The MSR Cookbook: Mining a decade of research
Bibliographic record
Abstract
The Mining Software Repositories (MSR) research community has grown significantly since the first MSR workshop was held in 2004. As the community continues to broaden its scope and deepens its expertise, it is worthwhile to reflect on the best practices that our community has developed over the past decade of research. We identify these best practices by surveying past MSR conferences and workshops. To that end, we review all 117 full papers published in the MSR proceedings between 2004 and 2012. We extract 268 comments from these papers, and categorize them using a grounded theory methodology. From this evaluation, four high-level themes were identified: data acquisition and preparation, synthesis, analysis, and sharing/replication. Within each theme we identify several common recommendations, and also examine how these recommendations have evolved over the past decade. In an effort to make this survey a living artifact, we also provide a public forum that contains the extracted recommendations in the hopes that the MSR community can engage in a continuing discussion on our evolving best practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.075 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".