µBert: mutation testing using pre-trained language models

Abstract

Mutation testing seeds faults using a predefined set of simple syntactic transformations, aka mutation operators, that are (typically) defined based on the grammar of the targeted programming language. As a result, mutation operators often alter the program semantics in ways that often lead to unnatural code (unnatural in the sense that the mutated code is unlikely to be produced by a competent programmer). Such unnatural faults may not be convincing for developers as they might perceive them as unrealistic/uninteresting, thereby hindering the usability of the method. Additionally, the use of unnatural mutants may have actual impact on the guidance and assessment capabilities of mutation testing. This is because unnatural mutants often lead to exceptions, or segmentation faults, infinite loops and other trivial cases. To deal with this issue, we propose forming mutants that are in some sense natural; meaning that the mutated code/statement follows the implicit rules, coding conventions and generally representativeness of the code produced by competent programmers. We define/capture this naturalness of mutants using language models trained on big code that learn (quantify) the occurrence of code tokens given their surrounding code. We introduce µBert, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it.