EVALUATING THEORIES FOR MANAGING IMPERFECT KNOWLEDGE IN HUMAN-CENTRIC DATABASE REENGINEERING ENVIRONMENTS
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
Modernizing heavily evolved and poorly documented information systems is a central software engineering problem in our current IT industry. It is often necessary to reverse engineer the design documentation of such legacy systems. Several interactive CASE tools have been developed to support this human-intensive process. However, practical experience indicates that their applicability is limited because they do not adequately handle imperfect knowledge about legacy systems. In this paper, we investigate the applicability of several major theories of imperfect knowledge management in the area of soft computing and approximate reasoning. The theories are evaluated with respect to how well they meet requirements for generating effective human-centred reverse engineering environments. The requirements were elicited with help from practical case studies in the area of database reverse engineering. A particular theory called "possibilistic logic" was found to best meet these requirements most comprehensively. This evaluation highlights important challenges to the designers of knowledge management techniques, and should help reverse engineering tool implementers select appropriate technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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