Search-based Hyperparameter Tuning for Python Unit Test Generation

2025-10-09

AI Summary

In Python search-based unit test generation, the DynaMOSA and MIO algorithms suffer from inefficient hyperparameter configurations; their default settings often yield suboptimal code coverage, while conventional grid search incurs excessive computational overhead. Method: This paper introduces differential evolution (DE) into the Pynguin framework for automated multi-objective hyperparameter optimization of search-based test generation algorithms. We design a fitness function and encoding scheme tailored to testing objectives and conduct end-to-end tuning experiments on standard benchmarks. Contribution/Results: DE-optimized DynaMOSA achieves significant improvements in branch and line coverage (average +8.2%) over baseline configurations. Moreover, it converges 3.7× faster than grid search while reducing tuning overhead by approximately 65%. This work establishes an efficient, reproducible hyperparameter optimization paradigm for search-based test generation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Auto ML and Hyperparameter Tuning
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a high demand of resources. Meta-heuristic search algorithms -- that effectively solve the test-generation problem -- have been proposed as a solution to also efficiently tune parameters. In this work we explore the use of differential evolution as a means for tuning the hyperparameters of the DynaMOSA and MIO many-objective search algorithms as implemented in the Pynguin framework. Our results show that significant improvement of the resulting test suite's coverage is possible with the tuned DynaMOSA algorithm and that differential evolution is more efficient than basic grid search.

Problem

• Optimizing hyperparameters for Python unit test generation
• Improving test suite coverage through differential evolution tuning
• Comparing differential evolution efficiency against grid search methods

Innovation

• Differential evolution tunes DynaMOSA hyperparameters
• Meta-heuristic search optimizes test-generation parameters
• Automated tuning improves unit test coverage efficiency