Navigating the Complexity of Generative AI Adoption in Software Engineering

Russo, Daniel

doi:10.1145/3652154

Cited by 221Technology Adoption and User BehaviourSoftware Engineering Techniques and PracticesSoftware Engineering Research

Navigating the Complexity of Generative AI Adoption in Software Engineering

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Russo, Daniel·2024·ACM Transactions on Software Engineering and Methodology

DOI 10.1145/3652154

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Abstract

This article explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model, the Diffusion of Innovation Theory, and the Social Cognitive Theory as guiding theoretical frameworks. Employing the Gioia methodology, we derived a theoretical model of AI adoption in software engineering: the Human-AI Collaboration and Adaptation Framework. This model was then validated using Partial Least Squares–Structural Equation Modeling based on data from 183 software engineers. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.

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The HACAF model integrates components from TAM, DOI, UTAUT, Social Cognitive Theory, and the personal innovativeness construct.
“It is a theoretical model that integrates components from several established theories, including TAM, DOI, UTAUT, Social Cognitive Theory (SCT), and the personal innovativeness construct.”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [13]
GenAI adoption is still in its early stages.
“its adoption is still in its early stages”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [13]
Russo's HACAF pinpoints that there are several levels connected to the adoption of GenAI tools in organizations.
“Russo's model [13], the Human-AI Collaboration and Adaptation Framework (HACAF), pinpoints that there are several levels connected to the adoption of GenAI tools in organizations.”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [13]
Integrating GenAI into professional workflows involves complexities across individual, technological, and social dimensions.
“Our investigation, guided by the Human-AI Collaboration and Adaptation Framework (HACAF) [13], highlights the complexities of integrating GenAI into professional workflows across individual, technological, and social dimensions.”
From Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT (2025) · cites this paper as [13]
Multiple-choice questions were constructed based on existing literature on developers' behaviors, benefits, challenges, risks, and security factors.
“To construct the multiple-choice questions, we relied on the existing literature on developers' behaviors, potential benefits, challenges, and risks associated with using AI-generated code, as well as factors contributing to security [8, 34, 35, 42, 45].”
From My Code Is Less Secure with Gen AI: Surveying Developers' Perceptions of the Impact of Code Generation Tools on Security (2025) · cites this paper as [34]

