On the effectiveness of data balancing techniques in the context of ML-based test case prioritization
Author:
Affiliation:
1. Carleton University, Canada
Funder
NSERC Discovery Grant
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3558489.3559073
Reference55 articles.
1. Separating passing and failing test executions by clustering anomalies
2. Striving for Failure: An Industrial Case Study about Test Failure Prediction
3. Striving for Failure: An Industrial Case Study about Test Failure Prediction
4. A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering
5. Reinforcement Learning for Test Case Prioritization
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