MapReduce as a general framework to support research in Mining Software Repositories (MSR)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Researchers continue to demonstrate the benefits of Mining Software Repositories (MSR) for supporting software development and research activities. However, as the mining process is time and resource intensive, they often create their own distributed platforms and use various optimizations to speed up and scale up their analysis. These platforms are project-specific, hard to reuse, and offer minimal debugging and deployment support. In this paper, we propose the use of MapReduce, a distributed computing platform, to support research in MSR. As a proof-of-concept, we migrate J-REX, an optimized evolutionary code extractor, to run on Hadoop, an open source implementation of MapReduce. Through a case study on the source control repositories of the Eclipse, BIRT and Datatools projects, we demonstrate that the migration effort to MapReduce is minimal and that the benefits are significant, as running time of the migrated J-REX is only 30% to 50% of the original J-REX's. This paper documents our experience with the migration, and highlights the benefits and challenges of the MapReduce framework in the MSR community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it