A survey and evaluation of tool features for understanding reverse‐engineered sequence diagrams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sequence diagrams can be valuable aids to software understanding. However, they can be extremely large and hard to understand in spite of using modern tool support. Consequently, providing the right set of tool features is important if the tools are to help rather than hinder the user. This paper surveys research and commercial sequence diagram tools to determine the features they provide to support program understanding. Although there has been significant effort in developing these tools, many of them have not been evaluated using human subjects. To begin to address this gap, a preliminary study was performed with a specially designed sequence diagram tool that implements the features found during the survey. On the basis of an analysis of the study results, we discuss the features that were found to be useful and relate these to the tasks performed. It concludes by proposing how future tools can be improved to better support the exploration of large sequence diagrams. Copyright © 2008 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it