Java-HCT: An approach to increase MC/DC using Hybrid Concolic Testing for Java programs

Abstract

Modified Condition / Decision Coverage (MC/DC) is the second strongest coverage criterion in white-box testing. According to DO178C/RTCA criterion it is mandatory to achieve Level A certification for MC/DC. Concolic testing is the combination of Concrete and Symbolic execution. It is a systematic technique that performs symbolic execution but uses randomly-generated test inputs to initialize the search and to allow the tool to execute programs when symbolic execution fails. In this paper, we extend concolic testing by computing MC/DC using the automatically generated test cases. On the other hand Feedback-Directed Random Test Generation builds inputs incrementally by randomly selecting a method call to apply and find arguments from among previously-constructed inputs. As soon as the input is built, it is executed and checked against a set of contracts and filters. In our proposed work, we combine feedback-directed test cases generation with concolic testing to form Java-Hybrid Concolic Testing (Java-HCT). Java-HCT generates more number of test cases since it combines the features of both Feedback- Directed Random Test and Concolic Testing. Hence, through Java-HCT, we achieve high MC/DC. Combinations of approaches represent different tradeoffs of completeness and scalability. We develop Java-HCT using RANDOOP, jCUTE, and COPECA. Combination of RANDOOP and jCUTE creates more test cases. COPECA is used to measure MC/DC% using the generated test cases. Experimental study shows that Java-HCT produces better MC/DC% than individual testing techniques(feedback-directed random testing and concolic testing). We have improved MC/DC by ×1.62 and by ×1.26 for feedback-directed random testing and concolic testing respectively.