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Why Your CI Pipeline Is Lying to You - Simon Stewart thumbnail

Why Your CI Pipeline Is Lying to You - Simon Stewart

Published 23 Apr 2026

Duration: 00:24:26

Flaky tests in CI/CD pipelines undermine reliability by causing intermittent failures due to shared state, timing issues, and environmental inconsistencies, requiring strategies like test exclusion, ownership, and prioritized fixes, alongside prevention through rigorous pre-CI testing and layered approaches, while AI aids debugging but not replacement, emphasizing iterative improvements over emotional attachment to code.

Episode Description

In this episode, I talk with Simon Stewart, professional software developer and former lead of the Selenium project for over 10 years, about one of th...

Overview

The podcast explores the challenges posed by flaky tests in software development, defined as tests that fail unpredictably due to a lack of repeatability. These tests undermine confidence in CI/CD pipelines by obscuring whether code changes are safe, while also consuming significant time investigating false failures. Key qualities of reliable tests include self-checking, fast feedback, isolation, and consistent results. The discussion highlights that flakiness often stems from shared state (e.g., database inconsistencies), race conditions, or environmental variables, emphasizing the need to root out systemic issues rather than merely masking symptoms.

Strategies for managing flaky tests include temporarily removing them from CI pipelines, assigning ownership to specific individuals for accountability, and prioritizing fixes for critical tests. Techniques like rerunning tests (up to three times) or implementing a "time-to-live" policy for unresolved flaky tests are suggested. Addressing root causes involves refactoring tests into a test pyramid structure, focusing on smaller, isolated tests, or even removing tests that lack value. The episode also touches on the role of AI in testing, which can generate test ideas or debug code but should not replace human judgment, due to its potential for overconfident or erroneous suggestions. Overall, the focus is on treating flakiness as an opportunity to improve system robustness and code reliability, rather than an unavoidable hurdle.

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