Selectively Combining Multiple Coverage Goals in Search-Based Unit Test Generation

TL;DR

This paper introduces a smart selection method for combining multiple coverage goals in search-based unit test generation, improving test coverage efficiency by reducing conflicting objectives.

Contribution

It proposes a novel smart selection approach that leverages coverage correlations and subsumption to optimize multiple coverage goals in SBST.

Findings

Smart selection outperforms combining all goals on 65.1% of classes.

The approach reduces optimization conflicts and improves coverage.

Experiments conducted on 400 Java classes with three genetic algorithms.

Abstract

Unit testing is a critical part of software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties, which cannot be captured by using an individual coverage criterion. Therefore, the state-of-the-art approach combines multiple criteria to generate test cases. As combining multiple coverage criteria brings multiple objectives for optimization, it hurts the test suites' coverage for certain criteria compared with using the single criterion. To cope with this problem, we propose a novel approach named \textbf{smart selection}. Based on the coverage correlations among criteria and the coverage goals' subsumption relationships, smart selection selects a subset of coverage goals to reduce the number of optimization objectives and avoid missing any properties of all criteria. We conduct experiments to evaluate smart selection on $400$ Java classes with three state-of-the-art genetic algorithms. On average, smart selection outperforms combining all goals on $65.1\%$ of the classes having significant differences between the two approaches.