Dynamic analysis for reverse engineering and program understanding
Bibliographic record
Abstract
The main focus of program understanding and reverse engineering research has been on modeling the structure of a program by examining its code. This has been the result of the nature of the systems investigated and the perceived goals of the reverse engineering activities. The types of systems under investigation have changed, however, and the maintenance objectives have evolved. Many legacy systems today are object-oriented and component-based. One of the most prominent maintenance objectives is system migration to distributed environments, most notably the World Wide Web, for interoperation with other systems. This new maintenance objective has a great impact on the types of models expected as products of reverse engineering. As the traditional static software analysis techniques keep their valuable role in program comprehension, additional techniques, especially those focusing on run-time analysis of the subject systems, become equally important. In this paper, we focus on the analysis of the system's dynamic behavior, as it pertains to understanding the system's processes and uses. We give an overview of currently used dynamic reverse engineering techniques and identify some challenges yet to be tackled.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".