EMPIRICAL SOFTWARE ENGINEERING

Scope & Guideline

Empowering the software engineering community with data-driven discoveries.

Introduction

Delve into the academic richness of EMPIRICAL SOFTWARE ENGINEERING with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1382-3256
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1996 to 2024
AbbreviationEMPIR SOFTW ENG / Empir. Softw. Eng.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The journal 'Empirical Software Engineering' focuses on the application of empirical methods to software engineering, promoting the understanding and improvement of software processes, products, and practices through rigorous research. Its scope encompasses a wide range of topics relevant to both academia and industry, ensuring that findings are grounded in real-world applications.
  1. Empirical Studies in Software Engineering:
    The journal publishes research that employs empirical methods, such as case studies, surveys, experiments, and data analysis, to explore various aspects of software engineering practices and challenges.
  2. Software Quality and Maintenance:
    A core area of focus is the analysis and improvement of software quality, including studies on bug detection, code smells, technical debt, and software maintainability.
  3. Machine Learning and AI in Software Engineering:
    The integration of machine learning and artificial intelligence techniques in software engineering processes is a significant theme, with research exploring their application in areas like defect prediction, automated testing, and code review.
  4. Human Factors in Software Development:
    The journal investigates the social and psychological aspects of software engineering, including team dynamics, developer behavior, and the impact of diversity on software projects.
  5. Software Development Methodologies:
    Research on agile practices, DevOps, and other software development methodologies is prevalent, examining their effectiveness, challenges, and adaptations in various contexts.
  6. Software Repositories and Mining:
    The analysis of software repositories, including code and issue tracking systems, to extract insights and improve software development practices is a recurring focus.
  7. Ethics and Sustainability in Software Engineering:
    Emerging discussions around ethical implications, sustainability, and the societal impacts of software engineering practices are becoming increasingly significant in the journal.
The journal 'Empirical Software Engineering' has identified several emerging themes that reflect current trends in the field. These themes are increasingly being recognized as critical areas for research and development in software engineering.
  1. Integration of AI and Machine Learning:
    There is a notable increase in research focusing on the application of AI and machine learning techniques in software engineering, particularly in areas such as automated testing, defect prediction, and code review.
  2. Remote and Distributed Software Development:
    With the rise of remote work, studies examining the dynamics and challenges of distributed software development teams are gaining prominence, addressing issues such as communication, collaboration, and productivity.
  3. User-Centric Software Engineering:
    Emerging themes include a stronger focus on user experience and user feedback mechanisms, highlighting the importance of understanding user needs and behaviors in the software development process.
  4. Sustainability and Ethical Considerations:
    Research addressing the ethical implications of software engineering practices and their contributions to sustainability is becoming increasingly relevant, reflecting broader societal concerns.
  5. Data-Driven Software Engineering:
    The use of data analytics and empirical evidence to inform software engineering practices is on the rise, with researchers leveraging large datasets to derive insights into software quality and development processes.
  6. DevOps and Continuous Delivery Practices:
    The exploration of DevOps practices and their impact on software delivery and quality is trending, with a focus on automation, integration, and collaboration across development and operations teams.

Declining or Waning

While the journal continues to evolve, certain themes that were once prominent appear to be declining in frequency or emphasis. This shift may reflect changes in industry needs, technological advancements, or evolving research interests.
  1. Traditional Software Testing Techniques:
    There seems to be a waning focus on traditional software testing techniques, with more emphasis now placed on automated and machine learning-enhanced testing methods.
  2. Legacy Systems and Technologies:
    Research specifically targeting legacy systems has decreased, possibly as attention shifts toward modern architectures and practices such as microservices and cloud computing.
  3. Static Analysis Tools:
    While still relevant, the frequency of studies focusing exclusively on static analysis tools appears to be decreasing, as the field moves towards more integrated and dynamic approaches to software quality assurance.
  4. General Software Engineering Theories:
    Theoretical explorations of software engineering concepts without empirical backing are less prevalent, as the journal increasingly prioritizes empirical validation and practical applications.
  5. Single-Discipline Focus:
    Research that remains confined to a singular discipline within software engineering is less common, with a growing trend towards interdisciplinary approaches that combine insights from various fields.

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