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Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2022) · arXiv · doi:10.48550/arxiv.2210.14306 · Cited by 114 · see citing works
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641936 · Cited by 114 · see citing works
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S.-C., Klein, J., & Bissyandé, T. F. (2023) · arXiv · doi:10.48550/arxiv.2304.11938 · Cited by 114 · cited 1× · see citing works
“Empirical evaluation suggests that while these models demonstrate aptitude in simpler, well-structured tasks, they tend to struggle with complex tasks involving semantic nuance [86].”
Background
A Survey of Generative AI Applications
Gozalo-Brizuela, R., & Merchan, E. E. G. (2024) · Journal of Computer Science · doi:10.3844/jcssp.2024.801.818 · Cited by 101 · see citing works
Q-TAM: A quality technology acceptance model for predicting organizational buyers’ continuance intentions for e-procurement services
Ramkumar, M., Schoenherr, T., Wagner, S. M., & Jenamani, M. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.06.003 · Cited by 96 · cited 1× · see citing works
“This result is noteworthy, especially considering that established models such as the technology acceptance model typically show a constructs’ performance range between 50% and 70% [66].”
Background
Choose your programming copilot
Sobania, D., Briesch, M., & Rothlauf, F. (2022) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3512290.3528700 · Cited by 91 · see citing works
Investigating and Designing for Trust in AI-powered Code Generation Tools
Wang, R., Cheng, R., Ford, D., & Zimmermann, T. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658984 · Cited by 87 · cited 1× · see citing works
“Our research of the literature revealed only three papers that even mention these tools, and solely within the context of related work or discussion sections [16, 42, 114].”
Background
Handbook of Moral Behavior and Development
(2014) · Psychology Press eBooks · doi:10.4324/9781315807287 · Cited by 81 · see citing works
Practices and Challenges of Using GitHub Copilot: An Empirical Study
Zhang, B., Liang, P., Zhou, X., Ahmad, A., & Waseem, M. (2023) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2023-077 · Cited by 75 · see citing works
How do i refactor this? An empirical study on refactoring trends and topics in Stack Overflow
Peruma, A., Simmons, S., AlOmar, E. A., Newman, C. D., Mkaouer, M. W., & Ouni, A. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-10045-x · Cited by 70 · cited 1× · see citing works
“This finding aligns with the TAM’s concept of perceived usefulness, as using LLMs for code refactoring, adherence to design patterns, and implementation of SOLID principles can enhance software quality and maintainability [63].”
Method
Do you Really Code? Designing and Evaluating Screening Questions for Online Surveys with Programmers
Danilova, A., Naiakshina, A., Horstmann, S. A., & Smith, M. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00057 · Cited by 63 · cited 1× · see citing works
“[23], asking “Which of these websites do you most frequently use as aid when programming?” with ‘Stack Overflow’ as the correct question.”
Background
The Daily Life of Software Engineers During the COVID-19 Pandemic
Russo, D., Hanel, P. H. P., Altnickel, S., & van Berkel, N. (2021) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00048 · Cited by 63 · see citing works
Self-citation
AI-Driven Development Is Here: Should You Worry?
Ernst, N. A., & Bavota, G. (2022) · IEEE Software · doi:10.1109/ms.2021.3133805 · Cited by 62 · cited 2× · see citing works
“Ernst and Bavota [28], although also discussing the complexities of AI integration, differ by highlighting additional challenges related to legal compliance and bias.”
Background
The Agile Success Model
Russo, D. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3464938 · Cited by 60 · see citing works
Self-citation
Gender Differences in Personality Traits of Software Engineers
Russo, D., & Stol, K.-J. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3003413 · Cited by 59 · see citing works
Self-citation
The Next Frontier in Software Development: AI-Augmented Software Development Processes
Özkaya, İ. (2023) · IEEE Software · doi:10.1109/ms.2023.3278056 · Cited by 52 · see citing works
"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools
Cheng, R., Wang, R., Zimmermann, T., & Ford, D. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3651990 · Cited by 47 · cited 1× · see citing works
“’s [14] exploration of community influence on developers’ trust.”
Background
An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and ApplicationsPreprint
Garrido-Merchán, E. C., & López, Á. L. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.02781 · Cited by 32 · cited 1× · see citing works
“This comparative analysis provides a unique perspective on the landscape of automatic programming methodologies.”
Background
A Meta-Model for Information Systems Quality
Russo, D., Ciancarini, P., Falasconi, T., & Tomasi, M. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3230713 · Cited by 25 · see citing works
Self-citation
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2022) · arXiv · doi:10.48550/arxiv.2206.15000 · Cited by 23 · cited 1× · see citing works
“Also, developers exhibit distinct interaction modes, each requiring different forms of tool support [9].”
Background
The Cybersecurity Crisis of Artificial Intelligence: Unrestrained Adoption and Natural Language-Based AttacksPreprint
Tsamados, A., Floridi, L., & Taddeo, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4578165 · Cited by 22 · see citing works
What do employees want? Halting record-setting turnovers globally
Tenakwah, E. S. (2021) · Strategic HR Review · doi:10.1108/shr-08-2021-0040 · Cited by 17 · cited 1× · see citing works
“To mitigate this challenge, organizations should communicate the benefits of LLMs and focus on upskilling and reskilling employees [12, 101].”
Background
Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems
Nunes, N., Martinie, C., Silva, J., Nichols, J., Calvary, G., Campos, P., & Campos, J. C. (2017) · doi:10.1145/3102113 · Cited by 16 · see citing works
An Empirical Study on Quality Issues of Deep Learning Platform
Gao, Y., Shi, X., Lin, H., Zhang, H., Wu, H., Li, R., & Yang, M. (2023) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip58684.2023.00052 · Cited by 6 · cited 1× · see citing works
“This discussion, grounded in the thematic analysis of the questionnaire survey data and DOI, contributes to the understanding of the factors that affect LLM adoption in the software engineering context.”
Background
A contingency model of computer and Internet self-efficacy
TorkzadehGholamreza, Cha-Jan, C., & DemirhanDidem (2006) · Information & Management · doi:10.5555/1150937.1709367 · Cited by 3 · see citing works
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2023) · arXiv · cited 1×
“For novice programmers, cognitive and metacognitive issues arise while using these tools for assignments, indicating a need for better supportive design [64].”
Background
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · cited 1×
“By incorporating the empirical evidence from the questionnaire survey statements and drawing on relevant literature, our findings contribute to the ongoing conversation about the role of LLMs in software engineering and the factors that influence their adoption.”
Method

