Practical Pipeline-Aware Regression Test Optimization for Continuous Integration

TL;DR

This paper introduces a lightweight, pipeline-aware regression test optimization method using reinforcement learning, tailored for large-scale, multi-language CI environments, to improve test prioritization and reduce feedback latency.

Contribution

The authors propose a novel reinforcement learning-based approach that is lightweight, adaptable, and language-agnostic, specifically designed for industrial CI pipelines with diverse testing objectives.

Findings

Significant reduction in test execution time.

Improved detection of non-flaky, meaningful test failures.

Effective in large, multi-language, industrial CI environments.

Abstract

Massive, multi-language, monolithic repositories form the backbone of many modern, complex software systems. To ensure consistent code quality while still allowing fast development cycles, Continuous Integration (CI) is commonly applied. However, operating CI at such scale not only leads to a single point of failure for many developers, but also requires computational resources that may reach feasibility limits and cause long feedback latencies. To address these issues, developers commonly split test executions across multiple pipelines, running small and fast tests in pre-submit stages while executing long-running and flaky tests in post-submit pipelines. Given the long runtimes of many pipelines and the substantial proportion of passing test executions (98% in our pre-submit pipelines), there not only a need but also potential for further improvements by prioritizing and selecting tests. However, many previously proposed regression optimization techniques are unfit for an industrial context, because they (1) rely on complex and difficult-to-obtain features like per-test code coverage that are not feasible in large, multi-language environments, (2) do not automatically adapt to rapidly changing systems where new tests are continuously added or modified, and (3) are not designed to distinguish the different objectives of pre- and post-submit pipelines: While pre-submit testing should prioritize failing tests, post-submit pipelines should prioritize tests that indicate non-flaky changes by transitioning from pass to fail outcomes or vice versa. To overcome these issues, we developed a lightweight and pipeline-aware regression test optimization approach that employs Reinforcement Learning models trained on language-agnostic features. We evaluated our approach on a large industry dataset collected over a span of 20 weeks of CI test executions. When predicting...