Prioritizing test cases for regression testing

Author:

Elbaum Sebastian1,Malishevsky Alexey G.2,Rothermel Gregg2

Affiliation:

1. Dept. of Computer Science & Engineering, University of Nebraska, Lincoln, NE

2. Computer Science Dept., Oregon State Univ., Corvallis, OR

Abstract

Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection , is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.

Publisher

Association for Computing Machinery (ACM)

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1. Difficulty and Severity-Oriented Metrics for Test Prioritization in Deep Learning Systems;2023 IEEE International Conference On Artificial Intelligence Testing (AITest);2023-07

2. Test Case Prioritization using Transfer Learning in Continuous Integration Environments;2023 IEEE/ACM International Conference on Automation of Software Test (AST);2023-05

3. Modification-Impact based Test Prioritization for Process-Driven Applications;2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW);2023-04

4. Severity-Aware Prioritization of System-Level Regression Tests in Automotive Software;2023 IEEE Conference on Software Testing, Verification and Validation (ICST);2023-04

5. A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning;ACM Transactions on Software Engineering and Methodology;2023-01-31

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