Code Coverage Aware Test Generation Using Constraint Solver
This work addresses the need for more efficient test generation in software testing, though it appears incremental as it builds on existing code coverage techniques.
The paper tackles the problem of generating effective test cases by introducing a Code Coverage-based Test Case Generation (CCTG) concept that uses code coverage data to guide a constraint solver, resulting in the detection of new faults in real-world case studies.
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.