Software Testing using Genetic Algorithm

Testing is a process used to identify the correctness, completeness and quality of developed computer software. Testing, apart from finding errors, is also used to test performance, safety, faulttolerance or security. Testing is the most important quality assurance measure for software. Testing is time consuming and laborious process. Therefore, techniques to automatic test data generation would be useful to reduce the cost and time. Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that’s why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web based type of software systems. The objective of this paper is to present an algorithm by applying a Genetic Algorithm Technique, for generation of optimal and minimal test sequences for behaviour specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage.

[1]  A. Jefferson Offutt,et al.  Using compiler optimization techniques to detect equivalent mutants , 1994, Softw. Test. Verification Reliab..

[2]  Mohammad Zulkernine,et al.  MUSIC: Mutation-based SQL Injection Vulnerability Checking , 2008, 2008 The Eighth International Conference on Quality Software.

[3]  Moheb R. Girgis,et al.  Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm , 2005, J. Univers. Comput. Sci..

[4]  Roy P. Pargas,et al.  Test‐data generation using genetic algorithms , 1999, Softw. Test. Verification Reliab..

[5]  John J. Grefenstette,et al.  Test and evaluation by genetic algorithms , 1993, IEEE Expert.

[6]  Antonia Bertolino,et al.  Software Testing Research: Achievements, Challenges, Dreams , 2007, Future of Software Engineering (FOSE '07).

[7]  Jeff Tian.  Measurement and continuous improvement of software reliability throughout software life-cycle , 1999, J. Syst. Softw..

[8]  A. Jefferson Offutt,et al.  Applying Mutation Testing to Web Applications , 2010, 2010 Third International Conference on Software Testing, Verification, and Validation Workshops.

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[10]  John A. Clark,et al.  Class Mutation : Mutation Testing for Object-Oriented Programs , 2000 .

[11]  Lech Madeyski,et al.  Judy - a mutation testing tool for Java , 2010, IET Softw..

[12]  Michael Winikoff,et al.  Mutation operators for cognitive agent programs , 2013, AAMAS.

[13]  Hossain Shahriar,et al.  Mutation-based testing of buffer overflows, SQL injections, and format string bugs , 2008 .

[14]  YongRaeKwon,et al.  Statistical Investigation on Class Mutation Operators , 2009 .

[15]  Sanghamitra Bandyopadhyay,et al.  Classification and learning using genetic algorithms - applications in bioinformatics and web intelligence , 2007, Natural computing series.

[16]  Yong Chen,et al.  Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data Generation , 2009, 2009 Fifth International Conference on Natural Computation.

[17]  Bogdan Korel,et al.  Dynamic method for software test data generation , 1992, Softw. Test. Verification Reliab..

[18]  SHAILENDRA MISHRA,et al.  Mutant Generation for Aspect Oriented Programs , 2011 .

[19]  Francesca Lonetti,et al.  X-MuT: A Tool for the Generation of XSLT Mutants , 2010, 2010 Seventh International Conference on the Quality of Information and Communications Technology.

[20]  Mark Harman,et al.  An Analysis and Survey of the Development of Mutation Testing , 2011, IEEE Transactions on Software Engineering.

[21]  Laura Painton,et al.  Genetic algorithms in optimization of system reliability. , 1995 .

[22]  Phil McMinn,et al.  Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..