Abstract
In this position paper, we argue that search based software engineering techniques can be applied to the optimisation problem during the requirements analysis phase. Search based techniques offer significant advantages; they can be used to seek robust, scalable solutions, to perform sensitivity analysis, to yield insight and provide feedback explaining choices to the decision maker. This position paper overviews existing achievements and sets out future challenges.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ang, K.H., Chong, G., Li, Y.: Visualization Technique for Analyzing Non-Dominated Set Comparison. In: 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), Singapore, vol. 1, pp. 36–40 (2002)
Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The Next Release Problem. Information & Software Technology 43(14), 883–890 (2001)
Collette, Y., Siarry, P.: Multiobjective Optimization: Principles and Case Studies. Springer, Heidelberg (2003)
Greer, D., Ruhe, G.: Software Release Planning: an Evolutionary and Iterative Approach. Information & Software Technology 46(4), 243–253 (2004)
Harman, M.: The Current State and Future of Search Based Software Engineering. In: 29th International Conference on Software Engineering (ICSE 2007), Future of Software Engineering (FoSE), pp. 342–357. IEEE Computer Society, Washington (2007)
Harman, M., Clark, J.: Metrics are Fitness Functions Too. In: 10th International Software Metrics Symposium (METRICS 2004), pp. 58–69. IEEE Computer Society, Washington (2004)
Harman, M., Swift, S., Mahdavi, K.: An Empirical Study of the Robustness of two Module Clustering Fitness Functions. In: International Conference on Genetic and Evolutionary Computation (GECCO 2005), pp. 1029–1036. ACM, New York (2005)
Obayashi, S., Sasaki, D.: Visualization and Data Mining of Pareto Solutions using Self-Organizing Map. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 796–809. Springer, Heidelberg (2003)
Pryke, A., Mostaghim, S., Nazemi, A.: Heatmap Visualisation of Population Based Multi Objective Algorithms. Technical Report, University of Birmingham (2006)
Ren, J.: Sensitivity Analysis in Multi-Objective Next Release Problem and Fairness Analysis in Software Requirements Engineering. Master’s thesis, DCS/PSE, King’s College London, London (2007)
Saliu, M.O., Ruhe, G.: Bi-Objective Release Planning for Evolving Software Systems. In: 6th European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, pp. 105–114. ACM, New York (2007)
Szidarovsky, F., Gershon, M.E., Dukstein, L.: Techniques for multiobjective decision making in systems management. Elsevier, New York (1986)
Zhang, Y., Harman, M., Mansouri, S.A.: The multi-objective next release problem. In: International Conference on Genetic and Evolutionary Computation (GECCO 2007), pp. 1129–1136. ACM, New York (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Finkelstein, A., Harman, M. (2008). Search Based Requirements Optimisation: Existing Work and Challenges. In: Paech, B., Rolland, C. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2008. Lecture Notes in Computer Science, vol 5025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69062-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-69062-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69060-3
Online ISBN: 978-3-540-69062-7
eBook Packages: Computer ScienceComputer Science (R0)