Visualizing Sequences of Debugging Sessions using Swarm Debugging
Bibliographic record
Abstract
In Software Engineering, one of the most important activities is debugging. Debugging is a set of techniques to detect, locate, and correct faults in a computer program. Modern Integrated Development Environments (IDEs), such as Eclipse or Visual Studio, provide infrastructure to support interactive debugging, during which a developer explores the source code of the system under development or maintenance. Although IDEs encourage developers to work collaboratively, debugging is still an individual activity. Furthermore, interactive debugging activity is limited by IDE debugging features that do not store previous debugging sessions. This condition forces developers to repeat debugging execution sessions to review the debugging information. In this paper, using the concept of Swarm Debugging, we present the Sequence Debugging Session View (SDV) tool. The primary goal is to capture the debugging information from a developer IDE (as Visual Studio) and store it. Then, the tool enables developers to retrieve the data in 3D interactive visualization and understand software behavior through the analysis and sharing of debugging session data. The main contribution of the tool is to assist on program comprehension and to reduce effort during software maintenance. To validate the solution, we performed two usage studies in real situations at a software house. The feedback from the evaluation of the tool suggests that the team could be helped on the software arrangement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".