Call for papers, Special Issue on Intelligent Techniques 2026

Name of the call: Intelligent Techniques for Automated Code Review and Software Quality Evaluation (2026)

Abstract

Automated code review and software quality evaluation are becoming increasingly important in modern software engineering, where codebases evolve rapidly and development cycles continue to shorten. Traditional quality assurance practices often rely on manual inspection, handcrafted rules, and isolated analysis tools, which may struggle to scale across large software ecosystems or adapt to changing coding patterns and development requirements. Recent advances in artificial intelligence have created new opportunities to improve the effectiveness, efficiency, and intelligence of software quality assurance.

A wide range of AI-driven techniques, including Automated Machine Learning (AutoML), large language models (LLMs), program analysis, and multimodal learning, are reshaping how software systems are reviewed, analyzed, and maintained. These techniques support a variety of tasks such as defect prediction, bug localization, vulnerability detection, code smell identification, maintainability assessment, refactoring recommendation, and review assistance. In particular, AutoML enables the automatic construction and optimization of predictive models through neural architecture search, hyperparameter optimization, meta-learning, and automated feature engineering, while LLMs and related foundation models provide strong capabilities in code understanding, reasoning, summarization, and developer interaction.

The combination of these emerging approaches offers new possibilities for intelligent software engineering. By jointly leveraging source code, development history, natural language documentation, execution traces, and developer feedback, AI-driven systems can provide more accurate quality evaluation, more explainable review decisions, and more adaptive support for continuous integration and delivery workflows. At the same time, important challenges remain, including scalability, generalization across projects and languages, interpretability, robustness, trustworthiness, and human-AI collaboration in code review processes.

This thematic collection aims to bring together researchers, practitioners, and industry experts working on intelligent techniques for automated code review and software quality evaluation. It seeks to advance the state of the art in AI-driven software engineering by encouraging original contributions on novel methods, practical systems, empirical studies, and industrial applications.

Topics of interest include, but are not limited to:

  • AutoML-driven models for defect prediction, bug localization, and vulnerability detection
  • Automated neural architecture search for code representation learning
  • Meta-learning and transfer learning for cross-project software quality prediction
  • Large language models for code review, code understanding, and quality assessment
  • AI-driven models for defect prediction, bug localization, and vulnerability detection
  • Surrogate-assisted and performance-prediction methods for accelerating software analytics
  • Integration of LLMs and AutoML for intelligent code understanding
  • Adaptive AutoML frameworks for CI/CD, DevOps, and real-time quality monitoring
  • Intelligent techniques for CI/CD, DevOps, software evolution, technical debt analysis, and refactoring recommendation

Therefore, this thematic collection aligns closely with the scope of Automated Software Engineering in terms of its technical focus, application scenarios, and engineering practices, while also resonating with the ongoing trends toward intelligent, automated, and data-driven software development. The collection aims to provide a high-quality forum for researchers and practitioners to advance the state of the art in AutoML, intelligent code review, and software quality evaluation, thereby fostering the next stage of progress in intelligent software engineering.

This thematic collection welcomes a broad range of submission types, including full research papers, short papers, review articles, vision papers, tool demonstrations, and practice-oriented or industry case studies, to encourage diverse perspectives and contributions from both academia and industry.

Rationale and Timeliness of the Thematic Collection

The diversity and recency of the studies listed above further underscore the timeliness and necessity of this thematic collection. These contributions originate from research groups across the globe and include several works published in 2025, reflecting the rapid growth and strong momentum in AutoML-driven code review and software quality research. The emergence of these cutting-edge studies highlights both the expanding interest in this domain and the current lack of a dedicated venue that brings together advances in AutoML, intelligent code analysis, and automated quality evaluation. By providing a focused platform for these rapidly evolving research directions, this special issue addresses a clear community need and supports the continued development of next-generation intelligent software engineering techniques.

Deadlines

  • CFP Distribution: April, 2026
  • Submission Deadline: October 1st, 2026
  • First Review Decisions: December 1st, 2026
  • Revised Paper Submission: February 1st, 2027
  • Final Acceptance Notification: March 1st, 2027

Springer site

  • https://2024.ieeewcci.org/special-sessions
  • https://2025.ijcnn.org/authors/special-sessions -https://www.conference2go.com/event/ieee-2025-7th-international-conference-on-data-driven-optimization-of-complex-systems-docs-2025/

Organizers

  • Nan Li is with Institute of Big Data Science and Industry, Shanxi University, and the personal website is https://www.researchgate.net/profile/Nan-Li-234
  • Xinyan Liang is with Institute of Big Data Science and Industry, Shanxi University, and the personal website is https://xinyanliang.github.io/
  • Jun Yu is with Institute of Science and Technology, Niigata University, and the personal website is https://www.eng.niigata-u.ac.jp/~yujun/indexEn.html
  • Wellington P. Santos is with Depto. de Eng. Biomédica, Universidade Federal de Pernambuco, and the personal website is https://www.researchgate.net/profile/Wellington-Dos-Santos https://scholar.google.com/citations?user=7OE2eYEAAAAJ&hl=vi

Contact

  • Nan Li: linan10@sxu.edu.cn
  • Xinyan Liang: liangxinyan@sxu.edu.cn
  • Jun Yu: yujun@ie.niigata-u.ac.jp
  • Wellington P. Santos: wellington.santos@ufpe.br