), [tag-bc4e97082857]() ### AI overview The article describes how hh optimized prompts for an LLM-powered bot that analyzes test coverage. The approach reduces token use by skipping unchanged, trivial, and very short methods, prioritizing more important code, and applying additional cost-saving practices. ### Source excerpt При анализе кода с помощью LLM всегда есть соблазн закинуть в модель всё сразу -- и пусть сама разбирается. Но на больших объёмах кода стоимость и время такого "пусть разбирается" быстро приводят нас в чувства и заставляют искать пути более экономной траты токенов. Привет, меня зовут Андрей, я занимаюсь автоматизацией тестирования в hh. В этой статье расскажу, как мы оптимизировали запросы нашего бота, который анализирует тестовое покрытие с помощью LLM, чтобы получить максимум пользы при минимальных затратах. Антоним токенмаксинга - токенпожарскинг токенминнинг? Читать далее ## Use AI for investigation, review, and QA reports while keeping architecture and ownership with humans DevFeed: [Use AI for investigation, review, and QA reports while keeping architecture and ownership with humans]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-06-28T04:00:39Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [Artificial Intelligence]() Tags: [ai](), [qa](), [reports](), [review]() ### AI overview The article argues that AI should support investigation, review, and QA reporting, while humans retain responsibility for architecture, product risk, and ownership. ### Source excerpt Use AI for investigation, review, and QA reports, while keeping architecture, product risk, and ownership with the human. ## T.J. Maher Elected to the Board of Directors of the Software Quality Group of New England DevFeed: [T.J. Maher Elected to the Board of Directors of the Software Quality Group of New England]() Original publisher: [Read original article]() Author: T.J. Maher (noreply@blogger.com) Published: 2026-06-17T23:26:28Z Content type: opinion Language: en Sources: [T.J. Maher]() Topics: [sqgne](), [Testing](), [Software Engineering]() Tags: [community](), [development](), [qa](), [quality](), [sqgne](), [testing]() ### AI overview T.J. Maher announces election to the volunteer board of directors of the Software Quality Group of New England, an all-volunteer nonprofit community focused on software quality. The article also mentions Maher's monthly SDET Lean Coffee discussion group. ### Source excerpt I've been elected to be on the Board of Directors of the Software Quality Group of New England (sqgne.org) on the volunteer leadership committee! The Software Quality Group of New England (SQGNE) is an all-volunteer, nonprofit professional community in New England dedicated to software quality. Since its founding, SQGNE has brought together QA engineers, SDETs, and software quality professionals for monthly educational presentations, discussions, and networking -- covering everything from testing techniques and process improvement to the latest tools and industry trends. I also have founded and moderate an online monthly SDET Lean Coffee discussion group, bringing together software development engineers in test, quality engineers, and automation practitioners to exchange ideas, challenges, and emerging practices. After our hiatus during the summer, we will be meeting monthly up in Burlington, MA. Check us out! Happy Testing! -T.J. Maher Software Engineer in Test BlueSky | YouTube | LinkedIn | Articles ## Breakpoint 2026: Highlights from Day 3 DevFeed: [Breakpoint 2026: Highlights from Day 3]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-02T13:03:05Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [AI Agent](), [Model Context Protocol](), [Test automation](), [MCP](), [LangChain](), [observability](), [Retrieval Augmented Generation (RAG)](), [Playwright](), [Cypress](), [debugging](), [Selenium]() Tags: [2026](), [agent](), [ai](), [automation](), [breakpoint](), [ci-cd](), [debugging](), [github](), [github-copilot](), [langchain](), [mcp](), [model-context-protocol](), [observability](), [qa](), [rag](), [testing](), [vscode]() ### AI overview A recap of Breakpoint 2026 Day 3 covering practical masterclasses and lightning talks on AI-driven test generation, non-deterministic testing, MCP-powered QA workflows, automated AI evaluation, and production testing practices. ### Source excerpt All sessions, masterclasses, and lightning talks from Breakpoint 2026 Day 3, covering AI agent testing, test automation with LangChain and CrewAI, MCP-powered QA workflows, Playwright network interception, non-deterministic testing, and building production-grade AI systems. ## Breakpoint 2026: Highlights from Day 2 DevFeed: [Breakpoint 2026: Highlights from Day 2]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-01T11:42:52Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Artificial Intelligence](), [Testing](), [Software Testing](), [Software Engineering](), [API]() Tags: [2026](), [ai](), [api-testing](), [breakpoint](), [generative-ai](), [llms](), [qa](), [testing]() ### AI overview This article highlights Day 2 of Breakpoint 2026, focusing on evidence-based discussions of AI-native quality engineering, enterprise AI testing, the future of test engineering, changing QA practices, and human factors in evolving testing roles. Sessions address enterprise AI security and scalability, generative AI, self-healing systems, LLM limitations, and organizational approaches to sustaining quality while increasing delivery speed. ### Source excerpt Explore all