Empirical Analysis of Computationally Intelligent Technique for Software Risk Prediction

 

 

 

Mohd Shabbir1,*, Rakesh Kumar Yadav1, Mohd Waris Khan2, Hitendra Singh3

 

1Department of Computer Science and Engineering, MUIT, Lucknow, India

 

2Department of Computer Application, Integral University, Lucknow, India

 

3Department of Electronic s and Communication Engineering, MUIT, Lucknow, India

 

Emails: shabbir.aec@gmail.com; rkymuit@gmail.com; wariskhan070@gmail.com; hit.singh111@gmail.com

 

 

 

Abstract

 

Software development is inherently associated with a high degree of uncertainty, often arising from unforeseen activities during different phases of the SDLC. As software systems expand in scale and complexity, the likelihood of failures and project delays also increases. Such situations, which are usually not anticipated, are known as software risks. They arise due to different reasons, which affect activities like essentials of engineering, making, putting into usage, and test. These risks need to be identified and managed in the initial phase for delivering software-related products that are both excellent and can be relied upon. While it has been standard practice in assessing software risks to depend upon human skills and previous experiences, it has been observed they lead to issues in consistency and often are reported to be unreliable. The current study is an attempt to tackle this issue through usage of predictive models that have their roots in machine learning (ML).  Borrowing from existing data, software risks are identified and classified through five popular machine-learning tools. To improve correctness and make it more robust, selection techniques of selection with multiple features are implemented. Among the other models, the Support Vector Machine (SVM) exhibited the maximum performance, achieving a classification accuracy of approximately 80%, with a precision of 84%, recall of 80%, and an F1 score of 80%. In terms of performance, Mutual Information was found to be best in methods of applied feature selection. The study indicates the ability of ML based methods in predicting and managing software risks. Additionally, this research highlights the potential of computationally intelligent techniques to assist project managers in early risk identification, proactive decision-making and enhancing the overall success rate of s/w projects.

 

Keywords: Software Risk; Software Risk Prediction; Risk Management; Requirement Analysis; AI & Machine Learning