Program comprehension through reverse‐engineered sequence diagrams: A systematic review
Bibliographic record
Abstract
Abstract Reverse engineering of sequence diagrams refers to the process of extracting meaningful information about the behavior of software systems in the form of appropriately generated sequence diagrams. This process has become a practical method for retrieving the behavior of software systems, primarily those with inadequate documentation. Various approaches have been proposed in the literature to produce from a given system a series of interactions that can be used for different purposes. The reason for such diversity of approaches is the need to offer sequence diagrams that can cater for the users' specific goals and needs, which can vary widely depending on the users' perception and understandability of visual representations and the target application domains. In this paper, we systematically review existing techniques in this context while focusing on their distinct purposes and potentials of providing more understandable sequence diagrams. In addition, a qualitative evaluation of such techniques is conducted to expose their adequacy and applicability for effective program comprehension. Finally, we list a set of possible extensions to the unified modeling language sequence diagram standard that we anticipate will enhance its versatility and understandability of program control flow, followed by a number of concluding remarks.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".