Recurring software testing challenges stem from human factors like ignored risks, schedule pressures, and flawed automation reliance, leading to superficial testing and overlooked critical issues. Emphasizing risk-based approaches, prevention over retesting, and integrated testing throughout development is crucial to addressing systemic quality gaps and reducing defect costs.

The Value of Software Testing

Randy Rice has a video Software Testing podcast - solo shows and interviews. Youtube only.

• https://www.youtube.com/playlist?list=PLGrFXPvIwr2WR6wn-Ngw7_9X_Ec3WO4vK
• https://www.riceconsulting.com/

Episode Details

• Show Notes: https://www.youtube.com/watch?v=x9p06y0wrZ4
• Published: 2026-06-28T17:25:03Z
• Duration: 00:29:52
• Author: Rice Consulting Services, Inc.

Overview

The podcast discusses recurring challenges in software testing, emphasizing that many issues stem from human factors rather than technological limitations. Over four decades of experience highlight systemic problems such as ignoring risk in testing practices, which leads to overlooking critical components in favor of minor issues, and the pressure to meet schedules, which often results in superficial testing. The text critiques the overreliance on automation for regression testing, noting its limited effectiveness in uncovering new defects under tight deadlines. It also stresses the importance of risk-based testing for efficient resource allocation, using a 1988 project as an example where this approach addressed major missed defects. Additionally, the podcast addresses the pitfalls of skipping reviews (e.g., requirements, code) that increase defects, despite peer reviews being more efficient over time. Limited testing phases, such as the overuse of 34 filters when only seven are recommended, are criticized, with the “coffee pot analogy” illustrating how multiple testing stages act as filters to catch defects.

The discussion also covers defects that persist across software releases, indicating deeper quality issues rather than isolated errors. It underscores the need to prevent defects from being created in the first place rather than retesting the same issues. Metrics focused on test volume (e.g., number of tests performed) are critiqued for lacking value, with a focus instead on measuring rework costs and financial impacts, such as the $16,000 average cost per defect post-release. Non-functional testing (e.g., performance, security) is another recurring mistake, often delayed until late in the cycle, leading to unmanageable risks that require specialized tools and environments. The podcast highlights the importance of documentation and knowledge retention to avoid repeating errors, with checklists praised for their value in safety-critical industries like aviation. Finally, it critiques the isolation of testing as a phase rather than integrating it into continuous decision-making, advocating for testing efforts spread throughout the development lifecycle.

What If

• What if you adopted risk-based testing as your primary testing strategy, prioritizing high-risk components over routine checks?

  • Move: Integrate risk assessments into each sprint, allocating 60% of testing time to high-impact features using historical defect data.
  • Why Now?: With AI and complex systems, the cost of overlooking critical defects has risen (e.g., $16k/post-release defect), and manual testing cant scale to cover all edge cases.
  • Expected Upside: Faster identification of systemic risks, reduced rework, and measurable cost savings from avoiding post-release fixes.

• What if you treated peer reviews as a non-negotiable part of your development workflow, even for solo projects?

  • Move: Implement a 3-step review process: code self-review, automated static analysis (e.g., SonarQube), and “shadow reviews” using AI tools to simulate peer feedback.
  • Why Now?: Schedule pressures often lead to skipped reviews, but studies show 40% of defects are caught post-release due to this oversight.
  • Expected Upside: 2030% reduction in post-release bugs, improved code reliability, and faster feature validation through early defect detection.

• What if you established a living knowledge base for testing insights, documenting every failure and their root causes?

  • Move: Create a lightweight, version-controlled repository (e.g., Notion or Git) to log defects, lessons learned, and testing strategies for each project.
  • Why Now?: Organizations that fail to retain knowledge repeat mistakes (e.g., the water utility projects performance failure), costing time/money.
  • Expected Upside: 50% faster onboarding for future projects, consistent testing practices, and benchmarking against past mistakes to avoid systemic errors.

Takeaway

• Prioritize risk-based testing by identifying and thoroughly testing high-risk components first, rather than focusing on minor issues, to ensure resources are allocated effectively and critical defects are addressed early.
• Allocate dedicated testing time upfront in project planning to avoid schedule pressures that lead to superficial testing; estimate and reserve time for thorough testing before launch.
• Balance automation with manual testing, using automation for regression tests but not relying on it for new test coverage, while leveraging human insight to identify edge cases automation might miss.
• Implement peer reviews for requirements, design, and code, even if time-consuming initially, to catch defects early and improve long-term efficiency; avoid discontinuing reviews after initial improvements.
• Integrate non-functional testing (e.g., performance, security) early and frequently into the development lifecycle, using specialized tools and environments, rather than postponing them until the end of a release cycle.