A Semantic Web-Enabled Approach for Dependency Management
Bibliographic record
Abstract
The use of external libraries in today’s software projects allows developers to take advantage of features provided by such application programming interfaces (APIs) without having to reinvent the wheel. However, APIs have also introduced new challenges to the software engineering community (e.g. API incompatibilities, software vulnerabilities, and license violations) that extend beyond traditional project boundaries and often involve different software artifacts. One potential solution to these challenges is to provide a technology-independent representation of software dependency semantics and its integration with knowledge from other software artifacts. In our research, we take advantage of the semantic web (SW) and its technology stack to establish a unified knowledge representation of build and dependency repositories. Given this knowledge base, we can now extend and integrate other (heterogeneous) resources to allow for a flexible and comprehensive global impact analysis approach. To illustrate the applicability of our SW-enabled modeling approach, we discuss two different applications. These applications illustrate how our modeling approach can not only integrate and reuse knowledge from dependency management systems and other software artifacts, but also take advantage of inference services provided by the SW to support novel software analytics services across artifact and project boundaries.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".