Are Your Tests Slowing You Down? • Trisha Gee • GOTO 2025

Are Your Tests Slowing You Down? • Trisha Gee • GOTO 2025

by Trisha Gee

Applied Sciences & Engineering Computing & Cybersecurity UMZSoftware EngineeringUMZTSoftware testing and verification
🎙 Trisha Gee 👥 1.1M 📅 July 1, 2026 ⏱ 43 min 👁 2K 📄 expert opinion 🧭 2026-08-02
Available in: English (current) Français

Keywords

testingdeveloper productivityAIIDEtest generationtest executiontest debuggingDeveloper Productivity Engineering

Summary

Trisha Gee, Head of Developer Advocacy at Gradle, presents a talk at GOTO Copenhagen 2025 on how testing can slow down developers and how to mitigate this. She identifies three main areas: writing tests, troubleshooting tests, and running tests. Through live demos, she shows that while IDE features like live templates and code generation can speed up test writing, AI tools are often slow and non-deterministic, sometimes failing to generate useful tests. She emphasizes that the bottleneck is not typing but thinking about test cases and expected behaviors. She suggests pairing with QA professionals to improve test design and adopting a ‘stabilizer’ mindset when writing tests. For troubleshooting, she highlights the importance of fast feedback and good error messages. For running tests, she discusses the benefits of test selection and parallelization, mentioning tools like Gradle Enterprise and predictive test selection. She also references external sources, including an article on the myths of generative AI and a blog post on organizational physics. The talk concludes with recommendations for improving developer productivity in testing, such as using IDE features, leveraging AI for test data generation, and investing in fast feedback loops.

189 words

Critical Evaluation

The talk provides a pragmatic and experience-based perspective on testing productivity, grounded in the speaker’s extensive background as a developer advocate and Java Champion. The live demos effectively illustrate the challenges and potential solutions, though they also highlight the unpredictability of AI tools, which the speaker acknowledges. The argumentation is coherent, moving from problem identification to practical solutions, and the speaker does not shy away from expressing her own biases, such as her preference for JetBrains IDEs. The references to external sources, including an article on the myths of generative AI and a blog on organizational physics, add credibility, though they are not deeply integrated into the argument. The talk could benefit from more empirical data to support claims about productivity gains, as much of the evidence is anecdotal. The adéquation between title and content is strong, as the talk directly addresses the question of whether tests slow developers down and offers strategies to mitigate this. Overall, the talk is valuable for practitioners seeking to improve their testing workflows, but it would be enhanced by more rigorous evidence and a deeper exploration of the trade-offs involved.

186 words

Title / Content Match

The title accurately reflects the content, which focuses on identifying and addressing factors that slow down developers in testing workflows.

Quality & Reliability

7/10

The talk is based on the speaker's extensive industry experience and includes live demos and references to external sources. However, some claims are anecdotal and not backed by rigorous empirical evidence.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • Challenging the Myths of Generative AI — Referenced in the context of the productivity myth and the value of thinking in software development.
  • What is PsiU? ChatGPT — Referenced in the context of personality types and the stabilizer mindset for QA.
  • Predictive Test Selection — Mentioned as a technique for reducing test execution time.
  • Trisha Gee's website — Speaker's personal website for further resources.
  • Trisha Gee on GitHub — Speaker's GitHub profile for code examples.

Concurring Sources

  • Challenging the Myths of Generative AI — Supports the argument that automation does not necessarily increase productivity if it ignores the importance of thinking.
  • What is PsiU? ChatGPT — Provides a framework for understanding different work mindsets, supporting the recommendation for a stabilizer approach in QA.

Dissenting Sources

  • No specific discordant sources found — The talk does not directly contradict any major sources, but some claims about AI productivity could be challenged by studies showing mixed results.

External References

Contribution & Novelties

The talk offers a practical, experience-based overview of common bottlenecks in testing workflows and provides actionable recommendations, such as using IDE features, pairing with QA, and adopting a stabilizer mindset. It also highlights the potential and limitations of AI in test generation, emphasizing that thinking is the real bottleneck.

Pour aller plus loin :

  • Test-Driven Development — Core practice for writing tests before code, relevant to the talk’s emphasis on test design.
  • Developer Productivity Engineering (DPE) — Concept central to the talk’s theme of improving developer efficiency.
  • Predictive Test Selection — Technique mentioned for reducing test execution time.
  • Gradle Enterprise — Tool for build and test acceleration, relevant to the talk’s sponsor.

112 words

Radar Profile

The radar profile shows strong scores in quantity of information and global reliability, with slightly lower scores in technical depth and information quality. This indicates a talk that is informative and credible but may not delve deeply into technical details or provide rigorous evidence for all claims.

Reliability 7/10

💬 No comments were provided for analysis.