Software visualization tools: survey and analysis
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
Recently, many software visualization (SV) techniques and tools have become available. There is ample anecdotal evidence that appropriate visualization can significantly reduce the effort spent on system comprehension and maintenance, yet we are not aware of any quantitative investigation and survey of SV tools. This paper reports on a survey on SV tools which was conducted in spring 2000 with more than 100 participants. It addresses various functional, practical, cognitive as well as code analysis aspects that users may be looking for in SV tools. The participants of the survey rated the usefulness and importance of these aspects, and came up with aspects of their own. The participants were in general quite pleased with the SV tool they were using and mentioned various benefits. Nevertheless, a big gap between desired aspects and the features of current SV tools was identified. In addition, a list of improvements that should be done to current tools was assembled. Finally, the collected data tends to suggest that in general code analysis aspects were not highly supported by the tools.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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