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Sorted by citations

A SEM–ANN analysis to examine impact of artificial intelligence technologies on sustainable performance of SMEs
Soomro, R., Al-rahmi, W., Dahri, N. A., Almuqren, L., Almogren, A. S., & Aldaijy, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-86464-3 · Cited by 112 · see citing works
Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16039 · Cited by 131 · see citing works
Exploring the generative AI adoption in service industry: A mixed-method analysis
Gupta, R., & Rathore, B. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103997 · Cited by 84 · see citing works
Strategic Integration of Generative AI in Organizational Settings: Applications, Challenges, and Adoption Requirements
Al-kfairy, M. (2025) · IEEE Engineering Management Review · doi:10.1109/emr.2025.3534034 · Cited by 61 · see citing works
“Content Creation and Management [1], [10], [2], [11], [12] Design and Prototyping [13], [14] Customer Service Enhancement [15], [5], [16], [8], [1] Decision Support Systems [7], [8], [15], [6], [17] Fraud Detection [3], [18], [19], [4], [8], [20] Software Development [14] Human Resources and…”
Influential
Driving Factors of Generative AI Adoption in New Product Development Teams from a UTAUT Perspective
Xia, Y., & Chen, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375686 · Cited by 49 · see citing works
“Our finding of the strong effects of task-tool fitness also aligns with a recent study about GenAI adoption in software engineering (Russo, 2024), which suggested that the adoption of GenAI was predominantly driven by the compatibility of GenAI tools within existing development workflows.”
Not Merely Useful but Also Amusing: Impact of Perceived Usefulness and Perceived Enjoyment on the Adoption of AI-Powered Coding Assistant
Kim, Y. W., Cha, M. C., Yoon, S. H., & Lee, S. C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375701 · Cited by 38 · see citing works
“Our structural model contributes to the current literature by assessing the impact of compatibility on both perceived usefulness and perceived ease of use of AI-CAs, areas that have not been definitively explored in earlier studies (Russo, 2024).”
Influential
Software Engineering by and for Humans in an AI Era
Abrahão, S., Grundy, J., Pezzè, M., Storey, M., & Tamburri, D. A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715111 · Cited by 44 · see citing works
“It also remains to be seen if the emergence of AI-powered tools that significantly change SE work and SE job expectations enables many who previously did not embark on an SE career to do so.”
From Code Generation to Software Testing: AI Copilot With Context-Based Retrieval-Augmented Generation
Wang, Y., Guo, S., & Tan, C. W. (2025) · IEEE Software · doi:10.1109/ms.2025.3549628 · Cited by 38 · see citing works
What drives the corporate payoffs of using generative artificial intelligence?
Bughin, J. (2024) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2024.09.011 · Cited by 31 · see citing works
Leveraging Large Language Model ChatGPT for enhanced understanding of end-user emotions in social media feedbacks
Khan, N. D., Khan, J. A., Li, J., Ullah, T., & Zhao, Q. Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125524 · Cited by 28 · see citing works
How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering
Treude, C., & Gerosa, M. A. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00033 · Cited by 28 · see citing works
“Artificial intelligence (AI) is rapidly transforming software development, bringing new capabilities and efficiency to every stage of the development lifecycle [1].”
LLMs: A game-changer for software engineers?
Haque, M. A. (2025) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2025.100204 · Cited by 29 · see citing works
“However, there are several areas that still require further exploration, development, and research [71-75].”
Codepori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
Rasheed, Z., Malik, S., Kemell, K.-K., Muhammad, W., Saari, M., Systä, K., & Abrahamsson, P. (2024) · doi:10.2139/ssrn.4979510 · Cited by 27 · see citing works
Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States
Ali, M. S. M., Wasel, K. Z. A., & Abdelhamid, A. M. M. (2024) · Journalism and Media · doi:10.3390/journalmedia5040101 · Cited by 26 · see citing works
“In the engineering field, Russo (2024) found that social factors had no significant influence on GenAI usage intention.”
Global adoption of generative AI: What matters most?
Ali, H., Mustafa, A. U., & Aysan, A. F. (2024) · Journal of Economy and Technology · doi:10.1016/j.ject.2024.10.002 · Cited by 25 · see citing works
Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach
Kim, C. G. (2025) · Electronics · doi:10.3390/electronics14030530 · Cited by 24 · see citing works
The Impact of Generative AI on Creativity in Software Development: A Research Agenda
Jackson, V., Vasilescu, B., Russo, D., Ralph, P., Prikladnicki, R., Izadi, M., D’Angelo, S., Inman, S., Andrade, A., & van der Hoek, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708523 · Cited by 23 · see citing works
“…years, with a commensurate explosion in research exploring new opportunities for LLM use in support of different development tasks (e.g., coding [14], code review [28], testing [115], debugging [60]) and research studying how industry is grappling with adopting LLM driven tools (e.g., [104, 113]).”
Self-citation
The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impaired
Flores-Saviaga, C., Hanrahan, B. V., Imteyaz, K., Clarke, S., & Savage, S. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714008 · Cited by 24 · see citing works
“Building on studies of information overload for visually impaired users [3, 32, 73, 84], we extend this discussion to dynamic AI-driven environments, where proactive AI mediation can disrupt cognitive coherence [73, 84].”
Bridging Governance and Technology: Key Determinants of AI Adoption in Public Administration
Ly, B. (2025) · Chinese Political Science Review · doi:10.1007/s41111-025-00308-z · Cited by 22 · see citing works
AI in risk management: An analytical comparison between the U.S. and Nigerian banking sectors
Nnaomah, U. I., Odejide, O. A., Aderemi, S., Olutimehin, D. O., Abaku, E. A., & Orieno, O. H. (2024) · International Journal of Science and Technology Research Archive · doi:10.53771/ijstra.2024.6.1.0035 · Cited by 20 · see citing works
“AI's integration into banking processes has not only revolutionized the way financial transactions are conducted but also significantly enhanced the sector's competitiveness by fostering innovation, agility, and scalability (T. Maheswari, E. Karthika, & K.R. Anusrii, 2023).”
