Mining the Lexicon Used by Programmers during Sofware Evolution
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
Identifiers represent an important source of information for programmers understanding and maintaining a system. Self-documenting identifiers reduce the time and effort necessary to obtain the level of understanding appropriate for the task at hand. While the role of the lexicon in program comprehension has long been recognized, only a few works have studied the quality and enhancement of the identifiers and no works have studied the evolution of the lexicon. In this paper, we characterize the evolution of program identifiers in terms of stability metrics and occurrences of renaming. We assess whether an evolution process similar to the one occurring for the program structure exists for identifiers. We report data and results about the evolution of three large systems, for which several releases are available. We have found evidence that the evolution of the lexicon is more limited and constrained than the evolution of the structure. We argue that the different evolution results from several factors including the lack of advanced tool support for lexicon construction, documentation, and evolution.
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 | 0.001 | 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 | 0.000 |
| 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