Automatically inferring concern code from program investigation activities
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
When performing a program evolution task, developers typically spend a significant amount of effort investigating and reinvestigating source code. To reduce this effort, we propose a technique to automatically infer the essence of program investigation activities as a set of concern descriptions. The concern descriptions produced by our technique list methods and fields of importance in the context of the investigation of an object-oriented system. A developer can rely on this information to perform the change task at hand, or at a later stage for a change that involves the same concerns. The technique involves applying an algorithm to a transcript of a program investigation session. The transcript lists which pieces of source code were accessed by a developer when investigating a program and how the different pieces of code were accessed. We applied the technique to data obtained from program investigation activities for five subjects involved in two different program evolution tasks. The results show that relevant concerns can be identified with a manageable level of noise.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it