Automated Software Engineering
Scope & Guideline
Elevating Software Engineering through Innovation and Collaboration
Introduction
Aims and Scopes
- Automation in Software Testing:
Research that explores automated methods for testing software, including automated test generation, regression testing, and fault localization. - Machine Learning Applications:
The application of machine learning techniques to various software engineering problems, such as defect prediction, code review, and requirement analysis. - Software Security and Vulnerability Detection:
Studies focused on identifying and mitigating vulnerabilities in software systems, including automated security testing and analysis of smart contracts. - Model-Driven Engineering:
Research on using models to automate aspects of software development, including model transformation, code generation, and requirements elicitation. - Human-Machine Collaboration:
Investigation into how human developers can collaborate with automated systems, including tools that enhance developer productivity and decision-making. - Advanced Software Architectures:
Explorations into new architectural paradigms for software systems, including microservices, cloud-based architectures, and embedded systems.
Trending and Emerging
- Deep Learning in Software Engineering:
There is a significant rise in the use of deep learning techniques for various software engineering tasks, including defect prediction, code generation, and sentiment analysis. - Automated Code Review and Analysis:
Recent publications emphasize the automation of code review processes, improving the efficiency and effectiveness of code quality assessments. - Quantum Software Engineering:
Emerging research is focusing on the unique challenges and methodologies associated with developing software for quantum computing environments. - Integration of AI in Development Processes:
The integration of artificial intelligence into software development is a growing theme, with studies exploring AI-assisted coding, debugging, and project management. - Security in Software Development:
The focus on software security, particularly in the context of automated vulnerability detection and analysis, is increasingly important in the current research landscape.
Declining or Waning
- Traditional Software Development Methodologies:
There is a noticeable decline in research focused on traditional methodologies such as waterfall or V-model approaches, as newer agile and automated frameworks gain traction. - Static Analysis Techniques:
Research on static code analysis appears to be decreasing, possibly due to the rise of more dynamic and machine learning-based approaches that provide better flexibility and insights. - Manual Testing Approaches:
As automation continues to dominate, the emphasis on manual testing techniques is diminishing, with fewer studies exploring traditional manual testing strategies. - Legacy Systems Maintenance:
The focus on maintaining and upgrading legacy systems is waning, as more research shifts towards modern development practices and cloud-native solutions. - Single-Platform Development:
Research concentrating on development for single platforms is decreasing, as there is a growing trend towards cross-platform and multi-device applications.
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