An experience report on scaling tools for mining software repositories using MapReduce
Bibliographic record
Abstract
The need for automated software engineering tools and techniques continues to grow as the size and complexity of studied systems and analysis techniques increase. Software engineering researchers often scale their analysis techniques using specialized one-off solutions, expensive infrastructures, or heuristic techniques (e.g., search-based approaches). However, such efforts are not reusable and are often costly to maintain. The need for scalable analysis is very prominent in the Mining Software Repositories (MSR) field, which specializes in the automated recovery and analysis of large data stored in software repositories. In this paper, we explore the scaling of automated software engineering analysis techniques by reusing scalable analysis platforms from the web field. We use three representative case studies from the MSR field to analyze the potential of the MapReduce platform to scale MSR tools with minimal effort. We document our experience such that other researchers could benefit from them. We find that many of the web field's guidelines for using the MapReduce platform need to be modified to better fit the characteristics of software engineering problems.
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 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".