Advertisement

Automated Testcase Generation and Prioritization Using GA and FRBS

  • Muhammad Azam
  • Atta-ur-Rahman
  • Kiran Sultan
  • Sujata DashEmail author
  • Sundas Naqeeb Khan
  • Muhammad Aftab Alam Khan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

Software Quality Assurance (SQA) is a process in which the quality of software is assured by adequate software testing techniques that mainly comprise of verification and validation of the software. Software testing is the process of assessing the features of a software item and evaluating it to detect differences between given input and expected output. This process is done during the development process just prior to deployment. The SQA process is usually a manual process due to the diverse and versatile nature of the software products. That means a technique devised to test one type of software may not work that efficiently while testing another kind of software etc. Moreover, it is a time consuming process; according to a survey it consumes almost half of the total development cost and around two third of the total development time. To address the above-mentioned issues, in this research an intelligent toolkit for automated SQA is proposed and compared them with the existing famous tools like Selenium. This research focuses on automated test case/test data generation and prioritization of test cases. For this purpose, Genetic Algorithm is investigated for automatic test case generation and a fuzzy based system is proposed for test case prioritization.

Keywords

SQA Testcase generation Testcase prioritization Automated testing GA FRBS 

References

  1. 1.
    Singh, A., Garg, N., Saini, T.: A hybrid approach of genetic algorithm and particle swarm technique to software test. Int. J. Innov. Eng. Technol. (IJIET), 3(4), 208–214 (2014)Google Scholar
  2. 2.
    Arora, D., Baghel, A.S.: Application of genetic algorithm and particle swarm optimization in software testing. IOSR J. Comput. Eng. (IOSR-JCE) 17(1), 75–78, Ver. II (2015), e-ISSN: 2278-0661, p-ISSN: 2278-8727Google Scholar
  3. 3.
    Sharma, C., Sabharwal, S., Sibal, R.: Applying genetic algorithm for prioritization of test case scenarios. IJCSI Int. J. Comput. Sci. Issues 8(3), 2, 433–444 (2011), Derived from UML DiagramsGoogle Scholar
  4. 4.
    Brar, K.M., Garg, S.: Survey on automated test data generation. Int. J. Comput. Appl. (0975–8887) 108(15), 1–4 (2014)Google Scholar
  5. 5.
    Mateen, A., Nazir, M., Awan, S.A.: Optimization of test case generation using genetic algorithm (GA). Int. J. Comput. Appl. (0975–8887) 151(7), 6–14 (2016)Google Scholar
  6. 6.
    Atta-ur-Rahman, Qureshi, I.M., Malik, A.N., Naseem, M.T.: QoS and rate enhancement in DVB-S2 using fuzzy rule base system. J. Intell. Fuzzy Syst. (JIFS) 30(1), 801–810 (2016)Google Scholar
  7. 7.
    Atta-ur-Rahman, Qureshi, I.M., Malik, A.N., Naseem, M.T.: Dynamic resource allocation for OFDM systems using differential evolution and fuzzy rule base system. J. Intell. Fuzzy Syst. (JIFS) 26(4), 2035–2046 (2014).  https://doi.org/10.3233/ifs-130880
  8. 8.
    Atta-ur-Rahman, Qureshi, I.M., Malik, A.N.: Adaptive resource allocation in OFDM systems using GA and fuzzy rule base system. World Appl. Sci. J. (WASJ) 18(6), 836–844 (2012)Google Scholar
  9. 9.
    Atta-ur-Rahman, Qureshi, I.M., Malik, A.N.: A fuzzy rule base assisted adaptive coding and modulation scheme for OFDM systems. J. Basic Appl. Sci. Res. 2(5), 4843–4853 (2012)Google Scholar
  10. 10.
    Abhishek, S., Chandna, S., Bansal, A.: Optimization of test cases using genetic algorithm 1 (2012)Google Scholar
  11. 11.
    Deepa, C., Sehgal, A.: Automated test data generation using soft computing techniques. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 4(4), 1165–1169 (2015)Google Scholar
  12. 12.
    Sathi, N., Rani, S., Singh, P.: Ants optimization for minimal test case selection and prioritization as to reduce the cost of regression testing. Int. J. Comput. Appl. (0975–8887) 100(17), 48–54 (2014)Google Scholar
  13. 13.
    Kire, K., Malhotra, N.: Software testing using intelligent technique. Int. J. Comput. Appl. (0975–8887) 90(19), 22–25 (2014)Google Scholar
  14. 14.
    Singla, S., Kumar, R., Kummar, D.: Natural computing for automatic test data generation approach using spanning tree concepts. Procedia Comput. Sci. 85, 929–939 (2016)Google Scholar
  15. 15.
    Badanahatti, S., Murthy, Y.S.S.R.: Optimal test case prioritization in cloud based regression testing with aid of KFCM. Int. J. Intell. Eng. Syst. 10(2), 96–106 (2017)Google Scholar
  16. 16.
    Panda, M., Dah, S.: Automatic test suite generation for object oriented programs using metaheuristic Cuckoo search algorithm. Int. J. Control Theory Appl. 10(18), 71–79 (2017)Google Scholar
  17. 17.
    Panda, M., Dash, S.: Automatic test data generation using bio-inspired algorithms: a travelogue. In: Dash, S., Tripathy, B.K., Rehman, A. (eds.) Handbook of Research on the Modeling. Analysis and Application on Nature-Inspired Metaheuristic Algorithms, pp. 140–159. IGI-Global, USA (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Muhammad Azam
    • 1
  • Atta-ur-Rahman
    • 2
  • Kiran Sultan
    • 3
  • Sujata Dash
    • 4
    Email author
  • Sundas Naqeeb Khan
    • 5
  • Muhammad Aftab Alam Khan
    • 2
  1. 1.Barani Institute of Information Technology (BIIT), PMAS-AA UniversityRawalpindiPakistan
  2. 2.College of Computer Science and Information Technology, Department of Computer ScienceImam Abdulrahman Bin Faisal UniversityDammamKingdom of Saudi Arabia
  3. 3.Department of CITJCC, AbdulAziz UniversityKing JeddahKingdom of Saudi Arabia
  4. 4.Department of Computer ScienceNorth Orissa UniversityBaripadaIndia
  5. 5.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia

Personalised recommendations