Issue Close Time: Datasets + Prediction Classifiers

This project contains experiments on predicting the amount of time required to close issue reports in software repositories. Namely, it contains (a) issue lifetime datasets from 10 large software projects and (b) experiment scripts to generate decision tree classifiers that predict issue close time. To run the cross-validation experiment: 1. Compile the Java classes by running "make" or "make compile-java" on the command line 2. Configure the experimental setup by changing the variables at the top of run.sh 3. Run "bash run.sh" on the command line 4. Results can be found in out/ To run the round robin experiment: 1. Compile the Java classes by running "make" or "make compile-java" on the command line 2. Run "bash roundRobin.sh" on the command line 3. Results can be found in out/roundRobin The latest version of this project can be found on GitHub: https://github.com/reesjones/issueCloseTime

Related Organizations

Urgench State University
Uzbekistan
Colby College
United States

Keywords

Effort estimation, Software engineering, Issue close time, Decision trees

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