Abstract
Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred.
Keywords
Artificial neural network
Computer science
Software
Artificial intelligence
Operating system
Subject Areas
Economic and Technological Systems Analysis
·Management of Technology and Innovation ·Social Sciences
Advanced Research in Systems and Signal Processing
·Control and Systems Engineering ·Physical Sciences
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Industry, innovation and infrastructure 40%