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Mutation testing is an established fault-based testing technique. It operates by seeding faults into the programs under test and asking developers to write tests that reveal these faults. These tests have the potential to reveal a large…

Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it "blindly" injects large…

We introduce $\mu$BERT, 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. Thus, the mutants…

Representational Similarity Analysis is a method from cognitive neuroscience, which helps in comparing representations from two different sources of data. In this paper, we propose using Representational Similarity Analysis to probe the…

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a…

Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program translation models still make elementary syntax errors,…

Patch prioritization ranks candidate patches based on their likelihood of being correct. The fixing ingredients that are more likely to be the fix for a bug, share a high contextual similarity. A recent study shows that combining both…

Mutation analysis is a well-established technique for assessing test quality in the traditional software development paradigm by injecting artificial faults into programs. Its application to deep learning (DL) has expanded beyond classical…

Mutation analysis is an effective technique to evaluate a test suite adequacy in terms of revealing unforeseen bugs in software. Traditional source- or IR-level mutation analysis is not applicable to the software only available in binary…

Software bugs pose an ever-present concern for developers, and patching such bugs requires a considerable amount of costs through complex operations. In contrast, introducing bugs can be an effortless job, in that even a simple mutation can…

Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test suite's capability to detect them, assuming that the test…

A classical heuristic in software testing is to reward diversity, which implies that a higher priority must be assigned to test cases that differ the most from those already prioritized. This approach is commonly known as similarity-based…

Automated test generators, such as search based software testing (SBST) techniques, replace the tedious and expensive task of manually writing test cases. SBST techniques are effective at generating tests with high code coverage. However,…

Mutation testing is an effective technique for assessing the effectiveness of test suites by systematically injecting artificial faults into programs. However, existing mutation testing techniques fall short in capturing many types of…

LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutations as few-shot examples or none at all. This can result in generic low-quality mutations, missed context-specific…

In the age of big data and machine learning, at a time when the techniques and methods of software development are evolving rapidly, a problem has arisen: programmers can no longer detect all the security flaws and vulnerabilities in their…

As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repair those faults. The best attestation of effectiveness for…

Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the…

Testing-based fault localization has been a research focus in software engineering in the past decades. It localizes faulty program elements based on a set of passing and failing test executions. Since whether a fault could be triggered and…

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e.,…