BackgroundMethod
Still just personal assistants? – A multiple case study of generative AI adoption in software organizations
Kemell, K., Saarikallio, M., Nguyen‐Duc, A., & Abrahamsson, P. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107805 · Cited by 20 · see citing works
Adoption of digital logistics platforms in the maritime logistics industry: based on diffusion of innovations and extended technology acceptance
Liu, Y., Zhao, S., & Zhao, S. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04969-8 · Cited by 30 · see citing works
“While complexity may hinder acceptance, its effect is moderated by industry-speci ﬁ c, cultural, and policy-related factors, leading to a constrained impact (Russo, 2024).”
Unveiling the Complexity of Designers’ Intention to Use Generative AI in Corporate Product Design: A Grounded Theory and fsQCA
Li, H., Liu, Y., Guo, Q., Shi, M., Zhang, P., & Kim, S. B. (2025) · Systems · doi:10.3390/systems13040275 · Cited by 17 · see citing works
What Guides Our Choices? Modeling Developers' Trust and Behavioral Intentions Towards Genai
Choudhuri, R., Trinkenreich, B., Pandita, R., Kalliamvakou, E., Steinmacher, I., Gerosa, M. A., Sanchez, C. A., & Sarma, A. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00087 · Cited by 22 · see citing works
Generative AI Uses and Risks for Knowledge Workers in a Science Organization
Wagman, K. B., Dearing, M. T., & Chetty, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713827 · Cited by 16 · see citing works
“Other research on generative AI applications in professional settings has largely focused on creative knowledge work in journalism and science writing [23, 38], user experience (UX) and industrial design [56], marketing and public relations [53], and software engineering [6, 42].”
Addressing Academic Dishonesty in Higher Education: A Systematic Review of Generative AI’s Impact
Sharma, R. C., & Panja, S. K. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.820 · Cited by 30 · see citing works
“The health science (Newton & Dimopoulos-Bick, 2024; Raza et al., 2024; Small et al., 2024) and engineering sector (Russo, 2024; Sundberg & Holmstorm, 2024) appear to have a positive outlook to embrace the technological movement.”
I won’t become obsolete! exploring the acceptance and use of GenAI by marketing professionals
Vitellaro, F., Schifilliti, V., Buratti, N., & Cesaroni, F. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-04-2024-0471 · Cited by 12 · see citing works
Insights from the Frontline: GenAI Utilization Among Software Engineering Students
Choudhuri, R., Ramakrishnan, A., Chatterjee, A., Trinkenreich, B., Steinmacher, I., Gerosa, M. A., & Sarma, A. (2025) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet66350.2025.00007 · Cited by 20 · see citing works
“These tools are widely used in software development as well as education [4, 5, 6].”
Assisting early-stage software startups with LLMs: Effective prompt engineering and system instruction design
Ahlgren, T. L., Sunde, H. F., Kemell, K., & Nguyen‐Duc, A. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107832 · Cited by 15 · see citing works
The State of Generative AI Adoption from Software Practitioners' Perspective: An Empirical Study
Simaremare, M. E. S., & Edison, H. (2024) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa64295.2024.00024 · Cited by 15 · see citing works
“Finding the applicable use cases for GenAI plays a critical role in its adoption [8].”
Influential
Generative AI in Agile Software Development: A Comprehensive Survey
Malladi, N. V., & Reddy, K. S. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280486 · Cited by 14 · see citing works
Impacts of the Usage of Generative Artificial Intelligence on Software Development Process
Santos, P. D. O., Figueiredo, A. C., Moura, P., Diirr, B., Alvim, A. C. F., & dos Santos, R. P. (2024) · Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI) · doi:10.1145/3658271.3658337 · Cited by 14 · see citing works
“…usam o modelo GPT-4 [14] e alguns estudos [14, 20] indicam que a produtividade no desenvolvimento de software pode ser impactada positivamente pela integração de ferramentas de IA, sendo previsto um aumento de produtividade que pode implicar redução de custos funcionais variando de 30% a 45% [38].”
Background
When ease becomes a barrier: What influences student intentions to use Generative AI in higher education?
Siu, B., & White, J. E. (2025) · British Educational Research Journal · doi:10.1002/berj.4206 · Cited by 13 · see citing works
An Empirical Study of AI Financial Advisor Adoption Through Technology Vulnerabilities in the Financial Context
Wang, Z., Yuan, R., Li, B., Kumar, V., & Kumar, A. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12795 · Cited by 14 · see citing works
Resilient by Design: Exploring the Social Abilities and Actor‐Network Roles of Artificial Intelligence in Supply Chain Management
Condé, L., & Münch, C. (2025) · Journal of Business Logistics · doi:10.1111/jbl.70032 · Cited by 17 · see citing works
Leveraging Generative AI for sustainable supply chain: adoption challenges and strategic insights
Maghroor, H. R., Madanchi, F., & O’Neal, T. (2025) · Production Planning & Control · doi:10.1080/09537287.2025.2561967 · Cited by 15 · see citing works
A Comparative Study on AI-Based Learning Behaviors: Evidence from Vietnam
Duong, N. T., Thi, T. D. P., & Pham, V. K. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2430433 · Cited by 17 · see citing works
“In the adoption of AI-based tools, personal cognition and motivation related to work significantly influence future performance (Russo, 2024).”
Transforming Leadership
Ali, Q., & Mahmood, M. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8960-7.ch009 · Cited by 14 · see citing works
Exploring trust and social cognition in the adoption of Metaverse-based museums
Al-kfairy, M. (2025) · Kybernetes · doi:10.1108/k-12-2024-3381 · Cited by 6 · see citing works
The Evolution of Information Seeking in Software Development: Understanding the Role and Impact of AI Assistants
Al Haque, E., Brown, C., LaToza, T. D., & Johnson, B. (2025) · Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3696630.3731665 · Cited by 9 · see citing works
My Code Is Less Secure with Gen AI: Surveying Developers' Perceptions of the Impact of Code Generation Tools on Security
Kudriavtseva, A., Hotak, N. A., & Gadyatskaya, O. (2025) · Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing · doi:10.1145/3672608.3707778 · Cited by 9 · see citing works
“5 https://www.qualtrics.com/ To construct the multiple-choice questions, we relied on the existing literature on developers’ behaviors, potential benefits, challenges, and risks associated with using AI-generated code, as well as factors contributing to security [8, 34, 35, 42, 45].”
