Can I read Predictive Mutation Analysis via the Natural Language Channel in Source Code on EtoBox?

Predictive Mutation Analysis via the Natural Language Channel in Source Code by Jinhan Kim; Juyoung Jeon; Shin Hong; Shin Yoo is a Computer Science article available to read on EtoBox.

What is Predictive Mutation Analysis via the Natural Language Channel in Source Code about?

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-t

Who reads Predictive Mutation Analysis via the Natural Language Channel in Source Code?

Author

Jinhan Kim; Juyoung Jeon; Shin Hong; Shin Yoo

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)