BOOMPizer: Minimization and prioritization of CONCOLIC based boosted MC/DC test cases

Abstract

Recent research evidence indicates that the powerful testing tools, even though generate test inputs automatically for coverage measures, but not up to satisfaction. These tools sometimes achieve high structural coverage, which do not guarantee to have high fault detection ability. These findings lead us to a decisive point that code coverage is merely one factor towards effective test data generation. Thus, we discuss our findings and proposed work on Modified Condition/ Decision Coverage (MC/DC) test case generation and prioritization techniques. This work aims to generate, minimize, and prioritize MC/DC test cases obtained through concolic testing process. This work presents three technical contributions. The first contribution is to propose a greedy algorithm to increase the number of effective test cases for improving MC/DC scores. The second contribution is to minimize the updated test suite size to have only the optimal number of contributing test cases towards forming MC/DC pairs. The third contribution is to prioritize these test cases by considering both their Contribution Index (CI) values and Fault Exposing Potential (FEP) values. The proposed approach is validated by experimenting on eighteen Java programs and achieved on an average ×1.67 times increase in the number of effective test cases that lead to an average increase in MC/DC score by 41.08%. We also achieved on an average 49.00% reduction rate to minimize the test suite size and finally prioritized the test cases, based on their prioritization index values.

Keywords

• Concolic testing
• Program analysis
• Test case minimization
• Test case prioritization