Abstract
The selection of the requirements to be included in the next release of a software is a complex task. Each customer has their needs, but it is usually impossible to fulfill all of them due to constraints such as budget availability. The Next Release Problem (NRP) aims to select the requirements that maximize customer’s benefit, while minimizing development effort. Visualizing the problem search space from a customer concentration perspective, we observed a recurring behavior in all instances analyzed. This work presents these findings and shows some initial results of a Hill Climbing algorithm modified to take advantage of this pattern. The modified algorithm was able to generate solutions that are statistically better than those generated by the original Hill Climbing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd International Conference on Software Engineering. ACM (2011)
Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The next release problem. Information and Software Technology 43(14), 883–890 (2001)
Barros, M.: An experimental evaluation of the importance of randomness in hill climbing searches applied to software engineering problems. Empirical Software Engineering, 1382-3256 (2014)
Gershon, N.D.: From perception to visualization. Computer Graphics 26(2), 414–415 (1992)
Harman, M.: The current state and future of search based software engineering. In: 2007 Future of Software Engineering, pp. 342–357. IEEE Computer Society (2007)
Lu, G., Bahsoon, R., Yao, X.: Applying elementary landscape analysis to search-based software engineering. In: 2010 Second International Symposium on Search Based Software Engineering (SSBSE), pp. 3–8. IEEE (2010)
Sagrado, J., Aguila, I.M., Orellana, F.J.: Ant colony optimization for the next release problem: A comparative study. In: 2010 Second International Symposium on Search Based Software Engineering (SSBSE), pp. 67–76. IEEE (2010)
Xuan, J., Jiang, H., Ren, Z., Luo, Z.: Solving the large scale next release problem with a backbone-based multilevel algorithm. IEEE Transactions on Software Engineering 38(5), 1195–1212 (2012)
Zhang, Y., Harman, M., Mansouri, S.A.: The multi-objective next release problem. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1129–1137. ACM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Fuchshuber, R., de Oliveira Barros, M. (2014). Improving Heuristics for the Next Release Problem through Landscape Visualization. In: Le Goues, C., Yoo, S. (eds) Search-Based Software Engineering. SSBSE 2014. Lecture Notes in Computer Science, vol 8636. Springer, Cham. https://doi.org/10.1007/978-3-319-09940-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-09940-8_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09939-2
Online ISBN: 978-3-319-09940-8
eBook Packages: Computer ScienceComputer Science (R0)