The present work is based on the training of an ANFIS network through dataįrom 93 software projects made available by NASA. In this segment line, anĪdaptive Fuzzy Inference System Network, better known as ANFIS, is proposed. Models of effort estimation based on fuzzy logic have the advantage of beingĮasy to manipulate, working with information or misshapen. The internal dynamics of their teams, environment and resources. Traditional models such as CoCoMo and Function Points are widely used, andĬompanies over time arrive at the “fine tuning” of effort modifying factors by knowing Required for software development, however the margin of error resulting from theĬharacteristics is still very significant, showing how much software engineering can Current estimation techniques to predict the effort The experiment results demonstrated a productivity gain when compared to the traditional way of implementing (e.g., Google Maps API, OpenLayers, and Leaflet), and efficient algorithm implementation.Įstimating software project costs is one of the main challenges for softwareįactories and their engineers. To measure the potential of our DSL, we evaluated four types of geospatial data visualization maps with four different technologies. Also, we implemented it to support efficient data filtering operations and generate HTML or Javascript output code files (using Google Maps API). Therefore, we proposed an external Domain-Specific Language (DSL) that allows massive input of raw data and provides a small dictionary with suitable data visualization keywords. Our goal is to increase the productivity of experts who are familiar with the application domain. However, the programming/creating of a visualization for large data sets is still a challenging task for users with low-level of software development knowledge. Data visualization is an alternative for representing information and helping people gain faster insights.