Praveen Ranjan Srivastava
ABSTRACT
Structural testing is one of the most widely used testing paradigms to test software. The aim of this paper is to present a simple and efficient algorithm that can automatically generate all possible paths in a Control Flow Graph for structural testing. Pheromone releasing behavior of ants is used in this algorithm for extracting optimal paths. This algorithm generates paths equal to the cyclomatic complexity.
- I. Sommerville. 2009.Software Engineering, 8th Edition, Pearson Edition, India.Google Scholar
- R. S. Pressman. 2004. Software Engineering: A Practitioner's Approach, 6th Edition, McGraw-Hill, USA. Google ScholarDigital Library
- A. P. Mathur. 2007. Foundations of Software Testing, Pearson Education, India. Google ScholarDigital Library
- R. Sedgewick. 2003. Algorithms in Java, 3rd Edition, Part 5: Graph Algorithms, Addison Wesley, USA. Google ScholarDigital Library
- V. Bhattacherjee, D. Suri and P. Mahanti.2005. Application of Regular Matrix Theory to Software Testing, European Journal of Scientific Research, Vol. 12, No.1, 60-70.DOI= 10.1504/IJICT.2007.013274Google Scholar
- L. C. Briand. 2002. On the Many Ways Software Engineering Can Benefit from Knowledge Engineering, Proceedings of 14th International Conference on Software Engineering and Knowledge Engineering (SEKE), Italy, 3--6. doi>10.1145/568760.568762 Google ScholarDigital Library
- W. Pedrycz and J. F. Peters.1998. Computational Intelligence in Software Engineering, World Scientific Publishers. Google ScholarDigital Library
- P. McMinn.2004. Search-Based Software Test Data Generation: A Survey, Software Testing, Verification and Reliability, Vol. 14, No. 3, 212--223. doi>10.1002/stvr.v14:2 Google ScholarDigital Library
- M. Harman.2007. The Current State and Future of Search Based Software Engineering, International Conference on Software Engineering, Future of Software Engineering, IEEE Computer Society press, Washington, DC, USA, 342--357. doi>10.1109/FOSE.2007.29 Google ScholarDigital Library
- P. R. Srivastava, V. Ramachandran, M. Kumar, G. Talukder, V. Tiwari, P. Sharma. 2008 Generation of Test Data using Meta Heuristics Approach, IEEE TENCON 2008, India.Google ScholarCross Ref
- M. Dorigo and T. Stutzle.2005. Ant Colony Optimization, MIT Press, USA. Google ScholarDigital Library
- K. Ayari, S. Bouktif and G. Antoniol.2007.Automatic Mutation Test Input Data Generation via Ant Colony, Genetic and Evolutionary Computation Conference, London, UK, 1074--1081. doi>10.1145/1276958.1277172 Google ScholarDigital Library
- P. R. Srivastava, K. M. Baby and G. Raghurama. 2009. An Approach of Optimal Path Generation using Ant Colony Optimization, IEEE TENCON, November 2009, Singapore, ISBN 978-1-4244-4546-2.Google Scholar
- S. D. Shtovb. 2005. Ant Algorithms: Theory and Applications, Programming and Computing Software, Vol. 31, Issue 4, pp. 167--178, 2005, Plenum Press, USA. Google ScholarDigital Library
Index Terms
- Structured testing using ant colony optimization
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