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Random Based Unit Testing For Automated Test Report Generation

M. Chandran


Random based Testing is the practice of using randomization for some aspects of test input data selection. Random based unit testing is unit testing where there is some randomization in the selection of the target method call sequence Software testing involves running a piece of software on selected input data and checking the outputs for correctness. The optimization techniques for GA tools Using feature subset selection techniques; It is able to achieve high coverage of complex, real-world Java units, while retaining the most desirable feature of randomized testing, the ability to generate many new high-coverage test cases quickly.


Software Testing, Genetic Algorithms, Test Automation, Unit Testing.

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Arun K.Pujari, "Data mining Techniques‖ University Press, First Edition, 2001.

Beizer, Boris, "Software Testing Techniques", Second Edition. New York, Van Nostrand Reinhold, 1990

Herbert Schildt ―Java™ 2: The Complete Reference, Fifth Edition‖, Tata McGRAW-Hill Edition.

Jiawei Han and Micheline Kamber, ―Data Mining: Concepts and Techniques‖, Kaufmann Publishers, 2001.

Richard E. Fairley, "Software engineering concepts",Tata McGraw- Hill, 2000.

Roger S. Pressman, "Software Engineering", TataMcGraw-Hill. 2000.

Ted Husted and Vincent Massol ―JUnit in Action‖ Publisher: Manning Publications 2003Journals

A. Groce, G.J. Holzmann, and R. Joshi, ―Randomized Differential Testing as a Prelude to Formal Verification,‖ Proc. 29th Int’l Conf. Software Eng., pp. 621-631, May 2007

C. Csallner and Y. Smaragdakis, ―JCrasher: An Automatic Robustness Tester for Java,‖ Software Practice and Experience, vol. 34, no. 11, pp. 1025- 1050, 2004.

D. Owen and T. Menzies, ―Lurch: A Lightweight Alternative to Model Checking,‖ Proc. 15th Int’l Conf. Software Eng. and Knowledge Eng., pp. 158-165, July 2003.

G. Antoniol, M.D. Penta and M. Harman, ―Search-Based Techniques Applied to Optimization of Project Planning for a Massive Maintenance Project,‖ Proc.IEEE Int’l Conf. Software Maintenance, pp. 240-249, 2005.

I. Ciupa, A. Leitner, M. Oriol, and B. Meyer, ―Artoo: Adaptive Random Testing for Object-Oriented Software,‖ Proc. 30th ACM/IEEE Int’l Conf. Software Eng., pp. 71-80, May 2008.

J. Andrews and T. Menzies, ―On the Value of Combining Feature Subset Selection with Genetic Algorithms: Faster Learning of Coverage Models,‖ Proc. Fifth Int’l Conf. Predictor Models in SoftwareEng.,ttp://, 2009.

J. Andrews, F. Li, and T. Menzies, ―Nighthawk: A Two-Level Genetic-Random Unit Test Data Generator,‖Proc. 22nd IEEE/ACMInt’lConf.AutomatedSoftwareEng.,, 2007.

J.H. Andrews, S. Haldar, Y. Lei, and C.H.F. Li, ―Tool Support for Randomized Unit Testing,‖ Proc. First Int’l Workshop Randomized Testing, pp. 36- 45, July 2006.

James H. Andrews, Tim Menzies and Felix C.H. Li, ―Genetic Algorithms for Randomized Unit Testing‖ IEEE Transactions on Software Engineering, Vol. 37, No. 1, January/February 2011.

Mark Harman and Phil McMinn, ―A Theoretical and Empirical Study of Search-Based Testing: Local, Global and Hybrid Search‖ IEEE Transactions on Software Engineering, vol. 36, no. 2, 2010.


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