Advancing Software Engineering with Artificial Intelligence

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 481

Special Issue Editors


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Guest Editor
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
Interests: AI; machine learning; explainable AI; data mining
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Interests: artificial intelligence; machine learning; software engineering; optimization; software testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integration of Artificial Intelligence (AI) into all aspects of Software Engineering. While AI has been applied to tasks such as code generation, testing, and requirements analysis, several critical challenges remain underexplored, including explainability, human–AI collaboration, software maintenance, security, ethics, reproducibility, and sustainability.

For this Special Issue, we request high-quality original research and surveys addressing these gaps, aiming to foster AI solutions that are trustworthy, effective, ethical, and sustainable across the software development lifecycle.

Key areas of interest include, but are not limited to, the following:

  • Artificial intelligence in software engineering;
  • AI-assisted software development;
  • Lifecycle-wide AI integration;
  • Explainable and trustworthy AI for software engineering;
  • Human–AI collaboration;
  • AI for software maintenance;
  • AI for software testing;
  • Bias and fairness in software AI;
  • Security and robustness of AI systems;
  • Reproducibility;
  • AI-powered refactoring and code quality analysis.

Dr. Waddah Saeed
Dr. AbdulRahman Alsewari
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence (AI)
  • software engineer
  • software AI
  • AI systems

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Published Papers (1 paper)

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Research

26 pages, 1283 KB  
Article
BlackBoxTestGen: An Automatic Black-Box Test Case Generation Framework
by Adisak Intana, Kuljaree Tantayakul and Pongsakorn Kaewnaka
Computers 2026, 15(5), 263; https://doi.org/10.3390/computers15050263 - 22 Apr 2026
Viewed by 204
Abstract
Software testing is essential for software engineering practices, as it ensures that the final software product is reliable and satisfies all requirements before delivery. However, manually designing black-box testing test cases is time-consuming, inconsistent, and difficult to maintain in accordance with changing specifications. [...] Read more.
Software testing is essential for software engineering practices, as it ensures that the final software product is reliable and satisfies all requirements before delivery. However, manually designing black-box testing test cases is time-consuming, inconsistent, and difficult to maintain in accordance with changing specifications. Therefore, this paper presents BlackBoxTestGen, an automatic framework that unifies three specification-driven black-box testing techniques, including rule-based Equivalence Class Partitioning (ECP), syntax, and state transition testing. The framework utilises a redesigned XML structure for test case generation to be shared among a data dictionary, decision tree, and state machine, used by each testing technique. The degree of testing coverage is accumulatively calculated during the test case generation process. The beneficial value of our proposed framework was demonstrated with the development of a web-based prototype tool. We rigorously evaluated its performance in terms of accuracy, computational efficiency, and scalability through a multidimensional approach. This included assessment by professional experts, algorithmic stress testing via parameter scaling, and application to close-to-realistic case studies. The results indicate that BlackBoxTestGen provides a robust integration of testing techniques. By automating the generation of compact and reproducible test cases, the framework substantially reduces manual effort and minimises drift between techniques. Full article
(This article belongs to the Special Issue Advancing Software Engineering with Artificial Intelligence)
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