Software release planning: an evolutionary and iterative approach

• Published in: Information and software technology 2004-03, Vol.46 (4), p.243-253
• Main Authors: Greer, D, Ruhe, G
• Format: Article
• Language: English
• Subjects: Decision support systems; Development; Genetic algorithm; Genetic algorithms; Incremental software development; Planning; Product introduction; Release planning; Requirements prioritization; Software; Software engineering decision support; Stakeholders; Studies; Systems development
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2003.07.002

To achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. In adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. Three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. The technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. Stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. The technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. This paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called EVOLVE aimed at the continuous planning of incremental software development. The optimization method used is iterative and essentially based on a genetic algorithm. A set of the most promising candidate solutions is generated to support the final decision. The paper evaluates the proposed approach using a sample project.