CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Search-based Software Requirements Selection: A Case Study

by A. Charan Kumari, K. Srinivas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 21
Year of Publication: 2013
Authors: A. Charan Kumari, K. Srinivas
10.5120/10760-5715

A. Charan Kumari, K. Srinivas . Search-based Software Requirements Selection: A Case Study. International Journal of Computer Applications. 64, 21 ( February 2013), 28-34. DOI=10.5120/10760-5715

@article{ 10.5120/10760-5715,
author = { A. Charan Kumari, K. Srinivas },
title = { Search-based Software Requirements Selection: A Case Study },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 21 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number21/10760-5715/ },
doi = { 10.5120/10760-5715 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:15.025432+05:30
%A A. Charan Kumari
%A K. Srinivas
%T Search-based Software Requirements Selection: A Case Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 21
%P 28-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a Multi-objective Quantum-inspired Hybrid Differential Evolution (MQHDE) for the solution of software requirements selection problem and its application on a real-world project. As the customer requirements change from time to time, often software products are developed in an iterative or incremental manner so as to deal with these changing requirements. The problem is to identify a set of requirements to be included in the next release of the software product, by minimizing the cost and maximizing the customer satisfaction. This problem is referred to as Multi-objective Next Release Problem (MONRP) in the jargon of Search-based Software Engineering (SBSE). The solution to the problem of MONRP has been studied by researchers using different metaheuristic search techniques. The efficiency of the proposed MQHDE is tested on a real-world application and the results are compared against the state-of-the-art multi-objective evolutionary algorithm NSGA-II, and found that the performance of MQHDE is promising and therefore can be used with confidence for the solution of real-world instances of MONRP.

References
  1. Mark Harman, Bryan F. Jones. 2001. "Search-based software engineering", Information and software Technology, 833-839.
  2. A. Bagnall, V. Rayward-Smith, and I. Whittley. 2001. "The next release problem", Information and software technology, 883-890.
  3. J. Aguilar-Ruiz, I. Ramos, J. C. Riquelme, and M. Toro. 2001. "An evolutionary approach to estimating software development projects", Information and Software Technology, 875–882.
  4. R. Lutz. 2001. "Evolving good hierarchical decompositions of complex systems", Journal of Systems Architecture, 613–634.
  5. P. McMinn, M. Harman, D. Binkley, and P. Tonella. 2006. "The species per path approach to search-based test data generation", International Symposium on Software Testing and Analysis, 13–24.
  6. R M Hierons, Z Li, M Harman. 2008. "Search Algorithms for Regression Test Case Prioritization", IEEE Transactions on Software Engineering, 225-237.
  7. M. O'Keeffe and M. O'Cinneide. 2006. "Search-based software maintenance", Conference on Software Maintenance and Reengineering ,249–260.
  8. D. Greer and G. Ruhe. 2004. "Software release planning : an evolutionary and iterative approach", Information & Software Technology, 243-253.
  9. Zhang . Y, M. Harman, and A. S. Mansouri, 2007. "The Multi-Objective Next Release Problem", GECCO: proceedings of the 9th annual conference on genetic and evolutionary computation, 1129–1136.
  10. Durillo, J. , J. , Y. Zhang, E. Alba, A. J. Nebro, 2009. "A study of the multi-objective next release problem", SBSE: proceedings of the 2009 1st international symposium on search based software engineering, 49–58.
  11. Finkelstein A, Harman M, Mansouri SA, Ren J, Zhang Y. 2009. "A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making", Requirement Eng , 231–245.
  12. M. Harman, J. Krinke, J. Ren, and S. Yoo. 2009. "Search Based Data Sensitivity Analysis Applied to Requirement Engineering", Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 1681–1688.
  13. Yuanyuan Zhang, Enrique Alba, Juan J. Durillo, Sigrid Eldh and Mark Harman. 2010 . "Today/Future importance analysis". Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation.
  14. A. Charan Kumari, K. Srinivas and M. P. Gupta. 2012. "Software Requirements Selection using Quantum-inspired Elitist Multi-objective Evolutionary Algorithm". Proceedings of the IEEE-International Conference on Advances in Engineering, Science and Management, 782-787.
  15. A. Charan Kumari, K. Srinivas and M. P. Gupta, "Software Requirements Optimization Using Multi-Objective Quantum-Inspired Hybrid Differential Evolution", EVOLVE – A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II Advances in Intelligent Systems and Computing, 107-120.
  16. K. Price and R. Storn, 1995. "Differential Evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces", Technical Report, International Computer Science Institute, Berkley.
  17. Han, K. H. and J. H. Kim, 2002. "Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization", IEEE Transactions on Evolutionary Computation, 580-593.
  18. Deb, K. , A. Pratap, S. Agarwal, and T. Meyarivan. , 2002. "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 182-197.
  19. Su, H. and Yang, Y. 2008. "Quantum-inspired differential evolution for binary optimization", The 4-th international conference on natural computation, 341–346.
  20. Deb, K. . 2001. "Multi-Objective Optimization using Evolutionary Algorithms". Wiley Chichester, UK.
Index Terms

Computer Science
Information Sciences

Keywords

Search-based software engineering Multi-objective optimization Multi-objective next release problem