An Empirical Study on Bayesian Network-based Approach for Test Case Prioritization
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Bibliographic record
Abstract
A cost effective approach to regression testing is to prioritize test cases from a previous version of a software system for the current release. We have previously introduced a new approach for test case prioritization using Bayesian Networks (BN) which integrates different types of information to estimate the probability of each test case finding bugs. In this paper, we enhance our BN-based approach in two ways. First, we introduce a feedback mechanism and a new change information gathering strategy. Second, a comprehensive empirical study is performed to evaluate the performance of the approach and to identify the effects of using different parameters included in the technique. The study is performed on five open source Java objects. The obtained results show relative advantage of using feedback mechanism for some objects in terms of early fault detection. They also provide insight into costs and benefits of the various parameters used in the approach.
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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 | 0.001 |
| 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