# A Large-Scale Study of Call Graph-based Impact Prediction using Mutation Testing

arXiv: https://arxiv.org/abs/1812.06286 · HTML: https://mlsanity.com/paper/1812.06286/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Test-Free Fault Localization](https://mlsanity.com/paper/2310.01726/) (2023)
- [Causal fault localisation in dataflow systems](https://mlsanity.com/paper/2304.11987/) (2023)
- [Call Graph Evolution Analytics over a Version Series of an Evolving Software System](https://mlsanity.com/paper/2210.08316/) (2022)

## Abstract (arXiv)

In software engineering, impact analysis involves predicting the software elements (e.g., modules, classes, methods) potentially impacted by a change in the source code. Impact analysis is required to optimize the testing effort. In this paper, we propose an evaluation technique to predict impact propagation. Based on 10 open-source Java projects and 5 classical mutation operators, we create 17,000 mutants and study how the error they introduce propagates. This evaluation technique enables us to analyze impact prediction based on four types of call graph. Our results show that graph sophistication increases the completeness of impact prediction. However, and surprisingly to us, the most basic call graph gives the best trade-off between precision and recall for impact prediction.