Code Coverage Aware Test Generation Using Constraint Solver

TL;DR

This paper presents a novel approach called CCTG that leverages code coverage data to guide constraint solver-based test generation, resulting in more effective test cases and improved fault detection.

Contribution

It introduces a new code coverage-aware test generation method that integrates code coverage analysis into the test input generation process using a constraint solver.

Findings

Generated effective test cases for real-world case studies.

Detected new faults with the proposed approach.

Outperformed traditional test generation methods.

Abstract

Code coverage has been used in the software testing context mostly as a metric to assess a generated test suite's quality. Recently, code coverage analysis is used as a white-box testing technique for test optimization. Most of the research activities focus on using code coverage for test prioritization and selection within automated testing strategies. Less effort has been paid in the literature to use code coverage for test generation. This paper introduces a new Code Coverage-based Test Case Generation (CCTG) concept that changes the current practices by utilizing the code coverage analysis in the test generation process. CCTG uses the code coverage data to calculate the input parameters' impact for a constraint solver to automate the generation of effective test suites. We applied this approach to a few real-world case studies. The results showed that the new test generation approach could generate effective test cases and detect new faults.