A WSAD-Based Fact Extractor for J2EE Web Projects
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
This paper describes our implementation of a fact extractor for J2EE Web applications. Fact extractors are part of each reverse engineering toolset; their output is used by reverse engineering analyzers and visualizers. Our fact extractor has been implemented on top of IBM's Websphere Application Developer (WSAD). The extractor's schema has been defined with the Eclipse Modeling Framework (EMF) using a graphical modeling approach. The extractor extensively reuses functionality provided by WSAD, EMF, and Eclipse, and is an example of component-based development. In this paper, we show how we used this development approach to accomplish the construction of our fact extractor, which, as a result, could be realized with significantly less code and in shorter time compared to a homegrown extractor implemented from scratch. We have assessed our extractor and the produced facts with a table- based and a graph-based visualizer. Both visualizers are integrated with Eclipse.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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