Detecting architectural integrity violation patterns using machine learning
Bibliographic record
Abstract
Recent1 years have seen a surge of research into new ways of analyzing software quality. Specifically, a set of studies has been devoted to the impact the architectural relations among files have on system maintainability and file bug-proneness. The literature has proposed a set of rules for determining recurring architectural design flaws that occur in most complex systems, are associated with bugs, and thus incur high maintenance costs. In the present paper we advocate for using machine learning as the means of refining the approach and revealing new patterns of architectural integrity violations. Having trained a machine learning model on the combination of structural and historical information acquired from the Tiki open source project, we have been able to replicate three of the six known types of architectural violations and discover one new type, the Reverse Unstable Interface pattern. The implication of our study is that machine learning can provide valuable insights into the problem and discover novel patterns which would help software analysts to pinpoint specific architectural problems that may be the root causes of elevated bug- and change-proneness.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".