Aims and scope

Automated Software Engineering is an archival, peer-reviewed journal publishing research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modelling software artifacts and processes. Both automatic systems and collaborative systems are within the scope of the journal, as are computational models of human software engineering activities. Knowledge representations and artificial intelligence techniques applicable to automated software engineering are of interest, as are formal techniques that support or provide theoretical foundations.

Topics of interest include, but are not restricted to: specification and design representation schemes, both formal and informal; descriptions and models of the development process; tools and environments to support software development; cognition in software development, including studies of specifiers, designers and implementors, and cognitive properties of representation schemes, programming and programming languages; software development methods, analysis and validation; requirements elicitation, acquisition and formalization; system rationale; software quality and metrics; software reuse and adaptation; animation and execution of specifications and designs; domain modelling and analysis; software visualization; software object management; development of user interfaces; group and team work in software engineering; development of distributed, real-time, embedded and composite systems; systems integration; software evolution and maintenance; system testing; reverse engineering and program understanding; documentation and program explanation.

Automated Software Engineering is published bi-yearly by Springer, with worldwide distribution to individuals, industry and institutions. It includes reviews of books, software, conferences, and workshops.

Special Issue on NIER@ASE Track 2025
截稿日期: 2026-02-28

The Special Issue for the NIER@ASE Track in the Automated Software Engineering Journal aims to provide a platform for showcasing forward-looking, innovative research in software engineering. This special issue invites selected papers from the NIER track at the ASE conference to submit extended versions for publication. Authors are encouraged to submit papers that present significant, original, and unpublished results within these areas. Submissions will undergo a rigorous peer-review process to maintain the journal’s high standards. We invite researchers and practitioners from academia and industry to contribute their cutting-edge research and insights to this special issue.

For the list of welcomed topics, please see the following link: https://ause-journal.github.io/26ase-nier.html 

Special Issue on Advances and Applications in Search-Based Software Engineering
截稿日期: 2026-03-31

Search-Based Software Engineering (SBSE) is a dynamic research field that reformulates software engineering problems as search problems, leveraging optimization techniques to address challenges across the software development lifecycle. By applying computational intelligence methods—ranging from metaheuristics like evolutionary algorithms and simulated annealing to exact optimization techniques—SBSE enables effective and efficient solutions to complex software engineering tasks.

This special issue aims to showcase recent advances in SBSE, highlighting innovative applications, theoretical contributions, empirical evaluations, and industrial experiences. The issue will explore how SBSE intersects with emerging trends in AI, machine learning, and automation, pushing the boundaries of software engineering research and practice.

We invite submissions of high-quality original research papers addressing topics including (but not limited to):

- Innovative Applications of SBSE: Applying SBSE techniques to novel or underexplored areas of software engineering, such as security, sustainability, or AI-driven systems.
- Theoretical Analyses: Advancing the understanding of search algorithms tailored for software engineering problems, including multi-objective optimization and robustness analysis.
- Empirical Evaluations: Rigorous experimental studies demonstrating the effectiveness and scalability of SBSE methods, with reproducible datasets and benchmarks.
- Industrial Experiences: Reports detailing real-world applications of SBSE in industry, including lessons learned and challenges encountered

Special Invitation for SSBSE 2025 Papers

Authors of well-reviewed papers from the 17th Symposium on Search-Based Software Engineering (SSBSE 2025), held in Seoul in November 2025, are especially encouraged to submit extended versions of their work. To ensure substantial contributions beyond the conference version, submissions must meet the following criteria:

- Revised Title: A distinct title differentiating the journal submission from the conference paper.
- New Content: At least 30% novel material (e.g., expanded experiments, additional theoretical insights, or new applications).
- Clear Discussion: A detailed explanation of how the journal version builds upon the original conference paper.

How to Submit

Submissions should conform to the journal’s formatting requirements and will undergo rigorous peer review. Authors are encouraged to include supplementary materials (e.g., datasets or experimental results) to enhance reproducibility.

Special Issue on Automated, Verifiable, and Generative Approaches to Software Engineering
截稿日期: 2026-04-30

The increasing complexity, dynamism, and scale of modern software systems have intensified the demand for advanced forms of automation throughout the software development lifecycle. The emergence of Generative Artificial Intelligence, particularly Large Language Models (LLMs), combined with architectural and methodological advances such as multi-agent systems, has introduced transformative opportunities for automated analysis, synthesis, verification, and maintenance of software. Simultaneously, the growing importance of correctness, adaptability, and trustworthiness in software-intensive systems underscores the need for rigorous verification and validation frameworks that can seamlessly integrate with automated engineering workflows.

