An Empirical Study of Generative AI Adoption in Software Engineering
- URL: http://arxiv.org/abs/2512.23327v1
- Date: Mon, 29 Dec 2025 09:24:52 GMT
- Title: An Empirical Study of Generative AI Adoption in Software Engineering
- Authors: Görkem Giray, Onur Demirörs, Marcos Kalinowski, Daniel Mendez,
- Abstract summary: GenAI tools are being increasingly adopted by practitioners in SE.<n>Despite increasing adoption, we still lack empirical evidence on how GenAI is used in practice.
- Score: 2.3419132746983236
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context. GenAI tools are being increasingly adopted by practitioners in SE, promising support for several SE activities. Despite increasing adoption, we still lack empirical evidence on how GenAI is used in practice, the benefits it provides, the challenges it introduces, and its broader organizational and societal implications. Objective. This study aims to provide an overview of the status of GenAI adoption in SE. It investigates the status of GenAI adoption, associated benefits and challenges, institutionalization of tools and techniques, and anticipated long term impacts on SE professionals and the community. Results. The results indicate a wide adoption of GenAI tools and how they are deeply integrated into daily SE work, particularly for implementation, verification and validation, personal assistance, and maintenance-related tasks. Practitioners report substantial benefits, most notably reduction in cycle time, quality improvements, enhanced support in knowledge work, and productivity gains. However, objective measurement of productivity and quality remains limited in practice. Significant challenges persist, including incorrect or unreliable outputs, prompt engineering difficulties, validation overhead, security and privacy concerns, and risks of overreliance. Institutionalization of tools and techniques seems to be common, but it varies considerably, with a strong focus on tool access and less emphasis on training and governance. Practitioners expect GenAI to redefine rather than replace their roles, while expressing moderate concern about job market contraction and skill shifts.
Related papers
- Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study [5.9568322124195845]
Generative Artificial Intelligence (GenAI) tools represent a paradigm shift in software engineering.<n>This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams.
arXiv Detail & Related papers (2026-02-14T13:26:16Z) - Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study [9.442926409509038]
Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers.<n>While adoption rates in the industry are rising, the underlying factors influencing the effective use of these tools have not been thoroughly investigated.<n>This issue is particularly relevant in environments with stringent regulatory requirements, such as Germany.<n>No empirical study has systematically examined the adoption dynamics of GenAI tools within the German context.
arXiv Detail & Related papers (2026-01-23T12:42:33Z) - Between Policy and Practice: GenAI Adoption in Agile Software Development Teams [3.4768202202649783]
generative AI (GenAI) tools have begun to reshape various software engineering activities.<n>This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts.
arXiv Detail & Related papers (2026-01-11T20:04:56Z) - On the Role and Impact of GenAI Tools in Software Engineering Education [2.867517731896504]
generative AI (GenAI) tools like ChatGPT and GitHub Copilot have transformed how software is learned and written.<n>In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning.<n>This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences.
arXiv Detail & Related papers (2025-12-03T20:51:16Z) - What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI [18.1243411839447]
We developed a theoretical model of factors influencing trust and adoption intentions towards genAI.<n>We found that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust.<n>We provide suggestions to guide future genAI tool design for effective, trustworthy, and inclusive human-genAI interactions.
arXiv Detail & Related papers (2025-05-23T03:05:56Z) - From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models [44.99833362998488]
We investigated the first steps for optimizing content creation for advanced math.<n>We looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content.
arXiv Detail & Related papers (2025-05-17T08:30:10Z) - Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations [55.2480439325792]
Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries.<n>However, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments.<n>This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises.
arXiv Detail & Related papers (2025-05-09T07:41:33Z) - AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
arXiv Detail & Related papers (2025-02-24T02:11:52Z) - Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot [42.558423984270135]
GenAI can be applied across numerous fields, with particular relevance in cybersecurity.<n>In this paper, we have analyzed the potential of leading generic-purpose GenAI tools.<n>Claude Opus, GPT-4 from ChatGPT, and Copilot-in augmenting the penetration testing process as defined by the Penetration Testing Execution Standard (PTES)
arXiv Detail & Related papers (2025-01-12T22:48:37Z) - Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI [41.96102438774773]
This work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools.
We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI.
arXiv Detail & Related papers (2024-10-20T18:44:45Z) - Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E)
We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
arXiv Detail & Related papers (2024-07-09T23:13:26Z) - Some things never change: how far generative AI can really change software engineering practice [5.17110203660516]
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities.
We performed a survey with SE practitioners to identify their expectations regarding GenAI in SE.
Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change.
arXiv Detail & Related papers (2024-06-14T05:26:42Z) - Higher education assessment practice in the era of generative AI tools [0.37282630026096586]
This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines.
Our findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills.
Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.
arXiv Detail & Related papers (2024-04-01T10:43:50Z) - Explainable Authorship Identification in Cultural Heritage Applications:
Analysis of a New Perspective [48.031678295495574]
We explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId.
In particular, we assess the relative merits of three different types of XAI techniques on three different AId tasks.
Our analysis shows that, while these techniques make important first steps towards explainable Authorship Identification, more work remains to be done.
arXiv Detail & Related papers (2023-11-03T20:51:15Z)
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.