IdeaWell

Project ideas from Hacker News discussions.

Dev-owned testing: Why it fails in practice and succeeds in theory

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Here are the four most prevalent themes from the Hacker News discussion regarding dev-owned testing and the role of QA:

1. Divergent Skillsets Between Developers and QA Many commenters argue that development and quality assurance require fundamentally different mindsets and skillsets. Developers are builders ("makers"), while QA specialists are testers ("breakers"), and attempting to combine the two often leads to reduced effectiveness in one or both roles.

"The are breakers. Good devs are makers. You can be both but I have yet to meet someone who is equally good in both mindsets." — weinzierl "I believe dev and QA are separate skillset. Of course there is overlap." — OptionOfT

2. Management Incentives Drive Testing Behavior A recurring theme is that engineering practices, including writing tests, are dictated by organizational incentives rather than technical best practices. If management prioritizes shipping features over quality or uses stack ranking, developers are incentivized to skip testing despite knowing its long-term value.

"Developers may understand that 'XYZ is better', but if management provides enough incentives for 'not XYZ', they're going to get 'not XYZ'." — pjdesno "When I came back, many of the tests were broken... Management didn't care at all... That made me realized exactly how much they valued unit tests." — wccrawford

3. The Value of Independent Verification (Fresh Eyes) Regardless of the structure (dev-owned or dedicated QA), many contributors emphasized the necessity of an independent perspective. Developers often suffer from "blind spots" due to their deep knowledge of the code, making an external tester crucial for catching issues related to user experience and edge cases.

"As a dev, it is simply not always a great idea that the same person that built the feature is the one testing it... I basically become blind to it because I know it too well." — javier2 "I have a conflict of interest... Even though I fully attempt to make perfect software, often I have blind spots or assumptions that an independent tester finds." — gwbas1c

4. The Stigma and Underutilization of QA Discussion participants noted that QA is often viewed as a lower-status role compared to development, leading to poor compensation, high turnover, and the hiring of under-skilled personnel. When QA is treated as a dumping ground for grunt work rather than a specialized technical role, the quality of testing suffers significantly.

"Companies think QA is shit, so they hire shit QA, and they get shit QA results." — pixl97 "Testing as a specialty means getting a pay cut and a loss in respect and stature... I wouldn't ever sell myself as a test automation engineer." — MoreQARespect

🚀 Project Ideas

Automated Test Maintenance and Legacy Test Recovery Tool

Summary

  • [Automatically repairs or rewrites flaky and outdated unit/integration tests when the codebase or testing framework changes.]
  • [Eliminates the "it's not worthwhile" pain point for developers, ensuring testing efforts remain valuable over time.]
Key Value
Target Audience Developers maintaining legacy codebases or frequently refactoring code.
Core Feature Static analysis of test failures linked to code changes, followed by AI-driven test case rewriting to match new paradigms or code structures.
Tech Stack Python (AST parsing), LLMs (Claude/GPT-4), GitHub/GitLab integration.
Difficulty Medium
Monetization Revenue-ready: SaaS subscription per repository.

Notes

  • [Addresses wccrawford's frustration: "the unit testing suite had fundamentally altered its nature. None of the tests worked and they all needed to be rewritten for a new paradigm."]
  • [Provides practical utility by saving developers from having to "give up on tests" due to maintenance overhead.]

Ambient Production Health Monitor

Summary

  • [A tool that correlates code changes to production incidents over long timeframes, assigning probabilistic ownership of latent bugs.]
  • [Solves the "blameless" accountability issue by highlighting which changes increased risk, even if the bug manifested years later.]
Key Value
Target Audience Engineering managers, On-call teams, and developers in high-reliability systems.
Core Feature Longitudinal analysis of version control history vs. incident databases (Sentry, PagerDuty) to identify "risky" commits or architectural patterns.
Tech Stack Go/Rust for ingestion, SQL for data warehousing, Elasticsearch for log correlation.
Difficulty High
Monetization Revenue-ready: Enterprise license + integration fees.

Notes

  • [Addresses Jtsummers' point: "If you introduce a flaw this year and it breaks the system in two years, it won't fall back on you but the poor sap that triggered your bug."]
  • [Incentivizes writing tests by making long-term code quality visible and quantifiable in performance reviews.]

QA-as-a-Code Reviewer (LLM Agent)

Summary

  • [An LLM-powered GitHub Action that acts as a strict, adversarial QA agent during the PR process, focused on specification adherence rather than syntax.]
  • [Replaces the "friction" of human QA misunderstandings with automated, tireless scrutiny of requirements versus implementation.]
Key Value
Target Audience Teams without dedicated QA, or devs wanting to reduce back-and-forth with QA.
Core Feature Ingests the ticket description/Jira story and the PR diff; identifies discrepancies between the stated requirements and the actual code changes.
Tech Stack GitHub Actions API, OpenAI/Claude API, Vector DB for context.
Difficulty Medium
Monetization Hobby (Open Source) OR Revenue-ready: Usage-based pricing (API calls).

Notes

  • [Addresses the "curse of knowledge" problem mentioned by OptionOfT, where devs know how they implemented the ticket, but QA has to guess.]
  • [Solves the "signal-to-noise ratio of bug tickets" issue by automating the initial requirement validation loop.]

Economic Incentive Alignment Simulator

Summary

  • [A decision-support tool for management that models the long-term cost of "stack ranking" and fast shipping vs. investing in testing culture.]
  • [Visualizes the hidden costs of technical debt and bugs that appear months later, helping justify QA resources or testing time.]
Key Value
Target Audience CTOs, Engineering Directors, and Product Managers.
Core Feature Monte Carlo simulation of software delivery based on variables like "QA staffing," "Test Coverage," and "Performance Review Rigor," projecting impact on stability and velocity.
Tech Stack Python (NumPy/Pandas), React/D3.js for visualization.
Difficulty Medium
Monetization Revenue-ready: Consulting/SaaS hybrid for enterprise planning.

Notes

  • [Addresses the root cause identified by pjdesno: "if your review was based on features shipped... and you were worried about being at the bottom of the stack, you'd skip tests."]
  • [Provides a framework to discuss the "broken environment" mentioned by eikenberry, quantifying the ROI of quality over speed.]

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