Aims, Objectives and Scope

VFAST Transactions on Software Engineering is a peer-reviewed scientific journal published by the VFAST Research Platform. Established in 2013, the journal provides an international platform for researchers, engineers, and academicians to share and advance knowledge in software engineering, artificial intelligence, and machine learning applications.

The journal primarily focuses on cutting-edge research in AI-driven software engineering methodologies, tools, and applications. It seeks to promote the integration of artificial intelligence, machine learning, and data-driven approaches with traditional software engineering paradigms to enhance automation, efficiency, and decision-making in software development.

To ensure high-quality publications, all submissions undergo a rigorous peer-review process to maintain originality, technical depth, and practical relevance. The journal also welcomes survey and review articles that provide insightful discussions on emerging trends and future directions in AI-powered software engineering.

The objectives of the journal include:

  • Publishing high-quality, peer-reviewed research contributions that bridge AI, machine learning, and software engineering.
  • Encouraging interdisciplinary research that integrates computational intelligence, big data analytics, and automated software systems.
  • Promoting novel methodologies that enhance software development, quality assurance, and intelligent decision-making.

Scope

The journal publishes original research articles, review papers, and case studies in software engineering, artificial intelligence, and machine learning applications, including but not limited to:

AI and Machine Learning in Software Engineering

  • AI-assisted Software Development and Testing
  • Machine Learning-based Software Optimization
  • Automated Bug Detection and Fixing using AI
  • Natural Language Processing in Software Engineering
  • Deep Learning for Code Generation and Understanding
  • AI-driven DevOps and Continuous Integration
  • Large Language Models (LLMs) in Software Engineering

Software Engineering and Computational Intelligence

  • Knowledge-based Software Engineering
  • Software Engineering Decision Support using AI
  • Evolutionary and Genetic Algorithms in Software Engineering
  • AI-powered Requirements Engineering and Software Design
  • Software Engineering for Autonomous Systems

Data-driven and Emerging Technologies

  • Big Data Analytics for Software Engineering
  • Explainable AI in Software Engineering
  • Software Engineering for Smart Systems and IoT
  • AI-based Cybersecurity and Secure Software Engineering
  • AI-driven Software Maintenance and Evolution

Interdisciplinary Applications

  • Software Applications in Machine Learning, Bioinformatics, Computer Vision, Robotics, Image Processing, and Cyber-Physical Systems
  • Human-AI Collaboration in Software Engineering
  • AI and Ethics in Software Development