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

A Hypervolume-based Genetic Algorithm (HGA) is proposed to solve the Test Case Prioritization problem using multiple test coverage criteria. Two empirical studies show HGA is more cost-effective and scalable than four state-of-the-art techniques. The dataset likely contains results from these studies, such as performance metrics comparing prioritization techniques.

Use Cases

• Benchmarking test case prioritization algorithms based on the described hypervolume metric.
• Evaluating the cost-effectiveness of genetic algorithms for regression testing based on the empirical study results.
• Analyzing the scalability of prioritization techniques as the number of coverage criteria grows, as mentioned in the description.

Strengths

• Empirical results compare the proposed HGA against four state-of-the-art techniques.
• The method is evaluated for scalability with a growing number of test coverage criteria.

Limitations

• Row count and specific data format are 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

Collection Method

Likely contains results from empirical studies comparing test case prioritization techniques.