Modified ACO to maintain diversity in regression test optimization

Regression testing is unavoidable maintenance activity that is performed several times in software development life cycle. Optimization of regression test case is required to minimize the test case (which will in-turn reduce the time and cost of testing) and to find the fault in early testing activity. The two widely used regression test case optimization techniques, namely, selection and prioritization are recently found to be integrated with different metaheuristic algorithms for fruitful regression test cases. Among the various meta-heuristic algorithms, Ant colony optimization (ACO) algorithm is most popularly used. ACO will try to find the smallest path out all the test cases and it is not sufficient because it will not cover all the test cases which are needed. In this paper we have proposed a modified ant colony optimization to solve test cases in huge search space. The modified algorithm selects the best test cases that find the maximum fault in minimum time.

[1]  Phil McMinn,et al.  Search-Based Software Testing: Past, Present and Future , 2011, 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops.

[2]  Mary Jean Harrold,et al.  Recomputing Coverage Information to Assist Regression Testing , 2009, IEEE Transactions on Software Engineering.

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Atif M. Memon,et al.  A Uniform Representation of Hybrid Criteria for Regression Testing , 2013, IEEE Transactions on Software Engineering.

[5]  Cem Kaner,et al.  Foundations of Software Testing , 2013 .

[6]  Gregg Rothermel,et al.  Selecting a Cost-Effective Test Case Prioritization Technique , 2004, Software Quality Journal.

[7]  Sigrid Eldh.  Software Testing Techniques , 2007 .

[8]  Andrea De Lucia,et al.  Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms , 2015, IEEE Transactions on Software Engineering.

[9]  Bharti Suri,et al.  Implementing Ant Colony Optimization for Test Case Selection and Prioritization , 2011 .

[10]  R. Prudêncio,et al.  Search based constrained test case selection using execution effort , 2013, Expert Syst. Appl..

[11]  P. G. Sapna,et al.  An Approach for Generating Minimal Test Cases for Regression Testing , 2015 .

[12]  Gregg Rothermel,et al.  Prioritizing test cases for regression testing , 2000, ISSTA '00.

[13]  Glenford J. Myers,et al.  Art of Software Testing , 1979 .

[14]  Mark Harman,et al.  Regression Testing Minimisation, Selection and Prioritisation - A Survey , 2009 .

[15]  Gustavo Augusto Lima de Campos,et al.  Optimization in Software Testing Using Metaheuristics , 2011 .

[16]  Ajay Rana,et al.  A novel strategy for automatic test data generation using soft computing technique , 2014, Frontiers of Computer Science.

[17]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[18]  Arvinder Kaur,et al.  Test case prioritization using ant colony optimization , 2010, SOEN.

[19]  Mark Harman,et al.  Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards , 2011, SSBSE.

[20]  Prabhat Ranjan,et al.  An Overview of Test Case Optimization Using Meta-Heuristic Approach , 2016 .

[21]  Bharti Suri,et al.  Regression Test Suite Reduction using an Hybrid Technique Based on BCO And Genetic Algorithm , 2012 .

[22]  Mark Harman,et al.  Regression testing minimization, selection and prioritization: a survey , 2012, Softw. Test. Verification Reliab..