Predicting Structure & Clarity of software projects with Machine Learning
Darius Mihnea BOGDAN, Anca Nicoleta Marginean · 2020
The software development domain is very dynamic and only around 28% of the projects that follow project management methodologies stay on schedule. This paper shows methods that try to solve this by aiming identification of the problems encountered by team members faster. The goal of the project is to allow project managers to find meaningful insights on the clarity and structure felt by team members on the tasks they are working on. The system we developed collects data from third party tools used for the development of software projects and correlates it with the marks given by team members for Structure & Clarity daily. We built models for prediction of Structure & Clarity using Random Forest Regression, Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM).