# Goal-Oriented Mutation Testing with Focal Methods

arXiv: https://arxiv.org/abs/1807.10953 · HTML: https://mlsanity.com/paper/1807.10953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [The Quality of Claude AI-authored Python Tests Is Not Weaker Than Human-authored Tests](https://mlsanity.com/paper/2608.15188/) (2026)

## Abstract (arXiv)

Mutation testing is the state-of-the-art technique for assessing the fault-detection capacity of a test suite. Unfortunately, mutation testing consumes enormous computing resources because it runs the whole test suite for each and every injected mutant. In this paper we explore fine-grained traceability links at method level (named focal methods), to reduce the execution time of mutation testing and to verify the quality of the test cases for each individual method, instead of the usually verified overall test suite quality. Validation of our approach on the open source Apache Ant project shows a speed-up of 573.5x for the mutants located in focal methods with a quality score of 80%.