SMURF: A SVM-based Incremental Anti-pattern Detection Approach
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
In current, typical software development projects, hundreds of developers work asynchronously in space and time and may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and-or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns incrementally and on subsets of a system could reduce costs, effort, and resources by allowing practitioners to identify and take into account occurrences of anti-patterns as they find them during their development and maintenance activities. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently four limitations: (1) they require extensive knowledge of anti-patterns, (2) they have limited precision and recall, (3) they are not incremental, and (4) they cannot be applied on subsets of systems. To overcome these limitations, we introduce SMURF, a novel approach to detect anti-patterns, based on a machine learning technique - support vector machines - and taking into account practitioners' feedback. Indeed, through an empirical study involving three systems and four anti-patterns, we showed that the accuracy of SMURF is greater than that of DETEX and BDTEX when detecting anti-patterns occurrences. We also showed that SMURF can be applied in both intra-system and inter-system configurations. Finally, we reported that SMURF accuracy improves when using practitioners' feedback.
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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 | 0.000 | 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