Uma taxonomia de preditores de esforço para projetos de aplicativos móveis

Abstract

Mobile applications have different characteristics in relation to other information systems, such as location awareness, types of connectivity, among others. These characteristics influence the estimation of effort, especially in some aspects like stress predictors that are factors associated with effort in the sense that they have an effect on the total amount of effort required to develop a project. There are different predictors, but there is no such classification that facilitates the sharing of knowledge among professionals and researchers, supports the decision-making process, assists the selection of effort predictors to estimate mobile application projects, and helps build a basis for managing knowledge of lessons learned in project estimation. In the context of mobile application effort estimation, the main goal of this research is to organize and classify the existing body of knowledge about mobile application effort predictors, designing and using a taxonomy that supports both research and the practice of effort estimating. To elaborate the taxonomy, a recently proposed taxonomy design method was used and experimental studies were carried out to characterize these effort predictors. The first study consisted of a qualitative research with 8 professionals involved in the effort estimation of 5 different companies, resulting in 54 factors. The second study consisted of a systematic literature mapping that identified 66 tained from the studies, a taxonomy of effort predictors (size metrics and cost factors) was proposed, with a hierarchy of three levels and classified in 12 categories, in addition, a knowledge base of 108 effort predictors was used and the predictors were organized in the 12 categories of taxonomy. The proposed taxonomy may be beneficial in the following ways: i) assist in the selection of effort predictors to estimate mobile application projects; ii) help identify literature of interest; iii) help build a base for knowledge management of lessons learned in project estimation.