Linker-Based Program Extraction and Its Uses in Studying Software Evolution
Bibliographic record
Abstract
One of the problems of empirical studies of software evolution is the lack of an effective technique for extracting facts about very large software systems (millio ns of lines of code) over hundreds of versions. In this paper, we describe a linker-based approach to program extraction that is well suited for the study of large software system evolution. Our approach is particularly accurate, convenient and efficient. Its core c omponent is a fact extractor, called ldx, which is a customized version of the GNU code linker ld and performs both code linking and fact extraction. We call a ldx output graph an as-linked view (ALV). A sequence of ALVs of successive versions of a software system can be utilized to help understand software evolution. We describe our preliminary empirical studies on the evolution of two large open source systems, Linux and PostgreSQL. We discuss several interesting results, which either validate results from earlier studies or sugge st new concepts in studying software evolution.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".