This package contains items for 9 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, 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:

Original data:

• For each project, its original artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)
• Evaluation of raters' notes by the assessor

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).

Anti-patterns are harmful phenomena repeatedly occurring, e.g., in software development projects. Though widely recognized and well-known, their descriptions are traditionally not fit for automated detection. The detection is usually performed by manual audits, or on business process models. Both options are time-, effort- and expertise-heavy, prone to biases, and/or omissions. Meanwhile, collaborative software projects produce much data as a natural side product, capturing their status and day-to-day history. Long-term, our research aims at deriving models for the automated detection of process and project management anti-patterns, applicable to project data. Here, we present a general approach for studies investigating occurrences of these types of anti-patterns in projects and discuss the entire process of such studies in detail, starting from the anti-pattern descriptions in literature. We demonstrate and verify our approach with the Fire Drill anti-pattern detection as a case study, applying it to data from 15 student projects. The results of our study suggest that reliable detection of at least some process and project management anti-patterns in project data is possible, with 13 projects assessed accurately for Fire Drill presence by our automated detection when compared to the ground truth gathered from independent data. The overall approach can be similarly applied to detecting patterns and other phenomena with manifestations in Application Lifecycle Management data.