# Test Prioritization in Continuous Integration Environments

arXiv: https://arxiv.org/abs/1809.00143 · HTML: https://mlsanity.com/paper/1809.00143/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration](https://mlsanity.com/paper/2406.19765/) (2024)
- [Systematic Literature Review on Application of Machine Learning in Continuous Integration](https://mlsanity.com/paper/2305.12695/) (2023)
- [Detecting Continuous Integration Skip : A Reinforcement Learning-based Approach](https://mlsanity.com/paper/2405.09657/) (2024)
- [Test Case Prioritization: A Snowballing Literature Review and TCPFramework with Approach Combinators](https://mlsanity.com/paper/2603.00183/) (2026)

## Abstract (arXiv)

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.