Related papers: Hybrid Fault-Driven Mutation Testing for Python

Fuzzing is an important method to discover vulnerabilities in programs. Despite considerable progress in this area in the past years, measuring and comparing the effectiveness of fuzzers is still an open research question. In software…

Mutation testing consists of evaluating how effective test suites are at detecting artificially seeded defects in the source code, and guiding the improvement of the test suites. Although mutation testing tools are increasingly adopted in…

Our goal is to produce validation data that can be used as an efficient (pre) test set for structural stuck-at faults. In this paper, we detail an original test-oriented mutation sampling technique used for generating such data and we…

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…

This paper presents FauxPy, a fault localization tool for Python programs. FauxPy supports seven well-known fault localization techniques in four families: spectrum-based, mutation-based, predicate switching, and stack trace fault…

Mutation testing may be used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in…

Various mature automated test generation tools exist for statically typed programming languages such as Java. Automatically generating unit tests for dynamically typed programming languages such as Python, however, is substantially more…

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…

Dynamically Adaptive Systems modify their behav- ior and structure in response to changes in their surrounding environment and according to an adaptation logic. Critical sys- tems increasingly incorporate dynamic adaptation capabilities;…

Large Language Models (LLMs) can generate plausible test code. Intuitively they generate this by imitating tests seen in their training data, rather than reasoning about execution semantics. However, such reasoning is important when…

While hardware generators have drastically improved design productivity, they have introduced new challenges for the task of verification. To effectively cover the functionality of a sophisticated generator, verification engineers require…

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…

Gradual typing enables developers to annotate types of their own choosing, offering a flexible middle ground between no type annotations and a fully statically typed language. As more and more code bases get type-annotated, static type…

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several…

Diversity has been proposed as a key criterion to improve testing effectiveness and efficiency.It can be used to optimise large test repositories but also to visualise test maintenance issues and raise practitioners' awareness about waste…

Mutation analysis has many applications, such as assessing the quality of test cases, fault localization, test input generation, security analysis, etc. Such applications involve running test suite against a large number of program mutants…

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a.…

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…

The C++11/14 standard offers a wealth of features aimed at helping programmers write better code. Unfortunately, some of these features may cause subtle programming faults, likely to go unnoticed during code reviews. In this paper we…

We present a new method for model-based mutation-driven test case generation. Mutants are generated by making small syntactical modifications to the model or source code of the system under test. A test case kills a mutant if the behavior…