Grant Dick

Andie Huang, Daniel Alencar da Costa, Grant Dick et al.

Continuous Integration (CI) systems often run many builds concurrently. In this setting, a legitimate build failure may not be caused by the code push that triggered it. Such unrelated build failures can waste developer effort because developers must determine whether the failure is actionable for their current change. We study 77,354 CI build failures from seven open source Apache projects to understand and predict unrelated build failures. We find that developers spend a median of 4 hours identifying whether a failure is related or unrelated to their push. We also perform a document analysis of 371 confirmed unrelated build failures sampled from 10,316 potentially unrelated failures. The analysis shows that unrelated test failures account for 20% of the cases in which developers classify build failures as unrelated. To predict unrelated build failures, we extract 33 features from issue reports, issue comments, and commits associated with the triggering push. We build semi-supervised Positive and Unlabeled (PU) learning models for seven Apache projects. The models achieve precision from 0.70 to 0.88, recall from 0.30 to 1.00, F1-score from 0.44 to 0.91, and AUC from 0.63 to 0.97. Feature importance analysis shows that CI latency, repeated error messages, and the number of preceding comments are useful indicators of unrelated build failures. These results show that PU learning can help developers identify build failures that are unlikely to be caused by their current push.

Grant Dick

Symbolic regression via genetic programming is a flexible approach to machine learning that does not require up-front specification of model structure. However, traditional approaches to symbolic regression require the use of protected operators, which can lead to perverse model characteristics and poor generalisation. In this paper, we revisit interval arithmetic as one possible solution to allow genetic programming to perform regression using unprotected operators. Using standard benchmarks, we show that using interval arithmetic within model evaluation does not prevent invalid solutions from entering the population, meaning that search performance remains compromised. We extend the basic interval arithmetic concept with `safe' search operators that integrate interval information into their process, thereby greatly reducing the number of invalid solutions produced during search. The resulting algorithms are able to more effectively identify good models that generalise well to unseen data. We conclude with an analysis of the sensitivity of interval arithmetic-based operators with respect to the accuracy of the supplied input feature intervals.