HGA: Test Case Prioritization Results Using a Hypervolume-based Genetic Algorithm

A paper by Dario Di Nucci proposes a Hypervolume-based Genetic Algorithm (HGA) for test case prioritization in software regression testing. The method aims to schedule test cases to reveal faults earlier by optimizing multiple coverage criteria, such as statement and branch coverage. Empirical studies compare HGA against four state-of-the-art techniques, suggesting it is more cost-effective and scalable.

Use Cases

• Benchmarking test prioritization algorithms based on the described hypervolume metric and coverage criteria.
• Developing or improving genetic algorithm approaches for multi-objective optimization in software testing.
• Studying the trade-offs between test execution cost and fault detection effectiveness as mentioned in the description.

Strengths

• The proposed HGA method is empirically compared against four state-of-the-art techniques.
• The approach is described as scalable when the number of optimization criteria grows.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Dario Di Nucci via paperswithcode

Collection Method

Likely contains results from empirical studies on software test suites.