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73 results for “Mutation Testing”
Mutation testing of smart contracts at scale
<p>Replication package for the paper <a href="https://arxiv.org/abs/1909.12563">Mutation testing of smart contracts at scale</a>.</p> <p>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 im- portant to ensure the quality and completeness of the tests. There are already several approaches that tackle this challenge with mutation test- ing, but their effectiveness is questionable since they only considered small contract samples. Hence, we evaluate the quality of smart contract mutation testing at scale. We choose the most promising of the existing (smart contract specific) mutation operators, analyse their effectiveness in terms of killability and highlight severe vulnerabilities that can be in- jected with the mutations. Moreover, we improve the existing mutation methods by introducing a novel killing condition that is able to detect a deviation in the gas consumption, i.e., in the monetary value that is required to perform transactions.</p>
Data and material for the manuscript "Mutation testing and self/peer assessment: analyzing their effect on students in a software testing course"
<p><strong>This repository is composed of two different parts: </strong></p> <ul> <li><a href="https://zenodo.org/record/4464300/files/Assessment%20data%20and%20Mutation%20Scores.xlsx?download=1">Assessment data and Mutation Scores</a> file contains the student-generated data used in the experience.</li> <li><a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file contains the files to be able to reproduce the experience.</li> </ul> <p> </p> <p><strong>The </strong><strong> <a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file for the lab is used in two sessions:</strong></p> <p>Session 1: Development and assessment of test suites</p> <p>In this session, the student has to develop a test suite for a program under test. At the end of the session, the test suite will be evaluated against a set of assessment criteria regarding the quality of the developed test suite.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S1 pdf file , with the description of this session.</li> <li>Material-S1 zip file, with the files required to complete this session.</li> </ul> <p>Session 2: Evaluation applying mutation testing with MuCPP</p> <p>In this session, the test cases designed in the first part of this lab will be evaluated based on the mutation adequacy criterion. This will be done by using the <a href="https://ucase.uca.es/mucpp/">MuCPP mutation tool</a>.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S2 pdf file, with the description of this session.</li> <li>Material-S2 zip file, with the files required to complete this session.</li> </ul> <p><em>The source code files family.[cpp|hpp] have been adapted from a listing in [1]. Note that, while considered to be fault free in this lab, these source files are used in other sessions where students are expected to detect some defects in them.</em></p> <p>[1] S. Wiener and L. J. Pinson, The C++ Workbook. USA: Addison-Wesley Longman Publishing Co., Inc., 1990.</p>
Dataset for Gas-centered Mutation Testing of Ethereum Smart Contracts
<p>This dataset contains additional material for the paper entitled "<em>Gas-centered Mutation Testing of Ethereum Smart Contracts</em>", published by Journal of Software: Evolution and Process. This material includes:</p> <ul> <li><strong>Mutation tool</strong>, with the implementation of our gas-centered mutation operators.</li> <li><strong>Mutation operators - Example</strong>, with the code excerpts used to illustrate the gas perturbation caused by the mutation operators.</li> <li><strong>Smart contract - Accelerator_0b58e2.dir</strong>, with the mutants generated in a smart contract and their execution results.</li> <li><strong>Smart contracts_features.xlsx,</strong> with the complete list of smart contracts and their characteristics.</li> </ul>
Mutation Testing in the Wild: Findings from GitHub
<p>Supplementary material for the articule "Mutation Testing in the Wild: Findings from GitHub" submitted to the Empirical Software Engineering Journal. It includes:</p> <ul> <li>Literature_search_for_mutation_tools_2018-2021.xlsx. This document contains the papers found in the search for mutation testing tools performed between 2018 and 2021.</li> <li>Mutation_tools.xlsx: This document includes the 127 mutation testing tools identified in our study along with tables and graphs.</li> <li>Repositories_raw_data.xlsx: All repositories data mined from Github as evidence of use of the top 10 mutation tools analyzed in our study. In addition, tables with calculations and graphs used in our work are included.</li> <li>Search_strings_mutation_tools.xlsx: The search strings used to look for repositories using each mutation tool in Github.</li> </ul>
Data from: Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment
<p>The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that of beneficial and deleterious mutations can have comparable fitness impacts, i.e., the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast <em>Saccharomyces cerevisiae</em> by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we find no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.</p>
Mutation-guided Metamorphic Testing of Optimality in AI Planning
<p>Experimental results presented in the article <em>Mutation-guided Metamorphic Testing of Optimality in AI Planning</em>, both the figures and the raw data.</p> <p> </p> <p>The framework itself is hosted on GitHub (see the link below).</p> <p>In order to use the data with the framework, unzip the files of the archive <em>results.zip</em> inside the folder <em>results/</em>.</p>
Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper
<p>Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper</p>
Data from: Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment
Open the record for dataset details and reuse information.
