Artificial Intelligence Applications in Software Engineering (ICAIASE-27)

This track focuses on the integration of artificial intelligence in software testing methodologies. It aims to explore innovative approaches that enhance the efficiency and effectiveness of testing processes through AI-driven tools.

This session will delve into the application of machine learning techniques for code analysis and quality assurance. Participants will discuss novel algorithms and frameworks that leverage ML to identify vulnerabilities and improve code maintainability.

This track examines the role of deep learning in various phases of software development, from design to deployment. It will highlight case studies and research that demonstrate the transformative impact of deep learning on software engineering practices.

This session will explore the use of intelligent software agents in automating and optimizing software development tasks. Discussions will include their role in collaborative environments and their potential to enhance productivity.

This track focuses on the development and application of AI-based tools for debugging software. Participants will share insights on how AI can assist developers in identifying and resolving defects more efficiently.

This session will investigate the application of natural language processing techniques in enhancing software recommender systems. It will cover methodologies that improve user experience and decision-making in software selection.

This track will explore advancements in autonomous code generation using AI technologies. Researchers will present their findings on how these techniques can streamline the coding process and reduce human error.

This session will focus on the application of AI in the requirements engineering process. It will discuss tools and methodologies that facilitate better requirements gathering, analysis, and validation.

This track examines the use of artificial intelligence in predictive maintenance strategies for software systems. Participants will discuss models that forecast potential issues and optimize maintenance efforts.

This session will explore how AI technologies can improve project management practices in software engineering. Discussions will include tools that assist in resource allocation, risk assessment, and project tracking.

This track will focus on the application of neural networks for detecting defects in software systems. Participants will share research on the effectiveness of neural network models in enhancing software quality assurance.