Message from the MSR 2023 General and Program Co-Chairs
Bibliographic record
Abstract
is a thriving research community that organizes a yearly conference that has gained a solid reputation amongst software engineering researchers.MSR 2023 features seven tracks -Technical, Data and Tool Showcase, Mining Challenge, Registered Report, Industry, Tutorials, and Vision and Reflection -and prestigious awards -ACM SIGSOFT Distinguished Paper, Most Influential Paper, FOSS Impact Paper, MSR Ric Holt Early Career Achievement, MSR Foundational Contribution, and MSR Doctoral Research Award.This year, we received 184 submissions across the six paper tracks of the conference: the technical track (118), the data and tool showcase (42), the mining challenge (9), the registered reports track (8), and the industry track (7).For the seventh year in a row, MSR used double-anonymous reviewing in both the technical and mining challenge tracks to reduce reviewer bias and to increase fairness in the review process.Due to the specific requirements of the tracks, the data and tool showcase, the registered reports, and the industry tracks followed a single-blind review model.Where possible, all MSR 2023 tracks encouraged Open Science policies to enable the sharing of tools and data for reviewers and fellow researchers (at the technical track Open Science was mandatory).The technical track received 118 submissions (99 full and 19 short).Three full papers were desk-rejected.The remaining 115 papers went through a thorough review process.We accepted a total of 43 out of the 118 submissions, with an overall acceptance rate of about 36%.By paper length, our acceptance rates are 37 out of 99 full papers (37%) and 6 out of 19 short papers (32%).
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.025 | 0.024 |
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".