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Abstract-Selecting and prioritizing the most stable software requirements within a set of requirements and engaging them in releases that satisfy the most customers is a difficult task for the decision maker. Many methods have been employed to solve this type of problem. But we do not find many solutions that use Verbal Decision Analysis. Therefore, in this paper we aim to select and prioritize software requirements using Verbal Decision Analysis techniques as a tool, exploring the ZAPROS III-i method and comparing the results with those obtained by the NSGA-II, SPEA2 and Mocell metaheuristics and also with a random algorithm.

An increasingly common practice in large software development companies is to distribute tasks among geographically dispersed teams. This practice can bring many benefits, such as gains in terms of time and cost, but many are the challenges. One of the major challenges regards the method of assigning tasks to remote teams. This method involves knowing, classifying and ordering the factors that drive the assignment of tasks in a distributed scenario. This is a typical scenario for decision-making based on multiple criteria. Verbal decision analysis (VDA) is a multi-criteria framework to decisionmaking. This study presents a hybrid methodology structured on methods of VDA for classification ORdinal CLASSification (ORCLASS) and ordering (ZAPROS III-i) of factors that drive task assignment to distributed teams in software development projects. Tasks were grouped according to their type, i.e. requirements, architecture, implementation, and testing.

During the software development process, the decision maker (DM) must master many variables inherent in this process. Software releases represent the order in which a set of requirements is implemented and delivered to the customer. Structuring and enumerating a set of releases with prioritized requirements represents a challenging task because the requirements contain their characteristics, such as technical precedence, the cost required for implementation, the importance that one or more customers add to the requirement, among other factors. To facilitate this work of selection and prioritization of releases, the decision maker may adopt some support tools. One field of study already known to solve this type of problem is the Search-Based Software Engineering (SBSE) that uses metaheuristics as a means to find reasonable solutions taking into account a set of well-defined objectives and constraints. In this paper, we seek to increase the possibilities of solving the Next Release Problem using the methods available in Verbal Decision Analysis (VDA). We generate a problem and submit it so that the VDA and SBSE methods try to resolve it. To validate this research, we compared the results obtained through VDA and compared with the SBSE results. We present and discuss the results in the respective sections.

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms’ composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.