Automated Software Engineering

Scope & Guideline

Innovating Automation in Software Development

Introduction

Welcome to your portal for understanding Automated Software Engineering, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN0928-8910
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1994 to 2024
AbbreviationAUTOMAT SOFTW ENG / Automat. Softw. Eng.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The journal 'Automated Software Engineering' focuses on advancing the field of software engineering through automation techniques. It encompasses a wide range of methodologies and research areas aimed at improving software development processes, enhancing software quality, and integrating advanced technologies such as machine learning and artificial intelligence into software engineering practices.
  1. Automation in Software Testing:
    Research that explores automated methods for testing software, including automated test generation, regression testing, and fault localization.
  2. Machine Learning Applications:
    The application of machine learning techniques to various software engineering problems, such as defect prediction, code review, and requirement analysis.
  3. Software Security and Vulnerability Detection:
    Studies focused on identifying and mitigating vulnerabilities in software systems, including automated security testing and analysis of smart contracts.
  4. Model-Driven Engineering:
    Research on using models to automate aspects of software development, including model transformation, code generation, and requirements elicitation.
  5. Human-Machine Collaboration:
    Investigation into how human developers can collaborate with automated systems, including tools that enhance developer productivity and decision-making.
  6. Advanced Software Architectures:
    Explorations into new architectural paradigms for software systems, including microservices, cloud-based architectures, and embedded systems.
The journal is increasingly publishing studies that reflect the current trends and emerging themes in software engineering. This section identifies these themes, which are gaining prominence in the recent literature.
  1. 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.
  2. Automated Code Review and Analysis:
    Recent publications emphasize the automation of code review processes, improving the efficiency and effectiveness of code quality assessments.
  3. Quantum Software Engineering:
    Emerging research is focusing on the unique challenges and methodologies associated with developing software for quantum computing environments.
  4. 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.
  5. 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

While 'Automated Software Engineering' has seen a surge in various research themes, some areas are becoming less prominent as the field evolves. This section highlights these waning themes that may be experiencing reduced focus in recent publications.
  1. 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.
  2. 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.
  3. Manual Testing Approaches:
    As automation continues to dominate, the emphasis on manual testing techniques is diminishing, with fewer studies exploring traditional manual testing strategies.
  4. 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.
  5. 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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