Auditor’s willingness to learn and its effect on the intention to use AI technologies in the audit process: evidence from emerging economies
Mansour, E. M., Al-Zyod, L., Ghassab, E. E., & Alaqrabawi, M. (2025) · Journal of financial reporting & accounting · doi:10.1108/jfra-08-2024-0508 · Cited by 9 · see citing works
University Students' Perception and Expectations of Generative AI Tools for Software Engineering
Yabaku, M., & Ouhbi, S. (2024) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet62301.2024.10663035 · Cited by 8 · see citing works
“Russo [12] highlighted AI technology adoption among engineers.”
Background
Pandemic pedagogy: Evaluating remote education strategies during COVID-19
Russo, D. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112392 · Cited by 8 · see citing works
Self-citation
Revolutionizing Newcomers’ Onboarding Process in OSS Communities: The Future AI Mentor
Tan, X. L., Long, X., Zhu, Y., Shi, L., Lian, X., & Zhang, L. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3715767 · Cited by 8 · see citing works
“This model has been widely applied in the SE literature [41, 50, 52, 59].”
Befriend GenAI Chatbots: Exploring the Determinants of Parasocial Relationship in human-AI Interaction Through a Hybrid PLS-ANN-NCA Approach
Zhong, W., & Luo, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2537794 · Cited by 9 · see citing works
“Previous research confirms that perceived adaptability in AI systems significantly influences user satisfaction and technology adoption by making interactions feel more relevant and personal (Ait Baha et al., 2023; Murtaza et al., 2022; Ruangkanjanases et al., 2024; Russo, 2024).”
Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers' Use of GitHub Copilot and ChatGPT
Stray, V., Barbala, A., & Wivestad, V. T. (2025) · Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3696630.3730566 · Cited by 7 · see citing works
“Our investigation, guided by the Human-AI Collaboration and Adaptation Framework (HACAF) [13], highlights the complexities of integrating GenAI into professional workflows across individual, technological, and social dimensions.”
Influential
Why does Automation Adoption in Organizations Remain a Fallacy?: Scrutinizing Practitioners' Imaginaries in an International Airport
Gomez-Beldarrain, G., Verma, H., Kim, E., & Bozzon, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713978 · Cited by 8 · see citing works
“In the field of automation and AI, adoption has been examined in empirical studies that were held in various types of organizations, including healthcare contexts [31, 94], public administration [56, 60], aviation [32], or software engineering [79].”
Influential
Emerging Data Practices: Data Work in the Era of Large Language Models
Garcia, A. A., Candello, H. C. D. S. P., Badillo-Urquiola, K. A., & Wong-Villacrés, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714069 · Cited by 8 · see citing works
“Similar to other IT companies, recently, the company shifted its workforce focus to LLM development, requiring employees to adopt new platforms and tools, learn new skills, and transform how they delivered their work [30, 106, 110, 146].”
Linkage mechanism of antecedents for employees' continuous adoption of artificial intelligence virtual assistants
Zhang, X., & Hao, X.-L. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124317 · Cited by 9 · see citing works
Exploring GenAI in Software Development: Insights from a Case Study in a Large Brazilian Company
Pereira, G. V., Jackson, V., Prikladnicki, R., van der Hoek, A., Fortes, L., Araújo, C., Coelho, A., Chelli, L., & Ramos, D. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00035 · Cited by 7 · see citing works
“These tools can assist in all phases of the software development lifecycle, from system analysis and design to maintenance [3].”
Development and validation of the generative AI engagement scale
Zhang, D., Tan, J. Y., Chew, Y. Y., Hew, L., & Choo, J. Y. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100221 · Cited by 12 · see citing works
High interest but low adoption: Navigating organizations’ journey towards generative artificial intelligence implementation
Wang, X., Zhong, W., Huang, K., & Liang, B. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.103009 · Cited by 7 · see citing works
Human–AI collaboration in operations and supply chain management: a systematic literature review
Kumar, N., & Kumar, R. (2025) · Management Review Quarterly · doi:10.1007/s11301-025-00575-9 · Cited by 7 · see citing works
Exploring AI Integration in Software Development: Case Studies and Insights
Udoidiok, I., Reza, H., & Zhang, J. (2024) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon61878.2024.10670347 · Cited by 7 · see citing works
“Additionally, integrating AI tools into existing SDE and workflows can be complex due to compatibility issues and varying technology stacks [15].”
The Role of Generative AI Tools in Application Development: A Comprehensive Review of Current Technologies and Practices
Donvir, A., Panyam, S., Paliwal, G., & Gujar, P. (2024) · International Conference on Engineering Management of Communication and Technology (EMCTECH) · doi:10.1109/emctech63049.2024.10741797 · Cited by 7 · see citing works
“Similarly, the article Navigating the Complexity of Generative AI Adoption in Software Engineering [6] explores the dynamics of adopting GenAI tools in software engineering.”
Method
Managing expectations towards AI tools for software development: a multiple-case study
Jensen, V. V., Alami, A., Bruun, A., & Persson, J. S. (2025) · Information Systems and e-Business Management · doi:10.1007/s10257-025-00704-7 · Cited by 7 · see citing works
“Presenting a theoretical framework on AI adoption among software developers, Russo (2024, 37) found that compatibility was central to AI adoption, and that positive perceptions about AI’s technical capabilities were key to establishing compatibility.”
Influential
Adoption and Adaptation of Generative Artificial Intelligence in Organizations: Actions for Efficient and Responsible Use in Interaction with Collaborators
Tecnológico Nacional de México – TES de Coacalco, M., & Hernández, N. C. (2024) · International Journal of Current Science Research and Review · doi:10.47191/ijcsrr/v7-i3-56 · Cited by 6 · see citing works
“This is because the personal willingness to adopt GenAI tools, as well as the support provided by an organization for their implementation, have been identified as factors that positively influence the perception of technology and self-effectiveness in the adoption process for work activities [12].”
Background
The role of firm AI capabilities in generative AI-pair coding
