Altino Dantas

An Architecture based on interactive optimization and machine learning applied to the next release problem

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 incorporate 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).

Interactive Software Release Planning with Preferences Base

The release planning is a complex task in the software development process and involves many aspects related to the decision about which requirements should be allocated in each system release. Several search based techniques have been proposed to tackle this problem, but in most cases the human expertise and preferences are not effectively considered. In this context, this work presents an approach in which the search is guided according to a Preferences Base supplied by the user. Preliminary empirical results showed the approach is able to find solutions which satisfy the most important user preferences.

Search-Based Bug Report Prioritization for Kate Editor Bugs Repository

The prioritization of bugs in online repositories can be considered a complex and important task. Thus, providing an automatic strategy to deal with this challenge can be useful and significantly collaborate with the repository use. In this paper, a search-based approach to prioritize bugs in the Kate Editor Bugs Repository is proposed, taking into account some valuable information given by the repository users about the bugs. Experiments demonstrate the proposed approach can be calibrated to fit particular scenarios and can produce intelligent bug orders.

Human Resource Allocation in Agile Software Projects Based on Task Similarities

The definition of which task should be assigned to each member of a team is a relevant issue on the software project management. This decision is complex because it involves a high number of variables, such as different levels of employee skills and several characteristics of each task. Thus, we propose a multi-objective approach aims at minimizing the time and cost of a software project through the allocation of suited and similar tasks to employees. In addition, we conducted a preliminary empirical study to investigate the performance of NSGA-II, MOCell and random search. Preliminary results suggest the approach is useful for allocating human resources in software projects.

Incorporating decision maker’s preferences in a multi-objective approach for the software release planning

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.

Dynamic Bugs Prioritization in Open Source Repositories with Evolutionary Techniques

The bugs prioritization in open source repositories is considered an important and complex task. Mainly because, a lot of information about bugs changes over time and affects the prioritization process. Based on this dynamic characteristic, this work proposes a model to prioritize bugs as dynamic optimization problem. A preliminary empirical study was conduced comparing two dynamic evolutionary approaches and a static one. The achieved results demonstrated that a dynamic approach outperforms the static one in all evaluated scenarios.

A Multi-objective Approach to Prioritize and Recommend Bugs in Open Source Repositories

Bugs prioritization in open source repositories poses as a challenging and complex task, given the significant number of reports and the impact of a wrong bug assignment to the software evolution. Deciding the most suitable bugs in order to be solved can be considered as an optimization problem. Thus, we propose a search-bas ed approach supported by a multi-objective paradigm to tackle this problem, aiming to maximize the resolution of the most important bugs, while minimizing the risk of later resolution of the most severe ones. Furthermore, we propose a strategy to avoid the developer’s effort when choosing a solution from the Pareto Front. Regarding the empirical study, we evaluate the performance of three metaheuristics and investigate the human competitiveness of the approach. Overall, the proposal can be said human competitive in a real-world scenario and the NSGA-II outperformed both MOCell and IBEA in the adopted quality measures.