Proceedings of the 2008 international working conference on Mining software repositories
Bibliographic record
Abstract
Willkommen/bienvenues/welcome to the 2008 Working Conference on Mining Software Repositories in Leipzig, Germany! This year marks the debut of MSR as a Working Conference, after four successful years as ICSE's largest workshop. During this time, our research community has grown significantly. We have seen tremendous advances in the depth and scope of the kinds of research questions we are investigating. Furthermore, we have also expanded the breadth of MSR into areas such as social network analysis and visualization. So what does is mean to be a working conference this year? It means that the maturity of the work and the quality of the papers have made MSR a venue of choice for research in this area. And MSR is a working conference, not just a static repository of dry research results. We will continue to hold discussions as being central to the mission of the MSR event. We will continue to accept short papers whose novelty stirs discussion. And we will continue to hold the MSR challenge, where researchers participate in a qualitative competition, using their own techniques to attack a common problem. This year, the timing was much tighter than we might have liked, requiring a very short turnaround for authors from the announced CFP to the final submission deadline. The tight schedule also put a burden on the program committee, who responded magnificently! The call for papers attracted 42 submissions (21 full papers and 21 short papers) from Germany, Canada, USA, Switzerland, UK, Japan, Spain, the Netherlands, Austria, Greece, the Czech Republic, and India. Eight of the 21 full papers were accepted as such, and another four were accepted as short papers. Twelve of the 21 short paper submissions were also accepted. We are very pleased to offer two invited talks this year. First, Prof. Carlo Ghezzi of the Politechnico di Milano will give a keynote address entitled: Dynamically Evolving Software: Some Radical Changes of Perspective. Our second day will lead off with an invited tutorial by Prof. Abraham (Avi) Bernstein of the University of Zurich on data mining and machine learning. In addition to the papers and discussion sessions, we have a session devoted to this year's MSR Mining Challenge.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.071 | 0.043 |
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".