Structural comparison of source code between multiple programming languages
Bibliographic record
Abstract
Software developers are often faced with the task of comparing two or more versions of software. Typical usages of software comparison utilities include: a code-review prior to checkin, tracking down a recently introduced regression, and searching for code-clones in the source code (for future refactoring). However, most traditional source code comparison tools typically use simple text-to-text comparison (with some simple rule-based comparisons for comments), which has the drawback of showing superfluous differences during comparison. Many projects, for a variety of business reasons, ship products and software development kits (SDKs) using multiple programing languages. It is desirable to compare amongst languages in order to detect potential errors and understand the meaningful differences between the two codebases. In some cases, fixes may be implemented in one language, but not in the other. In this paper, we create a tool called the Software Difference Analyzer Tool (SDAT), a tool capable of comparing Java and CSharp code, to address some of the unique problems associated with cross-language comparison. Automated testing demonstrated SDAT reduces the number of reported differences by up to 40%. User testing has shown a 37% increase in speed and 28% increase in accuracy.
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.
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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".