On Combining Commit Grouping and Build Skip Prediction to Reduce Redundant Continuous Integration Activity

Continuous Integration (CI) is a resource intensive, widely used industry practice. The two most commonly used heuristics to reduce the number of builds are either by grouping multiple builds together or by skipping builds predicted to be safe. Yet, both techniques have their disadvantages in terms of missing build failures and respectively higher build turn-around time (delays). We aim to bring together these two lines of research, empirically comparing their advantages and disadvantages over time, and proposing and evaluating two ways in which these build avoidance heuristics can be combined more effectively, i.e., the ML-CI model based on machine learning and the Timeout Rule. We empirically study the trade-off between reduction in the number of builds required and the speed of recognition of failing builds on a dataset of 79,482 builds from 20 open-source projects. We find that both of our hybrid heuristics can provide a significant improvement in terms of less missed build failures and lower delays than the baseline heuristics. They substantially reduce the turn-around-time of commits by 96