Program-operators to improve test data generation search

There has recently been a great deal of interest in search based test data generation, with many local and global search algorithms being proposed. In this paper, the program operations, in the form of the program-specific operations used to increase the performance in the generation of test data. The efficacy and performance of the proposed testing approach is assessed and validated using a variety of sample programs, and the empirical investigation is shown to give more than eightfold increase in performance.

[1]  P. D. Coward,et al.  Symbolic execution and testing , 1990 .

[2]  Mohammad Alshraideh,et al.  Search‐based software test data generation for string data using program‐specific search operators , 2006, Softw. Test. Verification Reliab..

[3]  Gary McGraw,et al.  Generating Software Test Data by Evolution , 2001, IEEE Trans. Software Eng..

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

[5]  Bryan F. Jones,et al.  Automatic structural testing using genetic algorithms , 1996, Softw. Eng. J..

[6]  Kumar Saurabh.  Software development and testing: a system dynamics simulation and modeling approach , 2010, ICSE 2010.

[7]  Nikos E. Mastorakis,et al.  Cost effective software test metrics , 2008 .

[8]  Basel A. Mahafzah,et al.  Using program data-state scarcity to guide automatic test data generation , 2010, Software Quality Journal.

[9]  James C. King,et al.  Symbolic execution and program testing , 1976, CACM.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Simeon C. Ntafos,et al.  An Evaluation of Random Testing , 1984, IEEE Transactions on Software Engineering.

[12]  Boris Beizer,et al.  Software testing techniques (2. ed.) , 1990 .

[13]  Edmund K. Burke,et al.  A Genetic Algorithm for University Timetabling , 1994 .

[14]  Leonardo Bottaci,et al.  Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data , 2003, GECCO.

[15]  Gary B. Fogel,et al.  A Clustal alignment improver using evolutionary algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[16]  Twittie Senivongse,et al.  A basis path testing framework for WS-BPEL composite services , 2008, ICSE 2008.

[17]  FerranteJeanne,et al.  The program dependence graph and its use in optimization , 1987 .

[18]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[19]  James C. King,et al.  A new approach to program testing , 1974, Programming Methodology.

[20]  John A. Clark,et al.  Automated program flaw finding using simulated annealing , 1998, ISSTA '98.

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  Joe D. Warren,et al.  The program dependence graph and its use in optimization , 1987, TOPL.

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

[24]  Padraig Cunningham,et al.  Using Case Retrieval to Seed Genetic Algorithms , 2001, Int. J. Comput. Intell. Appl..

[25]  Wouter Boomsma.  Using adaptive operator scheduling on problem domains with an operator manifold: applications to the travelling salesman problem , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[26]  L. Darrell Whitley,et al.  An overview of evolutionary algorithms: practical issues and common pitfalls , 2001, Inf. Softw. Technol..

[27]  Bryant A. Julstrom,et al.  Seeding the population: improved performance in a genetic algorithm for the rectilinear Steiner problem , 1993, SAC '94.

[28]  Roy P. Pargas,et al.  Test‐data generation using genetic algorithms , 1999 .