Mutation analysis can provide valuable insights into both the system under test 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 (MS) 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 Defects4J shows that Seshat can predict kill matrices with an average F-score of 0.83 for versions that are up to years apart. This is an improvement in F-score by 0.14 and 0.45 points over the state-of-the-art PMT technique and a simple coverage-based heuristic, respectively. Seshat also performs as well as PMT for the prediction of the MS only. When applied to a mutant-based fault localisation technique, the predicted kill matrix by Seshat is successfully used to locate faults within the top 10 position, showing its usefulness beyond prediction of MS. 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. We also show that Seshat can be successfully applied to automatically generated test cases with an experiment using EvoSuite.

This paper proposes a new mutation operator using neural network to generate plausible code elements to improve performance of mutation-based fault localization on omission faults. Unlike the existing mutation operators, the proposed mutation operator synthesizes new code elements at a given mutation site with a neural language model. We extended MUSE to use the proposed mutation operator, and conducted a case study with 3 omission faults found in JFreeChart of Defects4J. As a result, the accuracy of MUSE with the new mutation operator increased significantly in all three faults.