An empirical study on change recommendation
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
Recommending changes to programmers by exploiting their repetition tendencies during system evolution has been investigated by a number of studies. In our research we perform a change type (additions, deletions, and modifications) based analysis of the efficiency of change recommendation. We also investigate the programmer sensitivity of the repeated changes (i.e., the extent the same changes are repeated by the same programmers) of different change types. The existing studies did not perform such investigations. However, these investigations can be important for efficient ranking (i.e., prioritizing) and filtering of recommendations. According to our investigation on thousands of commits of five diverse subject systems we observe that modifications have a very low tendency (around 1.3%) of being repeated. We should primarily focus on recommending additions, and deletions. More importantly, overall 71% of the repeated changes are programmer sensitive. We believe that a change recommendation system that prioritizes recommendations considering programmer sensitivity can help programmers reuse previous changes in a time-efficient manner.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it