The next release problem (NRP) consists of selecting which requirements will be implemented in the next release of a software system. For many search based software engineering approaches to the NRP, there is still a lack of capability to efficiently incorporate human experience and preferences in the search process. Therefore, this paper proposes an architecture to deal with this issue, where the decision maker (DM) and his/her tacit assessments are taken into account during the solutions evaluations alongside the interactive genetic algorithm. Furthermore, a learning model is employed to avoid an overwhelming number of interactions. An empirical study involving software engineer practitioners, different instances, and different machine learning techniques was performed to assess the feasibility of the architecture to incorporate human knowledge in the overall optimization process. Obtained results indicate the architecture can assist the DM in selecting a set of requirements that properly incor-B Allysson Allex Araújo allysson.araujo@uece.br 123 Autom Softw Eng porate his/her expertise, while optimizing other explicit measurable aspects equally important to the next release planning. On a scale of 0 (very ineffective) to 5 (very effective), all participants found the experience of interactively selecting the requirements using the approach as a 4 (effective).

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Background: Release 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. Methods: Typically, 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. Results: The 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. Conclusions:The 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.

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