Navigating the Complexity of Generative AI Adoption in Software Engineering

• URL: http://arxiv.org/abs/2307.06081v2
• Date: Thu, 4 Jan 2024 07:41:29 GMT
• Title: Navigating the Complexity of Generative AI Adoption in Software Engineering
• Authors: Daniel Russo
• Abstract summary: The adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed.
• Score: 6.190511747986327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, the adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed using a mixed-methods approach for an extensive comprehension of AI adoption. An initial structured interview was conducted with 100 software engineers, employing the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theories. A theoretical model named the Human-AI Collaboration and Adaptation Framework (HACAF) was deduced using the Gioia Methodology, characterizing AI adoption in software engineering. This model's validity was subsequently tested through Partial Least Squares - Structural Equation Modeling (PLS-SEM), using data collected from 183 software professionals. The results indicate that the adoption of AI tools in these early integration stages is primarily driven by their compatibility with existing development workflows. This finding counters the traditional theories of technology acceptance. Contrary to expectations, the influence of perceived usefulness, social aspects, and personal innovativeness on adoption appeared to be less significant. This paper yields significant insights for the design of future AI tools and supplies a structure for devising effective strategies for organizational implementation.

Related papers

• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Next-Gen Software Engineering: AI-Assisted Big Models [0.0]
  This paper aims to facilitate a synthesis between models and AI in software engineering. The paper provides an overview of the current status of AI-assisted software engineering. A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches.
  arXiv  Detail & Related papers  (2024-09-26T16:49:57Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review [1.6006550105523192]
  Review explores the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs) Examines both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge. Review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.
  arXiv  Detail & Related papers  (2023-10-23T09:15:18Z)
• PerfDetectiveAI -- Performance Gap Analysis and Recommendation in Software Applications [0.0]
  PerfDetectiveAI, a conceptual framework for performance gap analysis and suggestion in software applications is introduced in this research. Modern machine learning (ML) and artificial intelligence (AI) techniques are used in PerfDetectiveAI to monitor performance measurements and identify areas of underperformance in software applications.
  arXiv  Detail & Related papers  (2023-06-11T02:53:04Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process [0.8481798330936974]
  This paper proposes a theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business technology approach for the agile co-creation process. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI.
  arXiv  Detail & Related papers  (2022-09-15T06:24:01Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

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.