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

Abstract

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 randomlygenerated 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 FeedbackDirected 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 FeedbackDirected 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.

Scalable Path Search for Automated Test Case Generation

“…Hybrid Concolic Testing [28,29] extends the CUTE work, where random search and bounded depth-first search are combined. A bounded depth-first search algorithm attempts to explore all neighborhoods of the current paths exhaustively, while a random search algorithm has the ability of reaching deep program branches quickly.…”

HiRSA: computing hit ratio for SOA-based applications through Tcases

“…of no. name services classes invocations messages events assignments 1 Quadratic 4 3 6 6 5 3 2 Cramer 7 3 10 9 9 8 3 Geometry 5 4 7 12 8 4 4 Calculator 5 0 5 8 6 6 5 Producer Consumer 4 3 7 8 6 3 6 Healthcare 10 4 13 17 13 11 7 CAssume 4 2 6 6 5 3 8 Bug Godboley et al (2013aGodboley et al ( , 2013bGodboley et al ( , 2015Godboley et al ( ,2016aGodboley et al ( , 2016bGodboley et al ( , 2016cGodboley et al ( , 2016d Proposed approach Black-box testing for SOA HiRSA Ma et al (2008) 4Lubke et al (2009) 5Karimi and Nasser (2009) × 6…”

“…Motlagh (2013) 7Yotyawilai and Taratip (2014) 8Kumar and Singh (2015) 9Kumar (2015) 10Nonchot and Taratip (2016) 11 Godboley (2013), Godboley and Mohapatra × (2013) and Godboley et al (2013aGodboley et al ( , 2013bGodboley et al ( , 2015Godboley et al ( , 2016aGodboley et al ( , 2016bGodboley et al ( , 2016cGodboley et al ( , 2016d Proposed approach Kumar (2015) The proposed algorithms give coverage for xml schema (XSD) elements of WSDL. In our proposed approach, we also compute the hit ratio percentage for SOA-based applications.…”

“…In our approach HiRSA, we have used an open source tool to generate the black box test cases and measured the hit ratio coverage percentage for the given system. Godboley (2013), Godboley and Mohapatra (2013) and Godboley et al (2013aGodboley et al ( , 2013bGodboley et al ( , 2015Godboley et al ( , 2016aGodboley et al ( , 2016bGodboley et al ( , 2016cGodboley et al ( , 2016d have presented automated approaches for test data generation, that help to achieve an increase in MC/DC coverage of a program under test. Several types of transformation techniques are used for transforming the input program.…”