On the Future of Software Reuse in the Era of AI Native Software Engineering
- URL: http://arxiv.org/abs/2508.19834v1
- Date: Wed, 27 Aug 2025 12:38:33 GMT
- Title: On the Future of Software Reuse in the Era of AI Native Software Engineering
- Authors: Antero Taivalsaari, Tommi Mikkonen, Cesare Pautasso,
- Abstract summary: We discuss the implications of AI-assisted generative software reuse, bring forth relevant questions, and define a research agenda for tackling the central issues associated with this emerging approach.
- Score: 1.4310278966797794
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software development is currently under a paradigm shift in which artificial intelligence and generative software reuse are taking the center stage in software creation. Earlier opportunistic software reuse practices and organic software development methods are rapidly being replaced by "AI Native" approaches in which developers place their trust on code that has been generated by artificial intelligence. This is leading to a new form of software reuse that is conceptually not all that different from cargo cult development. In this paper we discuss the implications of AI-assisted generative software reuse, bring forth relevant questions, and define a research agenda for tackling the central issues associated with this emerging approach.
Related papers
- A Viable Paradigm of Software Automation: Iterative End-to-End Automated Software Development [41.295627885484855]
We present a vision of an iterative end-to-end automated software development paradigm AutoSW.<n>It operates in an analyze-plan-implement-deliver loop, where AI systems as human partners become first-class actors.<n>The results indicate that AutoSW can successfully deliver executable software.
arXiv Detail & Related papers (2025-11-19T09:57:49Z) - Lost in Code Generation: Reimagining the Role of Software Models in AI-driven Software Engineering [1.3124479769761592]
We argue that this shift motivates a reimagining of software models.<n>Rather than serving only as upfront blueprints, models can be recovered post-hoc from AI-generated code.<n>In this role, models serve as mediators between human intent, AI generation, and long-term system evolution.
arXiv Detail & Related papers (2025-11-04T11:03:31Z) - AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation [6.38492008798679]
Current AI functions primarily as assistants to human developers.<n>Can AI move beyond its role as an assistant to become a core component of software?<n>We introduce AI-Driven Self-Evolving Software, a new form of software that evolves continuously through direct interaction with users.
arXiv Detail & Related papers (2025-10-01T07:17:51Z) - Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering [2.7808182112731537]
We discuss the implications of AI-assisted generative software reuse in the context of emerging "AI native" software engineering.<n>This paper defines a tentative research agenda and call to action for tackling some of the central issues associated with this approach.
arXiv Detail & Related papers (2025-06-22T08:09:25Z) - Generative AI and Empirical Software Engineering: A Paradigm Shift [8.65285948382426]
The widespread adoption of generative AI in software engineering marks a paradigm shift.<n>This paper examines how integrating AI into software engineering challenges traditional research paradigms.
arXiv Detail & Related papers (2025-02-12T04:13:07Z) - AI's Impact on Traditional Software Development [0.0]
The application of artificial intelligence (AI) has brought key shifts in conventional tactical software development.<n>This paper examines the technical aspect of integrating AI into prior traditional software development life cycle methodologies.
arXiv Detail & Related papers (2025-02-05T14:58:09Z) - Contemporary Software Modernization: Perspectives and Challenges to Deal with Legacy Systems [48.33168695898682]
"Software modernization" emerged as a research topic in the early 2000s.
Despite the large amount of work available in the literature, there are significant limitations.
arXiv Detail & Related papers (2024-07-04T15:49:52Z) - Innovating for Tomorrow: The Convergence of SE and Green AI [2.013374581642707]
Machine learning is changing the frontiers of existing software engineering processes.
We reflect on the impact of adopting environmentally friendly practices to create AI-enabled software systems.
arXiv Detail & Related papers (2024-06-26T07:47:04Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - Embedded Software Development with Digital Twins: Specific Requirements
for Small and Medium-Sized Enterprises [55.57032418885258]
Digital twins have the potential for cost-effective software development and maintenance strategies.
We interviewed SMEs about their current development processes.
First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
arXiv Detail & Related papers (2023-09-17T08:56:36Z) - Using Machine Learning To Identify Software Weaknesses From Software
Requirement Specifications [49.1574468325115]
This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications.
Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
arXiv Detail & Related papers (2023-08-10T13:19:10Z) - ChatDev: Communicative Agents for Software Development [84.90400377131962]
ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate.
These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
arXiv Detail & Related papers (2023-07-16T02:11:34Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.