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2 results for “Search-Based Software Testing”
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>
Complementary materials for: Looking For Novelty in Search-based Software Product Line Testing (TSE)
<p>In this repository, we provide complementary materials for the following paper: </p> <pre>Y. Xiang, H. Huang, M. Li, S. Li and X. Yang, "Looking For Novelty in Search-based Software Product Line Testing" in <em>IEEE Transactions on Software Engineering</em>, vol. , no. 01, pp. 1-1, 5555. doi: 10.1109/TSE.2021.3057853 url: https://doi.ieeecomputersociety.org/10.1109/TSE.2021.3057853</pre> <p>1. Correlation analysis results (Pearson'r and p-value) are tabulated in <a href="https://www.zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/CorrelationAnalysisResults.xlsx?versionId=2f4ec2ab-3e48-420d-9819-b5e60ea43c06">CorrelationAnalysisResults.xlsx</a>; </p> <p>2. MATLAB scripts and raw data, which were used to perform correlation analyses, are given in <a href="https://zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/RawDataForCorrelationAnalysis.rar?versionId=5a168e2e-df32-4773-b8f3-4bf49de8d01d">RawDataForCorrelationAnalysis.rar</a>. </p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>We suggest reproducing the correlation analysis, and other experiments described in the paper, using the codes provided at Github: <a href="https://github.com/gzhuxiangyi/TSE_NS">https://github.com/gzhuxiangyi/TSE_NS </a></p> <p> </p>
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