Dealing with inheritance in OO evolutionary testing

ABSTRACT

Most of the software developed in the world follows the object-oriented (OO) paradigm. However, the existing work on evolutionary testing is mainly targeted to procedural languages. All this work can be used with small changes on OO programs, but object orientation introduces new features that are not present in procedural languages. Some important issues are polymorphism and inheritance. In this paper we want to make a contribution to the inheritance field by proposing some approaches that use the information of the class hierarchy for helping test case generators to better guide the search. To the best of our knowledge, no work exists using this information to propose test cases. In this work we define a branch distance for logical expressions containing the instanceof operator in Java programs. In addition to the distance measure, we propose two mutation operators based on the distance. We study the behaviour of the mutation operators on a benchmark set composed of nine OO programs. The results show that the information collected from the class hierarchy helps in the search for test cases.

REFERENCES

• 1: Andrea Arcuri , Xin Yao, Search based software testing of object-oriented containers, Information Sciences: an International Journal, v.178 n.15, p.3075-3095, August, 2008 [doi>10.1016/j.ins.2007.11.024]
• 2: Thomas Back , David B. Fogel , Zbigniew Michalewicz, Handbook of Evolutionary Computation, IOP Publishing Ltd., Bristol, UK, 1997
• 3: André Baresel , David Binkley , Mark Harman , Bogdan Korel, Evolutionary testing in the presence of loop-assigned flags: a testability transformation approach, Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis, July 11-14, 2004, Boston, Massachusetts, USA
• 4: Boris Beizer, Software testing techniques (2nd ed.), Van Nostrand Reinhold Co., New York, NY, 1990
• 5: Eugenia Díaz, Raquel Blanco, and Javier Tuya. Tabu search for automated loop coverage in software testing. In Proceedings of the International Conference on Knowledge Engineering and Decision Support (ICKEDS), pages 229--234, Porto, 2006.
• 6: James Gosling , Bill Joy , Guy Steele , Gilad Bracha, Java(TM) Language Specification, The (3rd Edition) (Java (Addison-Wesley)), Addison-Wesley Professional, 2005
• 7: X. Liu, B. Wang, and H. Liu. Evolutionary search in the context of object oriented programs. In Proceedings of the Sixth Metaheuristics International Conference, Vienna, August 2005.
• 8: Phil McMinn, Search-based software test data generation: a survey: Research Articles, Software Testing, Verification & Reliability, v.14 n.2, p.105-156, June 2004 [doi>10.1002/stvr.v14:2]
• 9: Phil McMinn, David Binkley, and Mark Harman. Testability
• 10: transformation for efficient automated test data search in the presence of nesting. In Proceedings of the Third UK Software Testing Workshop (UKTest 2005), pages 165--182, 2005.
• 11: C. C. Michael , G. McGraw , M. A. Schatz, Generating Software Test Data by Evolution, IEEE Transactions on Software Engineering, v.27 n.12, p.1085-1110, December 2001 [doi>10.1109/32.988709]
• 12: W. Miller , D. L. Spooner, Automatic Generation of Floating-Point Test Data, IEEE Transactions on Software Engineering, v.2 n.3, p.223-226, May 1976 [doi>10.1109/TSE.1976.233818]
• 13: Paolo Tonella, Evolutionary testing of classes, ACM SIGSOFT Software Engineering Notes, v.29 n.4, July 2004
• 14: N. Tracey , J. Clark , K. Mander , J. McDermid, Automated test-data generation for exception conditions, Software—Practice & Experience, v.30 n.1, p.61-79, Jan. 2000 [doi>10.1002/(SICI)1097-024X(200001)30:1<61::AID-SPE292>3.0.CO;2-9]
• 15: Stefan Wappler , Joachim Wegener, Evolutionary unit testing of object-oriented software using strongly-typed genetic programming, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08-12, 2006, Seattle, Washington, USA [doi>10.1145/1143997.1144317]
• 16: Stefan Wappler , Joachim Wegener, Evolutionary unit testing of object-oriented software using strongly-typed genetic programming, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08-12, 2006, Seattle, Washington, USA [doi>10.1145/1143997.1144317]
• 17: Joachim Wegener, Andre Baresel, and Harmen Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43(14):841--854, December 2001.
• 18: Yuan Zhan , John A. Clark, The state problem for test generation in Simulink, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08-12, 2006, Seattle, Washington, USA [doi>10.1145/1143997.1144319]

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.5 Testing and Debugging
          Subjects: Testing tools (e.g., data generators, coverage testing)

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms, Experimentation

Keywords:
evolutionary algorithm, instanceof, object-oriented, oo evolutionary testing, search based software engineering, software testing