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The Quality Trail: January 2026 QA News

Home » Blogs/Events » The Quality Trail: January 2026 QA News

From the Desk of the Editor

Hey there, and welcome to the first issue of the Quality Trail newsletter for 2026! 

In today’s edition, we’ll be recapping some of the major events in 2025 that are poised to fundamentally shift the landscape of quality as we know it. If you know someone who could benefit from some or all of this information, don’t hesitate to forward it to them.

Last year, we started this newsletter with one, simple goal: Deliver the best resources available relating to software quality, so that you can stay informed and sharpen your knowledge in an industry that never stands still. We couldn’t be more appreciative of your support.

As always, let us know if you think we’ve missed something, or share the link with your colleagues or partners who may benefit from some or all of this information. You can also sign up to receive these testing updates via email.

– The QualityLogic Editorial Team

What’s Inside

Upcoming Conferences and Events 

  • Pacific NW Software Quality Conference: January 22 5:00 PM – 8:00 PM Pacific in Portland. This annual lightning talk event officially kicks off the PNSQC Call for Papers and sets the tone for the year ahead. Through it only lasts three hours, the line-up is packed with 8 speakers. 
  • Automation Guild ’26: Feb 9th – Feb 13th, 2026, online.

2025 in Review 

If you were one of the estimated 77% of people taking time off throughout the holidays this year, you’re probably just now getting back into the swing of things. What better way to do that than a quick look back on everything that happened (in terms of AI and QA) in 2025? Spoiler alert… there was a lot. Feel free to skim through this section if you’re strapped for time. 

Let us start at the beginning: 

Back in January, we here at QualityLogic published the first issue of this newsletter designed to help you not just stay updated, but ahead of the curve. This first version managed to largely stay away from GenAI, bringing DORA’s “Lead Time for changes (or a measure of how long it takes for committed code to start running in production) back into the conversation. The broader testing ecosystem also kept moving, with SmartBear’s acquisition of Qmetry reinforcing the continued consolidation of test tooling and management platforms. 

In February, a lot of QA conversation centered on sustainability and speed. Teams kept pushing ideas like shifting tests closer to the code to reduce flakiness, and the market continued to consolidate around assurance tooling when TASKING acquired LDRA, a name many safety and mission critical teams already associate with code analysis and verification. Also this month, Anthropic silently released Claude Code, and Andrej Karpathy coined the term “vibe coding” in a tweet to describe the process of talking to AI, running the code, and iterating with the key idea being to “forget that the code even exists.” This was to the disdain of many QA professionals, for reasons that are obvious to anyone reading this newsletter. 

March brought a clearer signal that quality was continuing to expand beyond functional testing and into operational outcomes. SolarWinds acquired Squadcast, which reinforced a shift right mindset where incident response, alert quality, and mean time to recovery increasingly influence how leaders define product quality in production. OpenAI added image generation to ChatGPT, which allowed users to upload any image and simply instruct AI on how to modify it. This feature alone triggered over 100 million sign-ups in a single week. 

Around April, model capability took a noticeable step forward, revolutionizing expectations across engineering and QA. Google introduced Gemini 2.5 as a thinking model aimed at stronger reasoning and more capable agents, then OpenAI followed with o3 and o4 mini, emphasizing longer thinking and better reliability for complex work. For QA teams, this translated into more AI assisted code and investigation work showing up in daily workflows. 

May kept a strong focus on engineering speed with stronger agent style workflows made possible by the aforementioned model releases in April. It was about this time that the theoretical benefits of long-running reasoning tasks in a loop began to take shape. Anthropic introduced Claude 4 and highlighted long running tasks and tool use, and the infrastructure for observing real user impact continued to consolidate as Virtana acquired Zenoss to deepen its observability platform. 

June was the month that accessibility deadlines stopped feeling theoretical. The European Accessibility Act came into effect on June 28, 2025, raising the stakes for accessibility testing programs and forcing many organizations to treat compliance as a release requirement with consequences for non-compliance. The implications reached farther than just the EU, since anyone wishing to sell products and services were bound by the same requirements and level of enforcement. 

July was a milestone for the testing profession itself. The International Software Testing Qualifications Board (ISTQB) formally approved the Testing with Generative AI syllabus, which signaled that GenAI skills were moving from informal experimentation into recognized competency and training paths. That same shift showed up in practice as more teams started building consistent guardrails for AI assisted test design, triage, and review. This was also the month that The Quality Trail got a dedicated AI section, and pull requests increasingly arrived partially or entirely written by AI tools which pushed testers to develop sharper instincts for risk, intent, and where extra scrutiny pays off. 

August put governance firmly on the 2025 checklist for QA professionals with EU exposure. On August 1, EU guidelines for providers of general purpose AI models began applying, which reinforced a simple leadership takeaway: if your products rely on models, quality now includes transparency, risk controls, and documentation that can stand up to scrutiny. Google also launched Nano Banana, the most advanced image editing and generation model that outperformed the competition on text generation, brand consistency, and ability to generate high-resolution images. 

