Ontological adaptive integration of reverse engineering tools
Bibliographic record
Abstract
The past decade has seen an increased awareness of the challenges involved in maintaining legacy software systems. Various reverse engineering tools designed to assist maintainers in carrying out software system understanding, analysis and migration tasks have been demonstrated. While progress has been made towards increasing the performance and usefulness of these tools, most continue to exist in isolation, using proprietary formats, technologies and schemata to represent the information they extract from software artifacts. This dissertation introduces the novel idea of using specially constructed, external tool adapters and a domain ontology to facilitate integration among reverse engineering tools. Using a constructive approach, we analyze software engineering tools, identifying the many similarities they share in terms of their architecture and the concepts they represent. Organizing these representational concepts into a domain ontology provides the knowledge required by specialized adapters to enable the sharing of services among tools participating in an integration. The design for a multi-phase methodology for sharing services among reverse engineering tools is presented. Various issues related to this service-sharing approach are explored. Our experiences in developing a proof of concept implementation based on the design and the challenges encountered are provided. We demonstrate that service-sharing among a set of reverse engineering tools can be accomplished by following our approach to ontology-based integration.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".