Continuous Integration (CI) provides a feedback loop for the change sets that developers produce. It is crucial that CI processes change sets quickly to provide timely feedback to developers and enable teams to rapidly release software updates. Prior work has made several advances in proposing automated approaches to speed up CI builds. While these approaches have been broadly adopted, CI platforms are flexible enough to enable teams to produce custom strategies to optimize or omit unnecessary or redundant tasks (i.e., developer-defined accelerations). Exploring developer-defined accelerations and identifying recurrent patterns within them may enable broader reuse and can inform recommendations to enhance software development efficiency.

In this paper, we set out to detect and catalog developer-defined CI accelerations. First, we propose clustering, rule-based, and ensemble approach to detect developer-defined accelerations in a dataset of 2,896 CircleCI jobs, which achieve an F1-score of up to 0.64. We then conduct a qualitative analysis of the detected developer-defined accelerations to create a detailed catalog of 14 patterns spanning four categories in purposes, 16 patterns spanning five categories in mechanisms, and three categories in magnitudes, from which we infer actionable implications for both the consumers and the providers of CI platforms. For example, developers can use our identified patterns to create templates that detect non-impactful changes to specific files, such as .yml and .json, used for project and Docker settings. This allows CI consumers and providers to conserve resources by skipping redundant builds for these changes.