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8 results for “test case prioritization”
Replication Package "Applying Test Case Prioritization to Software Microbenchmarks"
<p>Replication package for the paper "Applying Test Case Prioritization to Software Microbenchmarks" accepted for publication in Empirical Software Engineering.</p>
Execution and data log: on the effectiveness of random and adaptive random test case prioritization
<p>Execution and data logs automatically generated by the test driver during experimentation. These results are similar (but not identical) to the results summarized in Table 1 of the following paper:</p> <p>Z. Q. Zhou, A. Sinaga, and W. Susilo, "On the fault-detection capabilities of adaptive random test case prioritization: case studies with large test suites," in Proceedings of the 45th Annual Hawaii International Conference on System Sciences (HICSS-45). IEEE, 2012, pp. 5584-5593.<br> https://doi.org/10.1109/HICSS.2012.454</p> <p>Compared with the above paper, the current dataset was collected from a different but similar set of experiments and included faulty versions 13, 23, and 26 of the Replace program---although these three versions were excluded from the experiments reported in the above paper due to their instability.</p> <p>If you find these data useful, please cite the above paper.</p>
Prioritizing test cases with Markov Chains: a Preliminary Investigation
<p>This replication package includes the inputs to replicate the results shown in a paper under revision. Data is structured in four folders, one for each considered case study (there is an initial example besides the three case studies reported in Section 5). Within each folder, it is possible to inspect the input files as well as the results, for both proposed heuristics, i.e., H1 and H2. The input formats can be consulted to understanding and replicating the study. The dataset also contains the algorithm written in Python.</p>
Data extraction form - A Systematic Literature Review on Prioritizing Software Test Cases using Markov Chains
<p>A data extraction form was created to gather all relevant data from the identified studies and manage the selection process in this systematic literature review. Some of the main information presented in this form was followed by a protocol, identifier (id) for each returned study, bibliographic reference, and answers to research questions. This catalog helps us in the data extraction and synthesis procedures and may be used by potentially interested, for example, for updating or replication. </p>
A study on the Application of Markov Chains to Prioritize Test Cases
<p>The entire results and the inputs to run the experiment are available in this dataset. The data is composed of five folders one for each case study and one for the running example presented in Section 3. Within each folder, it is possible do see the input files, as well as the results, both for H1 and H2. The input formats can be consulted to understanding and potentially replication of the study. This dataset also contains the algorithm, in Python. The algorithm is available on GitHub, we did not put the link because of the blind review.</p>
Dataset Package for the paper "Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments"
<p><br> #Dataset Package for the paper "Automated Test Cases Prioritization for Self-driving Cars in Virtual Environments"</p> <p><br> Description of the content:</p> <p><br> 1) "1_datasets" folder contains all the datasets concerning the test scenarios considered in the study. <br> <br> - AsFault supports two SDC software as test subjects while generating test cases, which we use to generate our test suites.<br> - BeamNG.AI. <br> - DeepDriving<br> - STRUCTURE: We report test subjects considered and organized with sub-folders for each of the supported two SDC software (i.e., Driving AIs):<br> - fullroad<br> - BeamNG_AI<br> - Driver_AI<br> 2) "2_SDC-source-code" contains all code and scripts composing the MATLAB/R prototypical implementation of SDC-pririotizer: </p> <p> - STRUCTURE:<br> - runSearch.m<br> - runGreedy.m<br> - plotResults.m<br> - permutationMutation.m<br> - mainGreedy.m<br> - permutationCrossover.m<br> - main.m<br> - initialPopulation.m<br> - fitness.m<br> - featureSelection.m<br> - faultDetectionRatePlots.m<br> - faultDetection.m<br> - (folder) r-script<br> - (folder) python<br> <br> 3) "3_study-data" folder contains all the main experimental data concerning the study conducted, RQs and all generated figures and main row-data reported in section results<br> <br> - results.csv<br> - greedy_results.csv<br> - (folder) tables<br> - (folder) figures<br> - (folder) DriverAI<br> - (folder) BeamNG_RF_1_5<br> - (folder) BeamNG_RF_1 </p>
Revisiting Machine Learning based Test Case Prioritization for Continuous Integration
<p>Aborted version</p>
Revisiting Machine Learning based Test Case Prioritization for Continuous Integration
<p>This repository contains a replication package for a research paper submitted to the 45th International Conference on Software Engineering (https://conf.researchr.org/home/icse-2023). We provide our code, data, and result for the ease of replicating our experiments.</p> <p><strong>Code</strong></p> <ul> <li>In the <strong>collect_data</strong> subdirectory, scripts for constructing TCP datasets are provided. We do dependency analysis using Understand (https://www.scitools.com/), so please download the related tools in advance.</li> <li>In the<strong> rl</strong> subdirectory, we provide the python implementation for algorithms RL, COLEMAN, PPO2-PO, ACER-PA, PPO1-LI.</li> <li>In the <strong>supervised_learning</strong> subdirectory, we provide implementations for MART, RankNet, RankBoost, CA, L-MART, which mainly rely on Ranklib (https://sourceforge.net/p/lemur/wiki/RankLib/.). We also provide implementation for DeepOrder.</li> </ul> <p><strong>Data</strong></p> <ul> <li>The <strong>origin</strong> subdirectory contains the original datasets collected from github using our scripts, including 11 projects.</li> <li>The <strong>smote</strong> subdirectory contains the datasets pre-processed by SMOTE.</li> </ul> <p><strong>Result</strong></p> <ul> <li>Results for <strong>RQ1</strong>, <strong>RQ2</strong>, <strong>RQ3</strong>, and <strong>threats to validity</strong> are provided in the corresponding subdirectories. Scripts for plotting figures are also provided.</li> </ul> <p><strong>Reference</strong></p> <p>We adopt code from previous work</p> <p>Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration (https://dl.acm.org/doi/abs/10.1145/3377811.3380369) Github repository: https://github.com/icse20/RT-CI</p> <p>Reinforcement Learning for Test Case Prioritization (https://ieeexplore.ieee.org/abstract/document/9394799) Github repository: https://github.com/moji1/tp_rl</p> <p>DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing (https://ieeexplore.ieee.org/abstract/document/9609187) Github repository: https://github.com/AizazSharif/DeepOrder-ICSME21</p> <p>A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments (https://ieeexplore.ieee.org/abstract/document/9086053) Github repository: https://github.com/jacksonpradolima/coleman4hcs</p>
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