Predictive Mutation Analysis via Natural Language Channel in Source Code

TL;DR

Seshat is a novel predictive mutation analysis technique that leverages natural language processing to accurately forecast entire kill matrices across program versions, significantly reducing mutation testing costs.

Contribution

It introduces Seshat, which predicts full kill matrices using natural language features, outperforming existing methods in accuracy and speed across different program versions.

Findings

Achieves an average F-score of 0.83 in predicting kill matrices.

Outperforms state-of-the-art predictive mutation testing by 0.14 F-score points.

Predictions are on average 39 times faster than actual mutation analysis.

Abstract

Mutation analysis can provide valuable insights into both System Under Test (SUT) and its test suite. However, it is not scalable due to the cost of building and testing a large number of mutants. Predictive Mutation Testing (PMT) has been proposed to reduce the cost of mutation testing, but it can only provide statistical inference about whether a mutant will be killed or not by the entire test suite. We propose Seshat, a Predictive Mutation Analysis (PMA) technique that can accurately predict the entire kill matrix, not just the mutation score of the given test suite. Seshat exploits the natural language channel in code, and learns the relationship between the syntactic and semantic concepts of each test case and the mutants it can kill, from a given kill matrix. The learnt model can later be used to predict the kill matrices for subsequent versions of the program, even after both the source and test code have changed significantly. Empirical evaluation using the programs in the Defects4J shows that Seshat can predict kill matrices with the average F-score of 0.83 for versions that are up to years apart. This is an improvement of F-score by 0.14 and 0.45 point over the state-of-the-art predictive mutation testing technique, and a simple coverage based heuristic, respectively. Seshat also performs as well as PMT for the prediction of mutation scores only. Once Seshat trains its model using a concrete mutation analysis, the subsequent predictions made by Seshat are on average 39 times faster than actual test-based analysis.