Recovering a Balanced Overview of Topics in a Software Domain
Bibliographic record
Abstract
Domain analysis is a crucial step in the development of product lines and software reuse in general, in which domain experts try to identify the commonalities and variability between different products of a particular domain. This identification is challenging, since it requires significant manual analysis of requirements, design documents, and source code. In order to support domain analysts, this paper proposes to use topic modeling techniques to automatically identify common and unique concepts (topics) from the source code of different software products in a domain. An empirical case study of 19 projects, spread across the domains of web browsers and operating systems (totaling over 39 MLOC), shows that our approach is able to identify commonalities and variabilities at different levels of granularity (sub-domain and domain). In addition, we show how the commonalities are evenly spread across all projects of the domain.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".