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Reseach Article

Software Next Release Planning Approach through Exact Optimization

by FabrÌcio G. Freitas, Daniel P. Coutinho, Jerffeson T. Souza
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 8
Year of Publication: 2011
Authors: FabrÌcio G. Freitas, Daniel P. Coutinho, Jerffeson T. Souza
10.5120/2607-3636

FabrÌcio G. Freitas, Daniel P. Coutinho, Jerffeson T. Souza . Software Next Release Planning Approach through Exact Optimization. International Journal of Computer Applications. 22, 8 ( May 2011), 1-8. DOI=10.5120/2607-3636

@article{ 10.5120/2607-3636,
author = { FabrÌcio G. Freitas, Daniel P. Coutinho, Jerffeson T. Souza },
title = { Software Next Release Planning Approach through Exact Optimization },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 8 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number8/2607-3636/ },
doi = { 10.5120/2607-3636 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:50.170232+05:30
%A FabrÌcio G. Freitas
%A Daniel P. Coutinho
%A Jerffeson T. Souza
%T Software Next Release Planning Approach through Exact Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 8
%P 1-8
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Software Requirements phase has notable importance, since it is responsible for the definition of the system itself. Several customers indicate which functionalities they want to be present in the software. However, constraints, such as budget, make it impossible to implement all desired requirements at once. One activity in this context is the release planning. The selection of which requirements should be implemented to the next release is necessary. In literature, metaheuristics have been employed to solve this problem. The objective of this work is to propose the use of exact optimization techniques in the problem, with the advantage that the resolution through these techniques ensures the best solutions. The results in several experiments show the validity of such application, in comparison with the metaheuristics approach.

References
  1. T. Dyba, “An empirical investigation of the key factors for success in software process improvement”, IEEE Transactions on Software Engineering, May 2005, pp. 410-424.
  2. Fuggetta, A. 2000. Software process: a roadmap, The Future of Software Engineering, A. Finkelstein (ed).
  3. A. Bagnall, V. Rayward-Smith, L. Whittley, “The next release problem”, Information and Software Technology, 2001, pp. 883–890.
  4. Yoo, S. and Harman, M. 2007. Pareto Efficient Multi-Objective Test Case Selection. In Proceedings of the International Symposium on Software Testing and Analysis, pp. 140-150.
  5. Zhang, Y., Harman, M. and Mansouri, A. 2007. The multi-objective next release problem. In Proceedings of the 9th annual conference on Genetic and evolutionary computation (GECCO '07). ACM, pp. 1129-1137.
  6. Harman, M. 2006. Search Based Software Engineering, In Workshop on Computational Science in Software Engineering.
  7. J. Clarke, et al. “Reformulating software engineering as a search problem”, IEE Proceedings Software, Vol. 150, No. 3, June 2003, pp. 161-175.
  8. W. Miller and D. Spooner, “Automatic Generation of Floating-Point Test Data”, IEEE Transactions on Software Engineering, Vol. 2(3), pp. 223-226, 1976.
  9. M. Harman, and B.F. Jones, “Search-based software engineering”, Information and Software Technology, 2001, pp. 833-839.
  10. Glover, F. 1986. Future paths for integer programming and links to artificial intelligence, Computer Operational Research 13, pp. 533-549.
  11. S. Kirkpatrick, D. C. Gellat, M. P. Vecchi, “Optimizations by simulated annealing”, Science v. 220, pp. 671-680, 1983.
  12. Holland, J., 1975. Adaptation in Natural and Artificial Systems.
  13. Dantzig, G. B. Inductive proof of the simplex method. IBM J. Res. Development, 505-506, 1960.
  14. Carmo, R. A. F, Campos. G. A. L., Souza, J. T. Easymeta: a framework of metaheuristics for mono-Objective optimization problems. In Proceedings of the XL Simpósio Brasileiro de Pesquisa Operacional (SBPO´2008), 2008.
  15. Land, A. H., Doig, A. G.; An automatic method of solving discrete programming problems. Econometrica 28(3): 497-520, July 1960.
  16. Souza, J., Maia, C., Freitas, F. and Coutinho D. 2010. The Human Competitiveness of Search Based Software Engineering. In Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10), pp. 143-152.
Index Terms

Computer Science
Information Sciences

Keywords

Search Based Software Engineering Next Release Planning Software Requirements