September was the month of acquisitions. Check Point, a global leader in cyber security solutions, entered into an agreement to buy Lakera. Lakera is an AI first platform designed specifically to protect Agentic generative AI applications from security threats and misuse. A few days later, Atlassian (the makers of Confluence and Jira) announced that they would be acquiring DX, a platform with the singular goal of measuring developer productivity. Key themes from the newsletter included the need to avoid weaponizing metrics, protect culture, and create conditions for learning (because quality outcomes follow team dynamics more than tooling). 

In October, Playwright publicly build “agents” into test automation with a planner, generator, and healer flow that explicitly targets the bottlenecks QA teams complain about most: planning, authoring, and fixing tests. We also got Chrome DevTools MCP, and OpenAI’s Atlas web browser. Combined with the earlier release of Perplexity’s Comet browser, there was a strong signal that browsers themselves could become AI native surfaces with major implications for how we test user journeys, enabling new attack vectors like prompt injection. 

November taught us that too many AI tools create debt. While it may be tempting to chase shiny things, the greatest wins come from narrow well scoped use cases with human validation. Embrace, the user-focused observability platform, acquired SpeedCurve (the performance monitoring company), reinforcing the convergence of performance testing, real user monitoring, and release confidence. OWASP (Open Worldwide Application Security Project) published its  2025 Top 10 threats for LLM Applications, which gives teams a shared vocabulary for risks like prompt injection, excessive agency, system prompt leakage, and unbounded consumption – all of which matter more as agents touch more of the stack. We’re sure parts of this new vocabulary will persist in 2026 and beyond. Finally, OpenAI published guidance on understanding prompt injections, which helped cement prompt injection as a practical security testing category that belongs in everyday QA risk reviews and test plans. 

In December, year end reflections helped clarify what people will actually remember from 2025: reasoning models became the norm, agents and coding agents moved into everyday workflows, MCP had a breakout moment for connecting tools, and AI enabled browsers graduated from novelty to a serious testing surface. There’s a lot here, so for a full recap, check out Simon Willison’s ultimate 2025 year in review for LLM applications. OpenAI’s developer roundup emphasized that evals, graders, and tuning are maturing into a repeatable loop for shipping production grade agents, which lines up with our 2025 themes of Agentic AI, and the idea that QA success depends on reliability over adoption. OpenAI also published a detailed look at hardening ChatGPT Atlas against prompt injection, which is a good read for testers interested in how agentic applications can be exploited in the wild. Not to be outdone, Google published a post listing 60 of Google’s biggest AI announcements and updates in 2025.

Takeaways from the World Quality Report 2025 

The World Quality Report (WQR) is a leading global study produced by Capgemini, OpenText, and Sogeti that analyzes trends in quality engineering and testing by surveying 2,000 senior executives across countries and sectors. The results from the 17th publication reinforce what showed up in our 2025 timeline: GenAI became a useful part of the conversation. The question is no longer whether to adopt it, but how to scale it responsibly. A short two years after GenAI went mainstream, organizations report dramatically different outcomes. Here are some of the figures that we found intriguing: 

  • GenAI in Quality Engineering is past the novelty phase, but scaling is still rare. 43% of organizations report they are still just experimenting, 30% say GenAI is operational across multiple QE projects, and only 15% say they have scaled GenAI to meet enterprise-wide initiatives. 
  • Respondents report an average productivity boost of 19% from the use of AI, while about one third say they have observed very little improvement. This can be interpreted as a strong signal that workflow integration and measurement matter more than access to the latest and greatest tools. 
  • Use cases of AI are shifting toward earlier lifecycle work. The report notes a move from last year’s focus on documentation and analysis (56% test reporting, 53% defect analysis, and 54% knowledge management) toward shift left activities like test case design (46%) and requirements refinement (43%). 
  • There appears to be a significant gap between organizational interest in AI and readiness to adopt it effectively in the context of Quality engineering. Only 53% of respondents had upskilled testers with training on AI/ML fundamentals, meaning almost half of teams lack the foundational knowledge to leverage it confidently. Additionally, organizations are increasingly restricting access to LLM playgrounds in an effort to preserve risks posed by privacy and accuracy limitations, inhibiting skill development. 
  • Where GenAI is used for automation, it contributes real and useful outputs but is proving insufficient to replace human expertise. An average of 25% of new automated test scripts are being generated with GenAI driven tools. 
  • The biggest blockers look familiar, with data and governance rising in importance. Top challenges include secure, scalable test data (60%), adopting AI powered tools (58%), fragmented strategy and skill gaps (56%), and maintenance burden from flaky scripts (50%). 
  • Leaders still measure automation success in business relevant terms. Common KPIs include optimized test coverage (71%), team productivity gain (67%), product quality uplift (67%), release acceleration (60%), and cost savings or ROI (60%). 
  • When it comes to large-scale AI implementation, last year’s concerns were concentrated around how to start. This year, organizations are now grappling with the realities of execution. 

What We’ve Been Reading 

That’s All for Now!

That’s a wrap for this month’s edition of the Quality Trail! We hope you found something that sparked your curiosity, inspired a new approach, or simply reminded you why testing is such an exciting field. If something stood out to you, or if there’s a topic you’d love to see covered in a future issue, let us know. And don’t forget to share this newsletter with fellow testers who might find it useful.

Until next time, keep testing, keep learning, and keep pushing for quality!

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