Bughin, J. (2024) · Journal of Decision Systems · doi:10.1080/12460125.2024.2428187 · Cited by 6 · see citing works
Challenges and Opportunities for Generative AI in Software Engineering: A Managerial View
Rico, S., & Öberg, L. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728718 · Cited by 6 · see citing works
“These technological developments offer new opportunities to auto-mate tasks, boost productivity, and enhance software quality [3–5].”
Influential
Carbon-Conscious Intelligence: Life Cycle Assessment and Green Standards for Generative AI
Malik, F. (2025) · doi:10.38124/ijisrt/25aug585 · Cited by 6 · see citing works
Do comments and expertise still matter? An experiment on programmers’ adoption of AI-generated JavaScript code
Li, C., Treude, C., & Turel, O. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112634 · Cited by 6 · see citing works
“Research has demonstrated that programmers adopt AI coding tools because they trust that AI coding tools could increase productivity [10, 8].”
Influential
Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code
Porta, A. D., Lambiase, S., & Palomba, F. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3756681.3756938 · Cited by 19 · see citing works
CSLLM: Code-Specific Large Language Models—A Survey
Umre, J., Parihar, A. S., & Gupta, A. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130991 · Cited by 6 · see citing works
Who uses general-purpose AI? A typology of ChatGPT early adopters
Gerling, C., Teubner, T., & Braesemann, F. (2026) · Electronic Markets · doi:10.1007/s12525-025-00853-0 · Cited by 6 · see citing works
The New Developer: AI Skill Threat, Identity Change & Developer Thriving in the Transition to AI-Assisted Software DevelopmentPreprint
Hicks, C. M., Lee, C. S., & Foster-Marks, K. L. (2024) · doi:10.31234/osf.io/2gej5 · Cited by 6 · see citing works
What is Generative AI good for? Introduction to the special issue on Generative AI in software engineering
Stray, V. G., Hanssen, G. K., Barbala, A., Šmite, D., & Stol, K.-J. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107857 · Cited by 6 · see citing works
Pathways to digital entrepreneurship adoption among Malaysian SMEs: Analysing the determinants and technology readiness dynamics
Sangosanya, T. A., Salleh, S. S. M. M., & Saraih, U. N. (2025) · Journal of International Entrepreneurship · doi:10.1007/s10843-025-00403-x · Cited by 5 · see citing works
“Prior studies have highlighted that CX can obstruct technology adoption; for instance, perceived CX hindered cloud computing adoption among SMEs (Sutton et al. 2023), while AI adoption encountered similar issues (Russo 2024).”
Understanding Drivers and Outcomes of AI Adoption: A Theory-Elaboration Approach
Marhraoui, M. A. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3644129 · Cited by 6 · see citing works
“In software engineering, generative AI adoption can play a role in developing effective engineering implementation strategies [129].”
Leveraging the Power of LLMs to Transform Robot Programs into Low-Code
Schenkenfelder, B., Salomon, C., Schwandtner, M., Zefferer, R., Derfler, M., & Wimmer, M. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10711043 · Cited by 5 · see citing works
“Following recent advances in AI for software engineering [3], this paper explores how LLMs can be used to transform robot programs into low-code through their abstraction.”
Method
AI in Software Engineering: Perceived Roles and Their Impact on Adoption
Zakharov, I., Koshchenko, E., & Sergeyuk, A. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3730563 · Cited by 4 · see citing works
Human-Centric AI Tools Into Agile Methodologies for Optimized Software Development
Babulak, E., Kumar, A. P., Guru, D., Kudithipudi, K., Maddini, S., Gonaygunta, H., & Nadella, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4839-1.ch002 · Cited by 4 · see citing works
Evidências sobre o uso do ChatGPT no ensino de modelagem de software: um experimento controlado
Murad, S. B., Lemos, F. F. S., Melo, S. M., Paschoal, L. N., & Prates, J. M. (2025) · Anais do Workshop sobre Educação em Computação (WEI) · doi:10.5753/wei.2025.9261 · Cited by 4 · see citing works
Adoption of LLMs in Requirements Engineering: What Practitioners Are Worried About?
Santos, R., Steinmacher, I., Conte, T., Oran, A. C., & Gadelha, B. (2025) · Simpósio Brasileiro de Qualidade de Software (SBQS) · doi:10.5753/sbqs.2025.15089 · Cited by 4 · see citing works
“While Russo [19] explores AI adoption broadly in software engineering, and Zhao et al. [26] maps NLP techniques applied to requirements analysis, neither addresses the practical use of LLMs for requirements generation.”
Extension decisions in open source software ecosystem
Onagh, E., & Nayebi, M. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112552 · Cited by 4 · see citing works
“However, with the rise of generative AI and Large Language Models (LLMs) in software-related tasks (80; 81; 82; 83; 84), we opted to use LLMs for feature extraction.”
From Prompts to Properties: Rethinking LLM Code Generation with Property-Based Testing
Bose, D. B. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728702 · Cited by 4 · see citing works
Explainability as a Compliance Requirement: What Regulated Industries Need from AI Tools for Design Artifact Generation
Shah, S. T. U., Hussein, M., Barcomb, A., & Moshirpour, M. (2025) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew66121.2025.00007 · Cited by 5 · see citing works
“Broader adoption studies identify recurring issues of trust, customization, and integration challenges [27], [28], yet overlook how explainability affects artifact generation, potentially impacting safety.”
Development of an Adaptive User Support System Based on Multimodal Large Language Models
Wang, W., Li, L., Wickramathilaka, S., Grundy, J., Khalajzadeh, H., Obie, H. O., & Madugalla, A. K. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00044 · Cited by 5 · see citing works
Generative AI in Student Software Development Projects: A User Study on Experiences and Self-Assessment
Borghoff, U. M., Minas, M., & Schopp, J. (2025) · European Conference on Software Engineering Education (ECSEE) · doi:10.1145/3723010.3723012 · Cited by 4 · see citing works
The nexus between learning engagement, knowledge acquisition and valence of use: role of generative AI adoption in business context
Jiang, X., Yuan, L., Alshaghdali, N. O., Galgotia, A., Monaco, A. D., & Dell’Aversano, S. (2025) · Journal of Knowledge Management · doi:10.1108/jkm-03-2025-0375 · Cited by 3 · see citing works
Facilitating Trust in AI-assisted Software Tools
Johnson, B., Bird, C., Ford, D., Haque, E. A., Forsgren, N., & Zimmermann, T. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3772370 · Cited by 3 · see citing works
