MSR4ML: Reconstructing Artifact Traceability in Machine Learning Repositories
Bibliographic record
Abstract
The increasing popularity of Machine Learning (ML) is generating challenges also for developers. The multitude of programming languages, libraries and available resources allow them to easily build their own models or algorithms. However, ML models are tightly connected to their data implying a different development process from other types of software. Software projects often rely on version control platforms, such as GitHub, but these platforms have not yet been extended to support ML projects. There is poor support for data versioning and no link between ML and software artifacts. Thus, traceability and model evolution can become challenging for developers. While some specific ML platforms exist, they still require considerable manual specification of ML artifacts and links between them. In this work, we propose a framework for automatic identification and traceability of links between data, code and ML model through Mining Software Repositories (MSR) techniques. Our tool combines static code analysis and mining commit data to identify ML, code and data artifacts, reconstruct links between them and retrieve commits that affect each end of the link. The objective is to increase productivity and the developers' awareness of their project through the recovered traceability.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".