Machine Learning-Enhanced Software Development: State of the Art and Future Directions

Sreedhar Reddy Konda; Varun Shah

Sreedhar Reddy Konda Burns and McDonnell, Position: Software Engineer, Information Technology Department, Address: 9400 Ward Pkwy, Kansas City, MO 64114
Varun Shah Company: Amazon Service LLC, Position: Software Development Manager, Address: 188 Spear St #250, San Francisco, CA 94105

Keywords: Machine Learning, Software Development, Automation, Predictive Maintenance, Interpretability, Federated Learning, Ethical AI

Abstract

Abstract: Machine learning (ML) has emerged as a powerful tool for enhancing various aspects of software development, revolutionizing traditional practices and opening new avenues for innovation. This paper provides an overview of the current state of the art in ML-enhanced software development and outlines potential future directions in this rapidly evolving field. The abstract begins by highlighting the transformative impact of ML on software development processes, including requirements elicitation, design, implementation, testing, and maintenance. By leveraging large volumes of data and sophisticated algorithms, ML techniques have enabled automated code generation, intelligent bug detection, and predictive maintenance, streamlining development workflows and improving software quality. Next, the abstract discusses key challenges and limitations associated with ML-enhanced software development, such as data quality issues, algorithmic biases, and interpretability concerns. While ML algorithms have demonstrated remarkable performance in certain tasks, their effectiveness may be limited by the availability and quality of training data, as well as the interpretability of model outputs. Furthermore, the abstract explores emerging trends and future directions in ML-enhanced software development, including the integration of ML models into development tools and environments, the adoption of federated learning approaches for collaborative model training, and the exploration of ethical and societal implications of ML-powered software systems. Overall, this paper provides valuable insights into the current state of ML-enhanced software development and offers a roadmap for future research and innovation in this dynamic and rapidly evolving field. By harnessing the potential of ML technologies, software developers can unlock new capabilities, accelerate development cycles, and create more intelligent and adaptive software systems.