Web app performance testing in industrial contexts: Supporting workload generation with E2E-Loader++
Meglio, S. D., Starace, L. L. L., & Martino, S. D. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112684 · Cited by 3 · see citing works
Exploring the synergistic collaboration of Human Agentic-AI in enhancing the security of the software development lifecycle
Ayouni, S., Khan, R. A., Maddeh, M., Alwageed, H. S., Keshta, I. M., & Almagrabi, A. O. (2026) · Egyptian Informatics Journal · doi:10.1016/j.eij.2026.100915 · Cited by 3 · see citing works
Generative artificial intelligence adoption and managerial well-being in construction organizations
Olugboyega, O., Daniel, O. O., Victor, A. I., Olabayo, O. O., & Olatunji, S. B. (2026) · Engineering Construction & Architectural Management · doi:10.1108/ecam-12-2025-1962 · Cited by 4 · see citing works
Between Policy and Practice: GenAI Adoption in Agile Software Development Teams
Neumann, M., Bischof, L., Hinz, N. E., Altun, A., Stockmann, L., Schrader, D., Ahaus, A. C., Demirci, E. C., Gabel, B., Rauschenberger, M., et al. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-22375-3_18 · Cited by 5 · see citing works
Prevalence, Devices Used, Reasons for Use, Trust, Barriers, and Challenges in Utilizing Generative AI among Tertiary Students
Miranda, J. P. P., Bansil, J. A., Fernando, E. Q., Gamboa, A. B., Hernandez, H. E., Cruz, M., Dianelo, R. F. B., Gonzales, D., & Penecilla, E. (2024) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia61827.2024.10761175 · Cited by 3 · see citing works
“Medical education benefits from generative AI’s potential, but also raises ethical and legal issues, emphasizing the necessity for AI ethics training [22], [35], [36].”
Generative Artificial Intelligence Adoption: An Exploration of Challenges and Perceptions
Ziakis, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-74554-6_10 · Cited by 3 · see citing works
Unpacking the Drivers of AI Technology Acceptance
Sebetçi, Ö. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2564439 · Cited by 3 · see citing works
Can Generative Artificial Intelligence Drive Sustainable Behavior? A Consumer Adoption Model for Ai-Driven RecommendationsPreprint
Silalahi, A. D. K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5037486 · Cited by 3 · see citing works
Towards an Adoption Framework to Foster Trust in AI-Assisted Software Engineering
Barón, M. M. (2025) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain66642.2025.00038 · Cited by 2 · see citing works
“Russo [14] surveyed 100 software engineers to design the Human-AI Collaboration and Adaptation Framework (HCAF) to guide the adoption of generative AI (GenAI) technologies in software engineering.”
The Essential Human Contribution and its Impact on Future Workforce Development in the Field of Software Engineering
Schedlberger, L.-M., & Böhm, K. (2024) · International Conference on Modern Educational Technology (ICMET) · doi:10.1145/3729434.3729444 · Cited by 2 · see citing works
“As [16] shows, many software engineering professionals fear job insecurity due to the increased use of Generative AI.”
Advancing research software engineering with AI: a research framework
Farshidi, S., Bennin, K. E., Babur, Ö., Sallou, J., Kassahun, A., & Teki̇nerdoğan, B. (2026) · Automated Software Engineering · doi:10.1007/s10515-026-00621-0 · Cited by 3 · see citing works
“Cao et al. (2024) review generative AI in software engineering, and Russo (2024) investigates AI-assisted development practices, but these works do not jointly analyze engineering maturity, FAIR alignment, and operational workflows in research software settings.”
Staff Competency Assessment and Task Allocation Methods Considering AI Augmentation: A Study Based on the E-CARGO Model
Zhang, X., Yu, J., Huang, D., Zhu, H., Sun, Y., & Zhou, X. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3599525 · Cited by 2 · see citing works
“For instance, in software development, AI accelerates code generation and error detection [6], while in design ﬁelds, it enhances creative workﬂows through automated routines and conceptual suggestions [7].”
Continuance use of AI coding assistants among South Korean Industry Developers: A survey case study with large language models
Park, E. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10730-1 · Cited by 2 · see citing works
“…findings can also contribute to software engineering contexts by indicating the role of expectation confirmation in the continual use of LLMs. Prior research focused on initial adoption behaviors of AI-assisted tools, presenting perceived usefulness and ease of use as key determinants (Russo 2024).”
Influential
Application of Large Language Models in Software Development: Review of the Current State and Development Perspectives
Radenković, B., Prokhorov, S. P., Radenković, M., & Лабус, А. (2025) · International Workshop on Engineering Technologies and Computer Science (EnT) · doi:10.1109/ent68818.2025.11245717 · Cited by 2 · see citing works
“The inability to reason over large codebases due to token limits, and the lack of true semantic understanding, restrict their reliability in complex engineering tasks [21].”
Applying generative artificial intelligence for vulnerability fixing in a proprietary software ecosystem
Costa, L. A., Fontão, A., dos Santos, R. P., & Serebrenik, A. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112723 · Cited by 2 · see citing works
Measuring AI responsibility: A cross-country validation of a multidimensional framework
Jaturat, N., Na-Nan, K., & Hu, B. (2026) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2026.100388 · Cited by 2 · see citing works
Quantifying adoption: A SEM study of quantum software technology in software development
Lambiase, S., & Lucia, A. D. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10805-7 · Cited by 2 · see citing works
“…these values are in line with similar studies involving similar theoretical models and objectives (Voria et al. 2024; Lambiase and De Lucia 2024; Russo 2024) The analysis of participant roles indicates that Software Developer positions comprise the largest share, with 40.65% of participants.”
Influential
AI-Enhanced Software Testing: A Human-in-the-Loop Approach for Precision and Efficiency
Hosen, M. I., Alif, A. K., Zaman, Z., Bhuiyan, Z. W., Nahar, N., & Hasan, M. (2026) · International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) · doi:10.1109/icecte69292.2026.11429460 · Cited by 2 · see citing works
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Security Concerns
Ashraf, H. (2024) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-3703-5.ch012 · Cited by 2 · see citing works

