Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering

• URL: http://arxiv.org/abs/2506.17937v1
• Date: Sun, 22 Jun 2025 08:09:25 GMT
• Title: Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering
• Authors: Tommi Mikkonen, Antero Taivalsaari,
• Abstract summary: 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.
• Score: 2.7808182112731537
• 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. Consequently, earlier software reuse practices and methods are rapidly being replaced by AI-assisted 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 in the context of emerging "AI native" software engineering, bring forth relevant questions, and define a tentative research agenda and call to action for tackling some of the central issues associated with this approach.

Related papers

• Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey [1.4513830934124627]
  Explainable Artificial Intelligence (XAI) has emerged to address the black-box problem of making AI systems more interpretable and transparent.<n>This paper presents the first comprehensive survey of XAI techniques for every phase of the Software Development Life Cycle (SDLC)
  arXiv  Detail & Related papers  (2025-05-11T17:09:57Z)
• Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
  We discuss progress in AI for software engineering in threefold manner.<n>First, we provide a structured taxonomy of concrete tasks in AI for software engineering.<n>Second, we outline several key bottlenecks that limit current approaches.
  arXiv  Detail & Related papers  (2025-03-28T17:17:57Z)
• 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)
• Next-Gen Software Engineering. Big Models for AI-Augmented Model-Driven Software Engineering [0.0]
  The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, AI4SE.<n>A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development.
  arXiv  Detail & Related papers  (2024-09-26T16:49:57Z)
• 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)
• 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)
• 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)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• 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.