Issue Close Time: Datasets + Prediction Classifiers

View Dataset
Rees-Jones, Mitch;Martin, Matt;Menzies, Tim

Description

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

Citations (0)

Mentions (0)

Metrics

Dataset Index

1.6

FAIR Score

79%

Citations

0

Mentions

2

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0

Open Access

Assigned Domain

Subfield

Software

Field

Computer Science

Domain

Physical Sciences

Confidence Score

42%

Source

Scholar Data Model

Keywords

Issue close timeDecision treesSoftware engineeringEffort estimation

Normalization Factors

FT

57.69

CTw

1.00

MTw

1.00