Navigating the Complexity of Generative AI Adoption in Software Engineering—RCR Report
Russo, D. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3680471 · Cited by 27 · see citing works
Self-citation
Copiloting the future: How generative AI transforms Software Engineering
Banh, L., Holldack, F., & Strobel, G. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107751 · Cited by 66 · see citing works
Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study
Felder, L., Eisenreich, T., Fischer, M., Wagner, S., & Chen, C. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3805207
Empirical Analysis of Generative Ai Adoption in Software DevelopmentPreprint
Shao, D. D., & Ishengoma, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5149425
GenAI in Software Engineering: The Role of Technology Acceptance ModelsPreprint
Johansson, O., Börstler, J., & bin Ali, N. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.27642

Authors: Russo, D.
Affiliations: Aalborg University (DK)
Corresponding author: Russo, Daniel
ORCID: Russo, Daniel
Venue: ACM Transactions on Software Engineering and Methodology
Publisher: Association for Computing Machinery (ACM)
Published: 2024-06-04
Volume: 33
Issue: 5
Pages: 1-50
ISSN: 1049-331X (ISSN-L)
1557-7392
DOI: 10.1145/3652154
OpenAlex: W4393278146
Cited by: 217
References: 102
Citations by year: 2026: 86 · 2025: 97 · 2024: 34
FWCI: 80.98
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: ACM Transactions on Software Engineering and Methodology (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion, cc-by)
VBN Forskningsportal (Aalborg Universitet) (submittedVersion, cc-by)
arXiv (Cornell University)
Funders: Industriens Fond
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Keywords: generative AI adoption, software engineering, technology acceptance, AI tools, compatibility, perceived usefulness, social factors, personal innovativeness, AI tool design, software developers

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Navigating the Complexity of Generative AI Adoption in Software Engineering

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