This Special Issue invites high-quality submissions that explore the foundations, methods, and tools required to advance automated, verifiable, and generative approaches to software engineering. We seek contributions offering novel scientific insights, innovative engineering solutions, extensive empirical studies, and significant technological advancements that foster deeper integration between automation, AI-driven engineering, and formal verification. Interdisciplinary works that connect software engineering with artificial intelligence, distributed systems, human–AI collaboration, and cyber-physical systems are also welcome.

By bringing together perspectives on automated synthesis, rigorous verification, agent-based collaboration, and generative modeling, this Special Issue aims to showcase emerging research that can shape the future of trustworthy, adaptive, and scalable software systems.

Scope and Topics of Interest

Submissions are invited on (but not limited to) the following topics:

Automated and Generative Software Engineering

    Automated synthesis of software artifacts
    Generative AI and LLM-enabled approaches for requirements, modeling, design, code generation, evolution, and maintenance
    Integration of LLMs into automated engineering workflows
    Tool support for generative modeling, refactoring, design space exploration, and automated testing

Multi-Agent and Distributed Approaches

    Architectures and coordination frameworks for multi-agent systems in software engineering
    Distributed and collaborative problem-solving for development, verification, and adaptation
    Engineering agents for reasoning, negotiation, planning, and decision-making
    Practical issues in agent-based engineering frameworks

Verification, Validation, and Correctness

    Formal verification of automated or AI-generated artifacts
    Compositional theories, correctness proofs, and dynamic verification
    Hybrid verification combining formal methods with ML or generative models
    Validation of performance, energy, reliability, and security
    Runtime verification, monitoring, and self-adaptive assurance

Model-Driven and Architecture-Centric Approaches

    Model-driven engineering, transformations, co-simulation, and multi-model integration
    Formal specifications of architectures and system behaviors
    Automated migration to microservice- or component-based architectures
    Domain-specific modeling languages and model synthesis

AI-Driven and Learning-Based Methods

    ML and AI for comprehension, traceability, debugging, optimization, and anomaly detection
    Automated planning, reasoning, and decision-making for generative or adaptive design
    Intelligent agents and learning mechanisms for automation
    Predictive models integrated with verification workflows

Applications, Empirical Studies, and Emerging Domains

    Industrial case studies on automated, verifiable, or generative engineering
    Empirical evaluations, benchmarks, and experimental frameworks
    Automated development and formal verification of smart contracts
    Managing large-scale ecosystems through automation and AI
    Human–AI interaction and collaboration in automated environments

Please note that surveys, systematic literature reviews, and mapping studies fall outside the scope of this Special Issue.

Authors should prepare their manuscript according to the Instructions for Authors available from the Journal’s submission guidelines https://link.springer.com/journal/10515/submission-guidelines. Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation by at least two independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.

Please note that the authors of selected papers presented at AYSDE '25 and MAS-GAIN '25 are invited to submit an extended version of their contributions by taking into consideration both the reviewers’ comments on their conference paper, and the feedback received during presentation at the conference. It is worth clarifying that the extended version is expected to contain a substantial scientific contribution, e.g., in the form of new algorithms, experiments or qualitative/quantitative comparisons, and that neither verbatim transfer of large parts of the conference paper nor reproduction of already published figures will be tolerated.

The extended versions of AYSDE '25 and MAS-GAIN '25 papers will undergo the standard, rigorous journal review process and be accepted only if well-suited to the topic of this special issue and meeting the scientific level of the journal. Final decisions on all papers are made by the Editor in Chief.

Special Issue on Genetic Improvement, 2026
截稿日期: 2026-07-31

Genetic Improvement is the application of evolutionary and search-based optimisation methods to the improvement of existing software. It has been used to improve both software functional properties, such as fixing bug or performing automated code transplantation, and software non-functional properties, such as minimising execution time, memory usage, or energy consumption.

We invite submissions that discuss recent developments in all areas of research on, and applications of, Genetic Improvement. Topics of interest include both the theory and practice of Genetic Improvement. Applications of GI include, but are not limited to:

- Improve runtime efficiency
- Decrease memory consumption
- Decrease energy consumption
- Transplant new functionality
- Specialise software
- Generate multiple versions of software
- Improve low level or binary code
- GI techniques in industrial settings
- Use of AI/large language models in combination with GI
- Use of AI/large language models to improve GI or vice versa