Detecting and Mitigating Flaky Tests in Continuous Integration A Decision Catalogue for Quality Engineering Teams

Abstract

Continuous Integration (CI) has emerged as an essential method in contemporary software development, facilitating swift delivery and ongoing validation of software quality. The expanding volume and intricacy of automated test suites have engendered a recurrent issue referred to as test flakiness. Flaky tests demonstrate erratic results without any modifications to the application code, leading to ambiguity in build pipelines and diminishing trust in testing outputs. These inconsistent tests result in postponed releases, heightened maintenance expenses, decreased developer productivity, and eroded confidence in automation frameworks. Despite extensive research endeavors, several businesses find it challenging to properly discover, categorize, and prevent flaky tests inside continuous integration setups. This paper introduces a detailed decision catalog that aids quality engineering teams in identifying, diagnosing, prioritizing, and alleviating flaky tests. The proposed catalog brings together statistical analysis, machine learning detection methods, infrastructure monitoring, and automated repair solutions into a structured way to make decisions. A reference architecture is provided to enable practical implementation within business CI/CD ecosystems. The experimental evaluation demonstrates that the proposed framework significantly improves the precision of flaky test detection while reducing investigative effort and construct instability. The results offer practical recommendations for firms aiming to improve test reliability and expedite software delivery.