Deciphering the story of software development through frequent pattern mining
Bibliographic record
Abstract
Abstract—Software teams record their work progress in task repositories which often require them to encode their activities in a set of edits to field values in a form-based user interface. When others read the tasks, they must decode the schema used to write the activities down. We interviewed four software teams and found out how they used the task repository fields to record their work activities. However, we also found that they had trouble interpreting task revisions that encoded for multiple activities at the same time. To assist engineers in decoding tasks, we developed a scalable method based on frequent pattern mining to identify patterns of frequently co-edited fields that each represent a conceptual work activity. We applied our method to our two years of our interviewee’s task repositories and were able to abstract 83,000 field changes into just 27 patterns that cover 95 % of the task revisions. We used the 27 patterns to render the teams ’ tasks in web-based English newsfeeds and evaluated them with the product teams. The team agreed with most of our patterns and English interpretations, but outlined a number of improvements that we will incorporate into future work. Index Terms—software teams; task tracking; mining software repositories; pattern mining I.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".