Continuous integration (CI) pipelines are commonly used to execute regression tests before pull requests are merged. Regression test selection (RTS) aims to reduce the required testing effort and feedback time for developers. However, existing RTS techniques are imprecise for tests with cross-language links to compiled C++ binaries or unsafe if tests use external files. This is problematic because modern software in fact involves several programming languages and (non-)code artifacts such as configuration files. In this paper, we present BinaryRTS, a novel RTS technique that leverages dynamic binary instrumentation to collect the covered functions and accessed external files for each test. BinaryRTS then selects tests depending on changes issued to C++ binaries or external (non-)code artifacts. When evaluating BinaryRTS in our large-scale industrial context, we are able to exclude on average up to 74% of tests without missing real failures. We release BinaryRTS as the first publicly available RTS tool for software involving C++ code.

Regression test selection (RTS) and prioritization (RTP) techniques aim to reduce testing efforts and developer feedback time after a change to the code base. Using various information sources, including test traces, build dependencies, version control data, and test histories, they have been shown to be effective. However, not all of these sources are guaranteed to be available and accessible for arbitrary continuous integration (CI) environments. In contrast, metadata from version control systems (VCSs) and CI systems are readily available and inexpensive. Yet, corresponding RTP and RTS techniques are scattered across research and often only evaluated on synthetic faults or in a specific industrial context. It is cumbersome for practitioners to identify insights that apply to their context, let alone to calibrate associated parameters for maximum cost-effectiveness. This paper consolidates existing work on RTP and unsafe RTS into an actionable methodology to build and evaluate such approaches that exclusively rely on CI and VCS metadata. To investigate how these approaches from prior research compare in heterogeneous settings, we apply the methodology in a large-scale empirical study on a set of 23 projects covering 37,000 CI logs and 76,000 VCS commits. We find that these approaches significantly outperform established RTP baselines and, while still triggering 90% of the failures, we show that practitioners can expect to save on average 84% of test execution time for unsafe RTS. We also find that it can be beneficial to limit training data, features from test history work better than change-based features, and, somewhat surprisingly, simple and well-known heuristics often outperform complex machine-learned models.