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Published February 7, 2022 | Version v2

Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code

  • 1. Linnaeus University
  • 2. University of West Bohemia

Description

This package contains artifacts for 15 real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. Therefore, this package includes the following:

Fire Drill in issue-tracking data:

  • Ground truth for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.
  • Coefficients for indicators for the first method, per project.
  • Detailed issue-tracing data for each project: what occurred and when.
  • Time logs for each project.

Fire Drill in source-code data:

  • Four technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.
  • Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.
  • Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).

Notes

The repository for the source code based method is at: https://github.com/sse-lnu/anti-pattern-models

Files

Fire-Drill-detection.zip

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