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Quantitative studies in software release planning under risk and resource constraints

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2 Author(s)
Ruhe, G. ; Calgary Univ., Alta., Canada ; Greer, D.

Delivering software in an incremental fashion implicitly reduces many of the risks associated with delivering large software projects. However, adopting a process, where requirements are delivered in releases means decisions have to be made on which requirements should be delivered in which release. This paper describes a method called EVOLVE+, based on a genetic algorithm and aimed at the evolutionary planning of incremental software development. The method is initially evaluated using a sample project. The evaluation involves an investigation of the tradeoff relationship between risk and the overall benefit. The link to empirical research is two-fold: firstly, our model is based on interaction with industry and randomly generated data for effort and risk of requirements. The results achieved this way are the first step for a more comprehensive evaluation using real-world data. Secondly, we try to approach uncertainty of data by additional computational effort providing more insight into the problem solutions: (i) effort estimates are considered to be stochastic variables following a given probability function; (ii) instead of offering just one solution, the L-best (L > 1) solutions are determined. This provides support in finding the most appropriate solution, reflecting implicit preferences and constraints of the actual decision-maker. Stability intervals are given to indicate the validity of solutions and to allow the problem parameters to be changed without adversely affecting the optimality of the solution.

Published in:

Empirical Software Engineering, 2003. ISESE 2003. Proceedings. 2003 International Symposium on

Date of Conference:

30 Sept.-1 Oct. 2003