Test Prioritization in Continuous Integration Environments

TL;DR

This study evaluates and combines diversity-based and history-based test prioritization heuristics to improve early fault detection in continuous integration environments, demonstrating their effectiveness through empirical analysis of open-source projects.

Contribution

It investigates the combined use of diversity-based and history-based test prioritization in CI, highlighting the predictive power of failure history and the benefits of their integration.

Findings

History-based failure knowledge effectively predicts faults in CI.

Combining diversity and history improves test prioritization effectiveness.

History-based diversity using NCD Multiset offers superior effectiveness with higher overhead.

Abstract

Two heuristics namely diversity-based (DBTP) and history-based test prioritization (HBTP) have been separately proposed in the literature. Yet, their combination has not been widely studied in continuous integration (CI) environments. The objective of this study is to catch regression faults earlier, allowing developers to integrate and verify their changes more frequently and continuously. To achieve this, we investigated six open-source projects, each of which included several builds over a large time period. Findings indicate that previous failure knowledge seems to have strong predictive power in CI environments and can be used to effectively prioritize tests. HBTP does not necessarily need to have large data, and its effectiveness improves to a certain degree with larger history interval. DBTP can be used effectively during the early stages, when no historical data is available, and also combined with HBTP to improve its effectiveness. Among the investigated techniques, we found that history-based diversity using NCD Multiset is superior in terms of effectiveness but comes with relatively higher overhead in terms of method execution time. Test prioritization in CI environments can be effectively performed with negligible investment using previous failure knowledge, and its effectiveness can be further improved by considering dissimilarities among the tests.