sessions, speaker talks, and announcements from Breakpoint 2026 Day 2, covering AI in software testing, the future of test engineering, human-AI collaboration in QA, API testing with AI code assistants, accessibility, and sustaining quality in AI-driven systems. ## Breakpoint 2026: Highlights from Day 1 DevFeed: [Breakpoint 2026: Highlights from Day 1]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-01T09:25:38Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Testing](), [Artificial Intelligence](), [Test automation](), [Automation](), [API](), [Security](), [prompt injection](), [code productivity](), [Accessibility]() Tags: [2026](), [ai](), [automation](), [breakpoint](), [developer-productivity](), [prompt-injection](), [qa](), [security](), [software-testing](), [testing]() ### AI overview Breakpoint 2026 Day 1 examines how AI-native quality engineering can reduce the traditional trade-off between testing speed and quality. It covers AI-assisted test execution, Copilot-driven automation, intent-based workflows, agentic QA systems, and frameworks for testing AI agents across reasoning, actions, and end-to-end outcomes. ### Source excerpt Explore the biggest announcements, expert insights, and conversations from Breakpoint 2026 Day 1, covering AI in software testing, test automation, developer productivity, accessibility testing, and the future of intelligent testing workflows. ## Designing a custom AI agent for repetitive QA workflows DevFeed: [Designing a custom AI agent for repetitive QA workflows]() Original publisher: [Read original article]() Author: Rimple Sharma Published: 2026-05-13T09:34:47Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Agent](), [Automation](), [test](), [test data](), [configuration](), [pull-requests](), [consistency]() Tags: [ai-agent](), [ai-skills](), [automation](), [configuration](), [consistency](), [pull-requests](), [qa](), [software-testing](), [test-automation](), [test-data]() ### AI overview This article defines a repetitive QA workflow involving configuration updates, test-data additions, mapping checks, coverage validation, and pull requests. It argues that the main bottleneck is contextual decision-making and cross-file validation, which motivates designing a custom AI agent. ### Source excerpt Part 1: Core problem definition, three pillars of an effective agent & why structured instructions matter.Source: AI generated image Engineers on QA teams spend a disproportionate share of their time on work that follows a consistent pattern: updating configurations, modifying test artifacts, executing focused validations, and managing pull requests. The tasks are well-defined but the repetition adds up fast. Individually, none of this is complicated. But taken together, these tasks are: Repetitive Error-prone Hard to repeat reliably During my time in automation, I kept running into the same problem. Every release brought the same requirements. New datasets had to be added so the test suite could cover the scenarios tied to them. It was never a one-time effort. The same work had to be repeated across different combinations, every single time. The process typically looked like this: 1. Updating multiple configuration files. 2. Adding the corresponding test data. 3. Making sure everything was mapped correctly. 4. Validating coverage and consistency. 5. Committing the changes and raising a PR. Each step on its own was straightforward. But strung together and repeated across releases, it consumed a meaningful chunk of time that could have gone elsewhere. Problem breakdownFig 2.0 Pain of manual repetitive process (Source: AI generated image) Each step is straightforward. But together, context switching between files, making manual edits, double checking mappings, it added up to 30 to 60 minutes per release cycle in our case. Miss one mapping, introduce a typo, and the build breaks. It wasn't hard work, just repetitive. And that's exactly the kind of work that makes test suites brittle over time. Even with automation in place, every iteration still required someone to identify what had changed, figure out which files were affected, and make sure everything stayed consistent across the board. The framework we had was solid. Scripts handled the heavy lifting well. But there ## Breakpoint 2026 Speaker Spotlight: Andrew Knight DevFeed: [Breakpoint 2026 Speaker Spotlight: Andrew Knight]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-05-08T10:14:54Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [Behavior-driven development](), [Automation](), [Selenium](), [Playwright](), [MCP Server](), [Open Source]() Tags: [2026](), [ai](), [automation](), [breakpoint](), [breakpoint-spotlight](), [browser](), [enterprise](), [mcp](), [mcp-server](), [open-source](), [qa](), [quality-engineering](), [selenium](), [software](), [software-testing](), [test-coverage](), [testing](), [webdriver]() ### AI overview This speaker spotlight discusses how Andrew Knight combines open-source advocacy with enterprise software testing. It focuses on AI-powered test generation, behavior-driven development, context engineering, human oversight, and tools such as Selenium WebDriver, Playwright AI tooling, CLI skills, and an MCP server. ### Source excerpt Explore how Andrew Knight is using AI, behavior-driven development, and context engineering to reshape enterprise software testing at BrowserStack Breakpoint 2026. This speaker spotlight dives into AI-powered QA, test automation, and the future of modern quality engineering. [Next page]()