Using Software Metrics Thresholds to Predict Fault-Prone Classes in Object-Oriented Software
Bibliographic record
Abstract
Most code-based quality measurement approaches are based, at least partially, on values of multiple source code metrics. A class will often be classified as being of poor quality if the values of its metrics are above given thresholds, which are different from one metric to another. The metrics thresholds are calculated using various techniques. In this paper, we investigated two specific techniques: ROC curves and Alves rankings. These techniques are supposed to give metrics thresholds which are practical for code quality measurements or even for fault-proneness prediction. However, Alves Rankings technique has not been validated as being a good choice for fault-proneness prediction, and ROC curves only partially on few datasets. Fault-proneness prediction is an important field of software engineering, as it can be used by developers and testers as a test effort indication to prioritize tests. This will allow a better allocation of resources, reducing therefore testing time and costs, and an improvement of the effectiveness of testing by testing more intensively the components that are likely more fault-prone. In this paper, we wanted to compare empirically the selected threshold calculation methods used as part of fault-proneness prediction techniques. We also used a machine learning technique (Bayes Network) as a baseline for comparison. Thresholds have been calculated for different object-oriented metrics using four different datasets obtained from the PROMISE Repository and another one based on the Eclipse project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".