Extracting and Representing Cross-Language Dependencies in Diverse Software Systems
Bibliographic record
Abstract
This paper presents an approach for dealing with multi-language software systems. Much of the focus of reverse engineering tools is in analyzing software systems written in one programming language. Nowadays, the abundance of new technologies and languages used to ease application development raises new challenges for reverse engineers. Therefore, this paper focuses on finding cross-language dependencies in such diverse, heterogeneous software systems. Our approach uses source navigator extractors to produce the facts inside each language. Then, we show an example for finding Java Native Interface (JNI) dependencies between facts from Java and C/C++ code. The integrated facts are produced in GXL form, and conform to a unified schema introduced in the paper. This approach is useful from several perspectives. It illustrates how to retrieve the dependencies from software systems written in more than one programming language. Also, the generated facts conform to the GXL format, which is accepted by many reverse engineering tools. The usefulness and scalability of the approach are tested in a case study.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".