Related papers: Empirical Evaluation of Mutation-based Test Priori…

Mutation analysis is one of the most effective, but costly means of assessing the ability of software test suites to prevent bugs. Traditional mutation analysis involves producing and evaluating syntactic variants of the original to check…

Mutation testing is used extensively to support the experimentation of software engineering studies. Its application to real-world projects is possible thanks to modern tools that automate the whole mutation analysis process. However,…

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…

Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to simulate…

Mutation testing was proposed to identify weaknesses in test suites by repeatedly generating artificially faulty versions of the software (mutants) and determining if the test suite is sufficient to detect them (kill them). When the tests…

Background: Test flakiness is identified as a major issue that compromises the regression testing process of complex software systems. Flaky tests manifest non-deterministic behaviour, send confusing signals to developers, and break their…

Mutation testing has emerged as a powerful technique for evaluating the effectiveness of test suites for Deep Neural Networks. Among existing approaches, the statistical mutant killing criterion of DeepCrime has leveraged statistical…

Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale…

To alleviate the cost of regression testing in continuous integration (CI), a large number of machine learning-based (ML-based) test case prioritization techniques have been proposed. However, it is yet unknown how they perform under the…

Mutation testing is the state-of-the-art technique for assessing the fault detection capacity of a test suite. Unfortunately, a full mutation analysis is often prohibitively expensive. The CppCheck project for instance, demands a build time…

Software code complexity is a well-studied property to determine software component health. However, the existing code complexity metrics do not directly take into account the fault-proneness aspect of the code. We propose a metric called…

Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum…

There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highly time-constrained, and generates a…

Context: Automated fault localisation aims to assist developers in the task of identifying the root cause of the fault by narrowing down the space of likely fault locations. Simulating variants of the faulty program called mutants, several…

Recent research demonstrated that mutation-based fault localization techniques are relatively accurate and practical. However, these methods have never been compared and have only been assessed with simple hand-seeded faults. Therefore,…

Predictive Mutation Testing (PMT) is a technique to predict whether a mutant will be killed by using machine learning approaches. Researchers have proposed various machine learning methods for PMT under the cross-project setting. However,…

It is crucial that smart contracts are tested thoroughly due to their immutable nature. Even small bugs in smart contracts can lead to huge monetary losses. However, testing is not enough; it is also important to ensure the quality and…

Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are…

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The…

Higher-order mutation has the potential for improving major drawbacks of traditional first-order mutation, such as by simulating more realistic faults or improving test optimization techniques. Despite interest in studying promising…