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8 results for “Search-Based Test Generation”
Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results
<p>This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions".</p> <p>The dataset contains:</p> <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>
Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation
<p>Data Package for "Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation"</p> <p>This package contains data generated as part of our experiments on EvoSuite and Defects4J.</p> <p>This paper is currently under submission.</p> <p>This package contains experimental data and the algorithms that generated them in folder "framework\test\". Specifically, in the folder "framework\test\Experiments", the data folders contain the suites generated by each technique for each project and fault ID. </p> <p>If you have questions, please contact Afonso Fontes at afonsohfontes@gmail.com.</p>
Adaptive Parameter Control for Search-Based Unit Test Generation — Replication Package
<h1>Running the experiments</h1> <p>Prerequisites for running the experiments:</p> <ul> <li><a href="https://www.docker.com/" target="_blank" rel="noopener">Docker</a></li> <li><a href="https://python-poetry.org/" target="_blank" rel="noopener">Poetry</a></li> </ul> <p>Steps to run the experiments:</p> <ol> <li>Download the experiment zip file you wish to run (<code>single_parameter_experiment.zip</code> or <code>multi_parameter_experiment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd single_parameter_experiment</code>).</li> <li>Run <code>poetry install --only main</code> to install all dependencies.</li> <li>To run the experiment, run <code>poetry run python run_experiment.py</code>.</li> <li>All results can be found in the folder <code>data/</code>.</li> </ol> <p>The modules used for the experiment are defined in the file <code>experiment_modules.py</code> and to see the experiment configuration, look in <code>run_experiment.py</code>.</p> <p><strong>Warning</strong>: the experiments take several weeks to run on a single machine, therefore it is advisable to split the experiments based on modules and run them in parallel.</p> <h1>Running the analysis</h1> <p>Prerequisites for running the analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the analysis:</p> <ol> <li>Download the analysis zip file (<code>analysis-adaptive-parameter-control.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd analysis-adaptive-parameter-control</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>If you want to re-run the Bayesian models locally on your machine, follow the <strong>optional</strong> step below, otherwise download and unzip the trace data from the replication package, i.e., <code>Trace data single.zip</code> and <code>Trace data multi.zip</code>. </li> <li>Place the <code>.nc</code> files in the corresponding folder: <code>analysis-adaptive-parameter-control/single_parameter/</code> or <code>analysis-adaptive-parameter-control/multi_parameter/</code>. <ol> <li>E.g. the <code>coverage_rate_model_single_parameter.nc</code> goes in the <code>single_parameter</code> folder, while the <code>coverage_rate_model_multi_parameter.nc</code> goes in the <code>multi_parameter</code> folder.</li> </ol> </li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Data analysis" and run all cells in order.</li> </ol> <h3>(Optional) Running the Bayesian models locally before the analysis.</h3> <ol> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Model specification" and run the three notebook cells.</li> </ol> <p><strong>Warning</strong>: this will take a long time, if you don't have the time, use the following alternative instead</p> <h1>Data</h1> <p>The data from when we ran the experiments is available in the <code>Single data.zip</code> and <code>Multi data.zip</code> files.</p> <p>The structure of these are the following:</p> <ul> <li>There are folders for each module the experiment was run on, further divided into each unique run. All these folders include: <ul> <li>Coverage reports.</li> <li>Complete logs for the unique run.</li> <li>A timeline over controlled parameter values during the test generation process.</li> <li>The complete Pynguin configuration for the run.</li> <li>The generated test suite.</li> </ul> </li> <li>There is one <code>statistics.csv</code> file containing some information about each run and their branch coverage timelines.</li> </ul> <h1>Running the parameter assignment analysis</h1> <p>Prerequisites for running the parameter assignment analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the parameter assignment analysis:</p> <ol> <li>Download the analysis zip file (<code>parameter-assignment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd parameter-assignment</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open the notebook <code>parameter_assignment_analysis.ipynb</code>.</li> <li>Run all cells in order.</li> </ol>
Search-Based Test Data Generation for SQL Queries: Appendix
<p>The appendix of our ICSE 2018 paper "Search-Based Test Data Generation for SQL Queries: Appendix".</p> <p>The appendix contains:</p> <ul> <li>The queries from the three open source systems we used in the evaluation of our tool (the industry software system is not part of this appendix, due to privacy reasons)</li> <li>The results of our evaluation.</li> <li>The source code of the tool. Most recent version can be found at https://github.com/SERG-Delft/evosql.</li> <li>The results of the tuning procedure we conducted before running the final evaluation.</li> </ul>
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>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This zip file is the artifact that contains the following:</p> <p>1. The binary. </p> <p>2. All classes used in the experiments.</p> <p>3. The experimental data.</p> <p> </p>
Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter
<p># Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter</p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p>
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