Shimba—an environment for reverse engineering Java software systems
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 Shimba is a reverse engineering environment to support the understanding of Java software systems. Shimba integrates the Rigi and SCED tools to analyze and visualize the static and dynamic aspects of a subject system. The static software artifacts and their dependencies are extracted from Java byte code and viewed as directed graphs using the Rigi reverse engineering environment. The run‐time information is generated by running the target software under a customized SDK debugger. The generated information is viewed as sequence diagrams using the SCED tool. In SCED, statechart diagrams can be synthesized automatically from sequence diagrams, allowing the user to investigate the overall run‐time behavior of objects in the target system. Shimba provides facilities to manage the different diagrams and to trace artifacts and relations across views. In Shimba, SCED sequence diagrams are used to slice the static dependency graphs produced by Rigi. In turn, Rigi graphs are used to guide the generation of SCED sequence diagrams and to raise their level of abstraction. We show how the information exchange among the views enables goal‐driven reverse engineering tasks and aids the overall understanding of the target software system. The FUJABA software system serves as a case study to illustrate and validate the Shimba reverse engineering environment. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it