# Efficient Incremental Code Coverage Analysis for Regression Test Suites

arXiv: https://arxiv.org/abs/2410.21798 · HTML: https://mlsanity.com/paper/2410.21798/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Targeted Test Selection Approach in Continuous Integration](https://mlsanity.com/paper/2509.10279/) (2025)
- [FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks](https://mlsanity.com/paper/2610.01967/) (2026)

## Abstract (arXiv)

Code coverage analysis has been widely adopted in the continuous integration of open-source and industry software repositories to monitor the adequacy of regression test suites. However, computing code coverage can be costly, introducing significant overhead during test execution. Plus, re-collecting code coverage for the entire test suite is usually unnecessary when only a part of the coverage data is affected by code changes. While regression test selection (RTS) techniques exist to select a subset of tests whose behaviors may be affected by code changes, they are not compatible with code coverage analysis techniques -- that is, simply executing RTS-selected tests leads to incorrect code coverage results. In this paper, we present the first incremental code coverage analysis technique, which speeds up code coverage analysis by executing a minimal subset of tests to update the coverage data affected by code changes. We implement our technique in a tool dubbed iJaCoCo, which builds on Ekstazi and JaCoCo -- the state-of-the-art RTS and code coverage analysis tools for Java. We evaluate iJaCoCo on 1,122 versions from 22 open-source repositories and show that iJaCoCo can speed up code coverage analysis time by an average of 1.86x and up to 8.20x compared to JaCoCo.