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# Let’s Talk Quality!

AI rapidly accelerates coding. However, it also introduces untested flaws and severe cognitive overload. True quality engineering must remain a strictly human responsibility.

- **Speakers:** [Lilia Gargouri](https://www.wearedevelopers.com/@lilia-gargouri)
- **Event:** World Congress 2026 Europe
- **Published:** July 9, 2026
- **Duration:** 27:29
- **URL:** https://www.wearedevelopers.com/videos/100012-let-s-talk-quality

## Summary

Quality is not merely correcting defects at the end of a project; it must be explicitly defined and engineered from the beginning through a shift-left approach. Relying on ISO 25010 standards, comprehensive software quality encompasses one functional and eight non-functional characteristics. When organizations embrace shared, explicitly defined terminology—leveraging standards from boards like IREB and ISTQB—they align domain requirements, technical architecture, and quality assurance. This unified vocabulary directly mitigates costly miscommunication, coordination waste, and technical debt across cross-functional teams.

Determining exactly when a product has been tested enough requires mapping prioritized test conditions directly to requirements based on risk. Jumping blindly from basic requirements to test cases often misses critical software gaps or leads to accidental over-testing. By systematically scaling test design in both breadth and depth, engineering teams maintain manageable, clean test data and build crucial traceability.

As teams adopt agentic AI systems for software development, they encounter unique quality hurdles mapped by the ISO 25059 AI extension, such as probabilistic functional adaptability, user controllability, and transparency. While AI rapidly accelerates coding, it routinely generates untested "fluff" in automated test generation and masks fundamental architectural flaws through unchecked large-scale refactorings. Because engineers must constantly review black-box outputs, AI introduces severe decision fatigue and cognitive overload. Ultimately, software is built for humans; therefore quality engineering—encompassing critical thinking, usability validation, and final accountability—remains a strictly human responsibility.

**Keywords:** software quality engineering, iso 25010 standards, agentic ai development, shift-left testing approach, risk-based test scaling, requirements engineering, istqb test terminology, developer decision fatigue, ai generated code flaws, software test traceability, cross-functional team communication, technical debt prevention, system validation, software architecture design, iso 25059 ai extension

## Chapters

1. **Introduction to software quality assurance in enterprise applications** (00:03) — A senior quality engineer introduces the focus on complex, long-life enterprise systems and the agenda for software quality parameters.
1. **Defining quality as more than fixing existing defects** (01:33) — Relying purely on testing reduces quality to defect correction, whereas shifting attention to non-functional attributes prevents technical debt.
1. **Applying quality characteristics across the software test pyramid** (02:50) — Different test levels support targeted quality attributes like fault tolerance at the component level and recoverability at the system level.
1. **Evaluating quality characteristics specifically for artificial intelligence systems** (04:10) — Integrating agentic behavior requires addressing new attributes like adaptability, probabilistic reliability, transparency, and user controllability.
1. **Aligning software quality directly with critical stakeholder needs** (05:51) — Prioritizing defined quality requirements mitigates business risks and prevents teams from unconsciously accepting potential project failures.
1. **Understanding software engineering as more than just coding** (07:11) — Successful engineering depends heavily on requirement gathering, architectural design, and rigorous system validation alongside the actual coding phase.
1. **Asking critical engineering questions to prevent software defects** (08:34) — Actively analyzing specifications, scalability, dependencies, and security during every stage builds quality before issues can emerge.
1. **Scaling test breadth and depth based on project risk** (10:01) — Systematically breaking down requirements into specific test conditions prevents over-testing while securing critical coverage for higher-risk functionalities.
1. **Mastering project languages for efficient communication and collaboration** (12:21) — Using unified terminology across domain, technical, and architectural categories reduces chaotic communication and prevents implementation misunderstandings.
1. **Implementing agentic software development systems for rapid delivery** (15:08) — Agentic systems allow single developers to complete complex coding cycles significantly faster by synthesizing intent and context into reviewed increments.
1. **Recognizing developer fatigue and poor test generation issues** (16:18) — Relying heavily on AI coding assistants causes decision fatigue, unreviewable black-box logic, and the proliferation of low-quality test generation.
1. **Using test coverage visualization tools to detect code fluff** (18:53) — Integrating specialized coverage tools exposes untested code gaps left by AI, allowing engineers to compel the system to improve reliability.
1. **Maintaining human accountability in artificial intelligence software development** (19:50) — Human engineering foundations remain crucial since artificial intelligence changes how software is built without transferring ultimate accountability away from developers.
1. **Cultivating a shared team mindset for software quality** (20:55) — Quality engineering is a shared responsibility demanding rigorous definition rather than an isolated testing phase tacked onto the end.
1. **Feeding software standards into artificial intelligence coding tools** (21:53) — The deep contextual knowledge and project history required to properly guide agentic systems make experienced human developers irreplaceable.
1. **Mitigating excessive fluff generation in automated test suites** (23:02) — Forcing strict traceability directly from requirements into test outputs gives deterministic structure to probabilistic AI coding models.
1. **Advocating for human interaction and usability testing investments** (25:23) — The inherent impossibility of automating usability analysis proves that building a successful product requires dedicated human testing resources.

## Related Moments

- [Approaching software quality as a subjective social science](https://www.wearedevelopers.com/videos/87-excellent-software-testing) (from "Excellent Software Testing")
- [Transforming software quality from assurance to enablement](https://www.wearedevelopers.com/videos/100184-self-service-quality-qa-without-qa) (from "Self-service Quality: QA Without QA")
- [The shifting mindset of modern quality assurance engineers](https://www.wearedevelopers.com/videos/1984-ai-as-a-test-designer-transforming-experience-into-automated-testing) (from "AI as a Test Designer: Transforming Experience into Automated Testing")
- [Introduction to the speaker and visual testing journey](https://www.wearedevelopers.com/videos/540-let-s-get-visual-visual-testing-in-your-project) (from "Let's get visual - Visual testing in your project")
- [Motivations for adopting AI to enhance developer productivity](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) (from "Navigating the AI Revolution in Software Development")
- [Establishing a company-wide approach to software quality assurance](https://www.wearedevelopers.com/videos/428-automated-code-quality-checks-with-custom-sonarqube-rules) (from "Automated Code Quality Checks with Custom SonarQube Rules")

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- [Exploring AI: Opportunities and Risks for Developers](https://www.wearedevelopers.com/magazine/522-exploring-ai-opportunities-and-risks-for-developers)
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