Does a programmer's activity indicate knowledge of code?
Bibliographic record
Abstract
The practice of software development can likely be improved if an externalized model of each programmer's knowledge of a particular code base is available. Some tools already assume a useful form of such a model can be created from data collected during development, such as expertise recommenders that use information about who has changed each file to suggest who might answer questions about particular parts of a system. In this paper, we report on an empirical study that investigates whether a programmer's activity can be used to build a model of what a programmer knows about a code base. In this study, nineteen professional Java programmers completed a series of questionnaires about the code on which they were working. These questionnaires were generated automatically and asked about program elements a programmer had worked with frequently and recently and ones that he had not. We found that a degree of interest model based on this frequency and recency of interaction can often indicate the parts of the code base for which the programmer has knowledge. We also determined a number of factors that may be used to improve the model, such as authorship of program elements, the role of elements, and the task being performed.
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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 | 0.004 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".