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Abstract
BackgroundRelease planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker’s (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM’s preferences to guide the optimization process through search space area of his/her interests.MethodsTypically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM’s preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations.ResultsThe automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one.ConclusionsThe results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human’s preferences about the requirements allocation into the solution of release planning.
Highlights
The incremental and iterative software life cycle is based on the idea of developing an initial system implementation and evolving it through several releases in a cyclic way [1]
There are a number of existing approaches that are based on the belief that Release planning (RP) can be formalized as an optimization problem and widely explored by Search-based software engineering (SBSE) as well
This paper significantly extends the previous work in two major aspects: (a) besides increasing the automatic experiment through two more search techniques, one large artificial dataset, and additional results, we conduct a participant-based experiment to observe the behavior of the approach in a real-world context and (b) a prototype tool was developed and made available to enable a novel way to incorporate the human preferences during the release planning
Summary
The incremental and iterative software life cycle is based on the idea of developing an initial system implementation and evolving it through several releases in a cyclic way [1]. As “suitable,” we may consider that it properly deals with variables that present complex relations such as stakeholders’ preferences, technical constraints, limited resources, and subjective business aspects. This process can be time-consuming, requiring one to analyze an exhaustive list of possible combinations, which tend to be extremely large if the number of requirements is great. There are many approaches in which RP is formalized as an optimization problem In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. As highlighted by Harman [15], there are only two key ingredients for the application of search-based optimization to software engineering problems:
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