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Using Generative Artificial Intelligence Within Software Engineering
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Using Generative Artificial Intelligence Within Software Engineering

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4194

Special Issue Editors

Prof. Dr. Leon Sterling

E-Mail Website
Guest Editor
Centre for Design Innovation, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: software agents; agent-based systems; software engineering; human-computer interactions; information technology in education; internet; medical devices and diagnostics
Dr. Xuan Bach D. Le

E-Mail Website
Guest Editor
School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3052, Australia
Interests: software mining; empirical software engineering; program repair; formal methods; computer security; computing systems

Special Issue Information

Dear Colleagues,

Generative artificial intelligence (Gen-AI) has been applied to all stages of the software development lifecycle, from planning through to maintenance. Despite many claims that the use of Gen-AI constitutes a revolution to software engineering, it is hard to determine how effective artificial intelligence systems are in practice. Many of the claims, for example, relate to small problems that people can easily program themselves.

Furthermore, it is hard to determine the best ways to use new Gen-AI systems. While many dream of fully autonomous software development, human development is needed to avoid mistakes. People are needed to both point out mistakes and tweak the prompts to achieve a more accurate result.

This Special Issue calls for methods and case studies on the use of generative artificial intelligence in all phases of software engineering. The scope of this Special Issue is broad, encompassing software requirements, software design, coding, software testing, and software maintenance. All aspects of software engineering are of interest to this Special Issue. Also in scope are studies that use Gen-AI to analyze patterns of user behavior and suggest design improvements. Other subjects for analysis could include error logs and user feedback.

Prof. Dr. Leon Sterling
Dr. Xuan Bach D. Le
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative artificial intelligence
  • software requirements
  • software design
  • coding
  • software testing
  • software maintenance

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Published Papers (4 papers)

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Research

19 pages, 416 KB  
Article
Hybrid Intelligence in Requirements Education: Preserving Student Agency in Refining User Stories with Generative AI
by Leon Sterling and Eduardo Oliveira
Information 2026, 17(2), 166; https://doi.org/10.3390/info17020166 - 6 Feb 2026
Viewed by 514
Abstract
Generative Artificial Intelligence (Gen AI) offers significant potential to support requirements engineering (RE) education; however, its integration poses challenges regarding accuracy and student engagement. While Gen AI cannot independently specify requirements without hallucinating or overstepping scope, it can serve as a powerful partner [...] Read more.
Generative Artificial Intelligence (Gen AI) offers significant potential to support requirements engineering (RE) education; however, its integration poses challenges regarding accuracy and student engagement. While Gen AI cannot independently specify requirements without hallucinating or overstepping scope, it can serve as a powerful partner in a hybrid intelligence workflow. In this paper, we address the challenge of translating high-level motivational models into detailed user stories, a process that is traditionally labour-intensive for novices. We introduce a structured, human-in-the-loop workflow that uses Gen AI to refine and polish user stories while strictly preserving student agency. By grounding the output from Gen AI in a validated motivational model, the workflow minimises the risk of metacognitive offloading, requiring students to actively critique and validate the initially generated requirements. Our analysis of instructional artefacts demonstrates that Gen AI helps in three ways: suggesting structural improvements, offering alternative professional phrasing, and enhancing readability. However, we also identify risks of intent drift and scope expansion, reinforcing the need for rigorous human oversight. The findings advocate for a pedagogical approach where the Gen AI system acts as a reflective assistant rather than an autonomous generator. Full article
(This article belongs to the Special Issue Using Generative Artificial Intelligence Within Software Engineering)
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17 pages, 1377 KB  
Article
GemSP: An Ensemble Model for User Story Point Estimation Using Gemini Embeddings
by Imad Moufidi, Safaa Achour and Mohammed Benattou
Information 2026, 17(1), 110; https://doi.org/10.3390/info17010110 - 22 Jan 2026
Viewed by 388
Abstract
Accurately estimating story points in Agile Scrum environments remains a challenging task, as traditional models often struggle to capture the complex relationships between user stories and their corresponding effort estimations. In this study, we leverage Gemini’s embedding representations to enhance the modeling of [...] Read more.
Accurately estimating story points in Agile Scrum environments remains a challenging task, as traditional models often struggle to capture the complex relationships between user stories and their corresponding effort estimations. In this study, we leverage Gemini’s embedding representations to enhance the modeling of user stories within a story point estimation dataset. To improve prediction performance, we propose GemSP, an ensemble regression model that integrates two complementary regression techniques applied to the Gemini embeddings. Our approach aims to exploit the rich semantic representations of user stories while benefiting from the robustness of ensemble learning. Experimental results show that, when instantiated with Gemini embeddings, the proposed GemSP framework achieves lower prediction error than selected baseline models (GPT-2, Deep-SE, and GPT2SP) under cross-project evaluation on JIRA datasets. These results illustrate the practical benefit of decoupling semantic representation learning from regression, enabling effective integration of stronger embedding models within lightweight ensemble predictors. Full article
(This article belongs to the Special Issue Using Generative Artificial Intelligence Within Software Engineering)
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19 pages, 1733 KB  
Article
Integrating Model-Driven Engineering and Large Language Models for Test Scenario Generation for Smart Contracts
by Issam Al-Azzoni, Saqib Iqbal, Taymour Al Ashkar and Zobia Erum
Information 2026, 17(1), 1; https://doi.org/10.3390/info17010001 - 19 Dec 2025
Viewed by 948
Abstract
Large Language Models (LLMs) have demonstrated significant potential in transforming software testing by automating tasks such as test case generation. In this work, we explore the integration of LLMs within a Model-Driven Engineering (MDE) approach to enhance the automation of test case generation [...] Read more.
Large Language Models (LLMs) have demonstrated significant potential in transforming software testing by automating tasks such as test case generation. In this work, we explore the integration of LLMs within a Model-Driven Engineering (MDE) approach to enhance the automation of test case generation for smart contracts. Our focus lies in the use of Role-Based Access Control (RBAC) models as formal specifications that guide the generation of test scenarios. By leveraging LLMs’ ability to interpret both natural language and model artifacts, we enable the derivation of model-based test cases that align with specified access control policies. These test cases are subsequently translated into executable code in Digital Asset Modeling Language (DAML) targeting blockchain-based smart contract platforms. Building on prior research that established a complete MDE pipeline for DAML smart contract development, we extend the framework with LLM-supported test automation capabilities and implement the necessary tooling to support this integration. Our evaluation demonstrates the feasibility of using LLMs in this context, highlighting their potential to improve testing coverage, reduce manual effort, and ensure conformance with access control specifications in smart contract systems. Full article
(This article belongs to the Special Issue Using Generative Artificial Intelligence Within Software Engineering)
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32 pages, 2305 KB  
Article
SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development
by Yassine Ait Hsain, Naziha Laaz and Samir Mbarki
Information 2025, 16(10), 870; https://doi.org/10.3390/info16100870 - 7 Oct 2025
Cited by 1 | Viewed by 1414
Abstract
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and [...] Read more.
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering. Full article
(This article belongs to the Special Issue Using Generative Artificial Intelligence Within Software Engineering)
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