A Metric-Based Heuristic Framework to Detect Object-Oriented Design Flaws
Why this work is in the frame
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Bibliographic record
Abstract
One of the important activities in re-engineering process is detecting design flaws. Such design flaws prevent an efficient maintenance, and further development of a system. This research proposes a novel metric-based heuristic framework to detect and locate object-oriented design flaws from the source code. It is accomplished by evaluating design quality of an object-oriented system through quantifying deviations from good design heuristics and principles. While design flaws can occur at any level, the proposed approach assesses the design quality of internal and external structure of a system at the class level which is the most fundamental level of a system. In a nutshell, design flaws are detected and located systematically in two phases using a generic OO design knowledge-base. In the first phase, hotspots are detected by primitive classifiers via measuring metrics indicating a design feature (e.g. complexity). In the second phase, individual design flaws will be detected by composite classifiers using a proper set of metrics. We have chosen JBoss Application Server as the case study, due to its pure OO large size structure, and its success as an open source J2EE platform among developers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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