International Journal of Information Technology

, Volume 11, Issue 4, pp 633–637 | Cite as

Total fault exposing potential based test case prioritization using genetic algorithm

  • Deepti Bala MishraEmail author
  • Namita Panda
  • Rajashree Mishra
  • Arup Abhinna Acharya
Original Research


The quality of modified software depends on selecting an efficient technique of Regression testing, in which the test cases are selected based on how fast the mutants are detected. The process of executing the most beneficial test case is called as test case prioritization. The redundant test cases which detect the same mutants can be eliminated for minimization, which results in reducing the cost and time of regression testing. This paper presents a code based prioritization technique in which total statement coverage, total fault exposing potential award and total mutant coverage is taken as prioritization factors to prioritize test cases for software under test. In the proposed method, Genetic Algorithm is used for prioritization and further test cases are minimized based on total mutant coverage. The effectiveness of the prioritized order is measured by Average Percentage of Statement Coverage metric.


Software test case Genetic algorithm Test case prioritization Fault exposing potential (FEP) Statement coverage APSC 


  1. 1.
    Khatibsyarbini M, Isa MA, Jawawi DN, Tumeng R (2017) Test case prioritization approaches in regression testing: a systematic literature review. Inf Softw Technol 93:74–93CrossRefGoogle Scholar
  2. 2.
    Karamitsos I, Apostolopoulos C (2018) Optical trends in data centers architectures for smart cities. Int J Inf Technol 10(1):3–9Google Scholar
  3. 3.
    Raju S, Uma GV (2012) Factors oriented test case prioritization technique in regression testing using genetic algorithm. Eur J Sci Res 74(3):389–402Google Scholar
  4. 4.
    Krishnamoorthi R, Mary SSA (2009) Factor oriented requirement coverage based system test case prioritization of new and regression test cases. Inf Softw Technol 51(4):799–808CrossRefGoogle Scholar
  5. 5.
    Acharya AA, Mohapatra DP, Panda N (2010) Model based test case prioritization for testing component dependency in cbsd using uml sequence diagram. IJACSA). Int J Adv Comp Sci Appl 1(6):108–113Google Scholar
  6. 6.
    Kaur A, Goyal S (2011) A genetic algorithm for regression test case prioritization using code coverage. Int J Comput Sci Eng 3(5):1839–1847Google Scholar
  7. 7.
    Kavitha R, Sureshkumar N (2010) Test case prioritization for regression testing based on severity of fault. Int J Comput Sci Eng 2(5):1462–1466Google Scholar
  8. 8.
    Sabharwal S, Sibal R, Sharma C (2011) A genetic algorithm based approach for prioritization of test case scenarios in static testing. In: Computer and communication technology (ICCCT), 2011 2nd international conference on (pp. 304–309). IEEEGoogle Scholar
  9. 9.
    Gupta R, Kamal R, Suman U (2018) A QoS-supported approach using fault detection and tolerance for achieving reliability in dynamic orchestration of web services. Int J Inf Technol 10(1):71–81Google Scholar
  10. 10.
    Pathak N, Singh BM, Sharma G (2017) UML 2.0 based framework for the development of secure web application. Int J Inf Technol 9(1):101–109Google Scholar
  11. 11.
    Deb K (2012) Optimization for engineering design: algorithms and examples. PHI Learning Pvt. LtdGoogle Scholar
  12. 12.
    Mishra DB, Mishra R, Das KN, Acharya AA (2018) Solving sudoku puzzles using evolutionary techniques—a systematic survey. In: Soft computing: theories and applications. Springer, Singapore, pp. 791–802Google Scholar
  13. 13.
    Gedeon T (2017) Bio-inspired computing tools and applications: position paper. Int J Inf Technol 9(1):7–17Google Scholar
  14. 14.
    Jena T, Mohanty JR, (2017) GA-based customer-conscious resource allocation and task scheduling in multi-cloud computing. Arabian J Sci Eng, pp. 1–16Google Scholar
  15. 15.
    Mishra DB, Mishra R, Das KN, Acharya AA (2017) A systematic review of software testing using evolutionary techniques. In: Proceedings of sixth international conference on soft computing for problem solving. Springer, Singapore, pp. 174–184Google Scholar
  16. 16.
    Mishra DB, Bilgaiyan S, Mishra R, Acharya AA, Mishra S (2017) A review of random test case generation using genetic algorithm. Indian J Sci Technol 10(30)Google Scholar
  17. 17.
    Ahmed AA, Shaheen M, Kosba E (2012) Software testing suite prioritization using multi-criteria fitness function. In: Computer theory and applications (ICCTA), 2012 22nd international conference on (pp. 160–166). IEEEGoogle Scholar
  18. 18.
    Umbarkar AJ, Sheth PD (2015) Crossover operators in genetic algorithms: a review. ICTACT J Soft Comput 6(1):1083–1092CrossRefGoogle Scholar
  19. 19.
    Boopathi M, Sujatha R, Kumar CS, Narasimman S (2014). The mathematics of software testing using genetic algorithm. In: Reliability, Infocom technologies and optimization (ICRITO) (Trends and future directions), 2014 3rd International conference on (pp. 1–6). IEEEGoogle Scholar
  20. 20.
    Pradeepa R, Vimaldevi K (2013) Effectiveness of testcase prioritization using APFD metric: survey. In: IJCA Proceedings on international conference on research trends in computer technologies, pp. 1–4Google Scholar
  21. 21.
    Konsaard P, Ramingwong L (2015). Total coverage based regression test case prioritization using genetic algorithm. In: Electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), 2015 12th international conference on (pp. 1–6). IEEEGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  • Deepti Bala Mishra
    • 1
    Email author
  • Namita Panda
    • 1
  • Rajashree Mishra
    • 2
  • Arup Abhinna Acharya
    • 1
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia
  2. 2.School of Applied SciencesKIIT UniversityBhubaneswarIndia

Personalised recommendations