During the process of software development, it is very important to monitor the status of the project and identify the potential risks. Researchers have proposed several approaches to disclose the risks of software projects, but they are either based on black-box models which are hard to explain or require lots of manual efforts. In this research, we adopt a machine-learning-based approach to predict the success/failure of a software project based on the previous development data. In contrast to those black-box approaches, our approach can output the importance of the features which explains the reason of the prediction. Firstly, we build a machine learning model and train this model with previous development data of software projects. Secondly, we feed the data of the project under development to this trained model and predict the success/- failure of this project. Finally, the reasons of the prediction are displayed to the users. We implemented this approach and evaluated it with a dataset of 11,954 real world software projects. The evaluation reached a recall of 80.1% and precision of 53.7%, which shows the feasibility of this machine-learning-based approach.
https://ror.org/02exqgm79 