Estimating Software Anomalies Using Machine Learning

Abstract: Estimating Software Anomalies is a critical aspect of ensuring the reliability and quality of software projects. It involves identifying and predicting bugs or faults in the source code. By detecting faults early on, developers can address them promptly, leading to improved software development processes and reduced debugging time and costs. This project focuses on developing an intelligent system that leverages advanced machine learning techniques to predict software faults in the source code. The proposed software fault prediction (SFP) model utilizes a combination of LSTM networks, bidirectional LSTM networks, support vector machines (SVM), and neural networks. These techniques enable the system to learn complex patterns in the data and make accurate predictions. The effectiveness of the proposed system is evaluated using real-world software projects, where it outperforms existing software fault prediction models. By implementing this intelligent system, developers can quickly and accurately detect faults, leading to enhanced reliability and quality of software projects. The system's ability to identify bugs during the development stage of a new project version and detect faults in a completed version offers significant benefits. It empowers developers to improve the software development process, reduce debugging time, and ultimately deliver higher-quality software projects.

Keywords: LSTM, SFP, SVM, software anomalies, machine learning algorithm.