Program comprehension with dynamic recovery of code collaboration patterns and roles
Bibliographic record
Abstract
Software functionalities and behavior are accomplished by the cooperation of code artifacts. The understanding of this type of source code collaboration provides an important aid to the maintenance and evolution of legacy systems. Normally, legacy systems were developed decades ago using early programming languages such as Cobol, Fortran or C etc., and a huge number of such systems are still in use. Coyle et. al estimate that only the systems written in Cobol have been account for more than 100 billion LOC. Moreover, a large amount of domain business knowledge has been coded in legacy software. After many years of maintenance, the quality of operation and maintainability has deteriorated dramatically due to many reasons, such as lack of up-to-date documents, lost of key personnel, shift of technology of inter-operating peripheral systems etc. The extraction of code artifact collaborations and their roles is therefore an important support in legacy software comprehension and design recovery. However, the original collaboration design information is dispersed at the implementation level. In this paper, we present a novel approach to efficiently recover and analyze code collaborations and roles based on dynamic program analysis technique. We also illustrate the tools that we have developed to support our approach and illustrate the viability of our approach in a case study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".