Mutation Testing Operators
<p>Overview of Data</p> <p>This dataset lists the mutation operators for C-programming language</p> <p>Paper Abstract</p> <p>Safety-critical software must adhere to stringent quality standards and is expected to be thoroughly tested. However, exhaustive testing of software is usually impractical. The two main challenges faced by a software testing team are generation of effective test cases and demonstration of testing adequacy.</p> <p>This paper proposes an intuitive and conservative approach to determine the test adequacy in safety-critical software. The approach is demonstrated through a case study: the core temperature monitoring system of a nuclear reactor. We combine conservative test coverage of unique execution path test cases, and the results from mutation testing to determine the test adequacy.</p> <p>Although mutation testing is a powerful technique, the difficulty in identifying equivalent mutants has limited its practical utility. To gain confidence on the computed test adequacy: (i) faults during mutation testing must be induced at all possible execution paths of the code, (ii) properties of unkilled mutants must be studied, and (iii) all equivalent mutants must be detected. In this regard; results of static, dynamic and coverage analysis of the mutants is presented, and a technique to identify the likely equivalent mutants is proposed.</p> <p> </p>
Exploiting Pathogenic Tp53 Mutation for Early Diagnosis of Ovarian Cancer by Mean of Papanicolau Test
ClinicalTrials.gov study NCT04812938. IPD Sharing: YES. Countries: 1. Publications: 18.
Replication package for "Mutation testing of smart contracts at scale"
<p>Replication package for TAP2020 paper "Mutation testing of smart contracts at scale"</p> <p>Abstract: 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 completeness of the tests. There are already several approaches that tackle this challenge with mutation testing, but their effectiveness is questionable since they only considered small contract samples. Hence, we evaluate the quality of smart contract mutation testing at scale. We choose the most promising of the existing (smart contract specific) mutation operators, analyse their effectiveness in terms of killability and highlight severe vulnerabilities that can be injected with the mutations. Moreover, we improve the existing mutation methods by introducing a novel killing condition that is able to detect a deviation in the gas consumption, i.e., in the monetary value that is required to perform transactions.</p>
Search-based Test Data Generation for Mutation Testing: a tool for Python programs
<p>Test data generation for mutation testing consists of identifying a set of inputs that maximizes the number of mutants killed. Mutation Testing is an excellent test criterion for detecting faults and measuring the effectiveness of test data sets. However, it is not widely used in practice due to the cost and complexity to perform some activities as generating test data. Although test suites can be produced and selected manually by a tester this practice is susceptible to errors and tools are needed to facilitate it. Several tools have been developed to automate mutation testing, but, only a few address the test data generation. The present paper proposes an automated test data generation tool based on weak mutation for Python programming language using the Hill Climbing algorithm. For evaluation, we performed an experiment concerning the effectiveness and cost computational of the tool in a database composed of 348 mutants and we compare it with random generation. Overall, the experiment achieved an average mutation score of 86% for our proposed tool and random testing 64% on average.</p>
Mutation Testing for Task-Oriented Chatbots: Dataset
<p>Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.</p> <p>To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PM</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" accepted to the International Conference on Software Testing (ICST) 2023. The replication package is at https://github.com/FlowSs/RLMutation.</p>
FIG. 1 in OPINION Testing for the accumulation of deleterious mutations in asexual eukaryote genomes using molecular sequences
FIG. 1. Phylogeny of representative sexual and asexual Lachnidae with estimates of the numbers of replacement and silent substitutions for EF1a and CO2 on each branch. See text for description of estimation of the numbers of substitutions in each category. Phylogeny is from Normark (2000).
Ex Vivo Drug Sensitivity Testing and Mutation Profiling
ClinicalTrials.gov study NCT03860376. IPD Sharing: Not stated. Countries: 1. Publications: 2.
RAS Mutation Testing in the Circulating Blood of Patients With Metastatic Colorectal Cancer
ClinicalTrials.gov study NCT02502656. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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