Jul 2026· Revista Multidisciplinar Epistemología de las Ciencias· Vol 3, pp. 159-186· 0 citations· 5 references
TL;DR
The findings confirm the research hypothesis that AI can reliably produce key software development artifacts with minimal human input, marking a pivotal step toward the broader integration of AI in software engineering practices.
Abstract
This article explores the practical application of AI tool (ChatGPT v3.5 and 4) in the full lifecycle of a real-world software development project, based on field research of 2023-2024 in Europe. The research evaluates the effectiveness of AI in generating core software artifacts, including functional specifications, software code, automated tests, user documentation, and application content. While the AI tool proved to be a powerful assistant for creating and refining these deliverables, its limitations became evident in areas requiring up-to-date technical guidance or creative input in product business logic development. In general, ChatGPT significantly accelerated the development process, with project team members reporting a substantial increase in productivity. The study also highlights best practices for AI usage in software engineering, emphasizing the importance of service-oriented design, iterative prompt refinement, and ongoing human oversight. Despite its shortcomings in generating original ideas or adapting to evolving platform requirements, ChatGPT demonstrated strong capabilities in automating repetitive tasks and enhancing overall efficiency. The findings confirm the research hypothesis that AI can reliably produce key software development artifacts with minimal human input, marking a pivotal step toward the broader integration of AI in software engineering practices.
Algorithm-Driven Development is introduced, a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior that provides systematic coverage of functional scenarios from the outset of development.
Philippe Jawish, Pierre Evrard, Alexandre Lemerle et al.· Journal of Systems and Softw...· 0 citations
In recent years, it has become common for generative AI-based software development tools to be experimented with, adopted, and used in professional software development. To understand the effects of this generative AI transformation in the Finnish context, we conducted a survey targeting software professionals working in Finland. 163 survey responses were collected during two months, from December 2025 to February 2026. In this paper, we report a qualitative analysis of 100 respondents' perspectives on the future role of generative AI in professional software development, based on the responses to two optional open-ended questions in the survey. The results offer a practitioner-centred snapshot of how software professionals currently reason about the effects of generative AI on roles, skills, productivity, software quality, and the sustainability of expertise in the field.
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This article discusses the transition from a code-centered paradigm to Specification-Driven Development. We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems. Instead, it shifts this complexity toward domain understanding, requirements elicitation, specification development, validation, maintenance, and software evolution. Building on this perspective, we discuss the renewed centrality of Requirements Engineering, considering its implications for productivity and software quality, as well as risks associated with automation bias, ambiguity propagation, Specification Overfitting, and the accumulation of Specification Debt. Finally, we propose the Specification Paradox: the more capable artificial intelligence systems become at automatically generating software, the greater the dependence on correct, complete, verifiable, and explainable human-produced specifications. We conclude that the future of Software Engineering will depend not only on machines'ability to generate code, but also on humans'ability to correctly specify, evaluate, and evolve what is intended to be built.
The paper is trying to deeply analyze the pros and cons of implementing GenAI into software development, analysing current applications used in software development life cycle (SDLC), drawing upon case studies and programmer experience, and examining effect on code quality, team working and project timeline.
P. Arun, Jagdale, Deepti Ameta et al.· Economic Sciences· 0 citations
Understanding large, complex software architectures is difficult and time-consuming. Software architecture recovery aims to extract explanatory artefacts from code, but existing methods often lack generalisability. To address this, we previously proposed Deductive Software Architecture Recovery (DSAR) to extend current techniques. This paper presents a qualitative evaluation of DSAR in an industrial setting. Using a large language model-assisted prototype, we examined the recovered architecture's content quality, presentation quality, and usefulness for understanding and maintaining real-world systems. To ensure practical relevance, we conducted an in-depth judgement study with software engineering teams at ASML applying DSAR to 17 Java repositories across diverse teams. We contribute: an empirical assessment indicating that DSAR supports comprehension, architectural reasoning, and communication; qualitative evidence on the strengths and limitations of the generated explanatory artefacts; and recommendations for practitioners and researchers on adopting and further developing DSAR for architectural sense-making.
S. A. Rukmono, Lina Ochoa, Thijs Bressers et al.· SIGSOFT FSE Companion· 0 citations
This paper presents the development of a software verification and validation tool focused on the Prolog logic programming language, specifically designed for educational contexts. The project arose from the lack of a functional testing framework in the course "Logic for Computer Science" at the Universidad Nacional del Sur, which affects the quality and efficiency of grading students' work. The tool facilitates the automated creation and execution of test cases, allowing teachers to save time in validating students' programs. It features a user-friendly graphical interface and a console mode, following the Model-View-Presenter architecture to ensure clear separation of responsibilities and ease of maintenance. Additionally, it allows for the storage and comparison of the obtained results with the expected ones in structured formats such as JSON. The implementation has shown key benefits, significantly reducing evaluation time and providing detailed feedback to students. The developed tool is distributed freely.
Nicolas M. Leidi, Andrea Cohen, Luciano H. Tamargo et al.· Revista Iberoamericana de Te...· 0 citations