Beyond Code: The Multidimensional Impacts of Large Language Models in Software Development (2506.22704v2)

Abstract: LLMs are poised to significantly impact software development, especially in the Open-Source Software (OSS) sector. To understand this impact, we first outline the mechanisms through which LLMs may influence OSS through code development, collaborative knowledge transfer, and skill development. We then empirically examine how LLMs affect OSS developers' work in these three key areas. Leveraging a natural experiment from a temporary ChatGPT ban in Italy, we employ a Difference-in-Differences framework with two-way fixed effects to analyze data from all OSS developers on GitHub in three similar countries, Italy, France, and Portugal, totaling 88,022 users. We find that access to ChatGPT increases developer productivity by 6.4%, knowledge sharing by 9.6%, and skill acquisition by 8.4%. These benefits vary significantly by user experience level: novice developers primarily experience productivity gains, whereas more experienced developers benefit more from improved knowledge sharing and accelerated skill acquisition. In addition, we find that LLM-assisted learning is highly context-dependent, with the greatest benefits observed in technically complex, fragmented, or rapidly evolving contexts. We show that the productivity effects of LLMs extend beyond direct code generation to include enhanced collaborative learning and knowledge exchange among developers, dynamics that are essential for gaining a holistic understanding of LLMs' impact in OSS. Our findings offer critical managerial implications: strategically deploying LLMs can accelerate novice developers' onboarding and productivity, empower intermediate developers to foster knowledge sharing and collaboration, and support rapid skill acquisition, together enhancing long-term organizational productivity and agility.

• The paper demonstrates that LLMs significantly affect OSS development, with a 6.4% productivity drop, 9.6% increase in knowledge sharing, and 8.4% decline in skill acquisition during the ChatGPT ban.
• Using Italy's ChatGPT ban as a natural experiment, the study isolates LLM impacts and highlights how benefits differ between novice, intermediate, and experienced developers.
• The findings suggest that strategic LLM integration can enhance onboarding, collaboration, and continuous learning, thereby strengthening long-term software development outcomes.

Multidimensional Impacts of LLMs in Software Development

Introduction

The paper "Beyond Code: The Multidimensional Impacts of LLMs in Software Development" investigates the effects of LLMs in the sphere of Open-Source Software (OSS) development. It explores the influence of LLMs across three pivotal dimensions: code development, collaborative knowledge transfer, and skill development, to illuminate their comprehensive impact on software development practices.

Impact on Software Development

The paper underscores the transformative potential of LLMs, using the sudden ban of ChatGPT in Italy as a natural experiment to empirically assess the ramifications on productivity, knowledge sharing, and skill acquisition among software developers on GitHub. During the four-week ban, developers in Italy experienced a 6.4% reduction in productivity, highlighting the crucial support provided by LLMs in enhancing direct code generation and improving collaborative learning dynamics. Upon restoration of LLM access, there was a notable 9.6% increase in knowledge-sharing activities, reinforcing the role of LLMs in promoting collaboration and community interaction. Additionally, the results indicate an 8.4% decline in skill acquisition during the ChatGPT ban, emphasizing the importance of LLMs in fostering developers' learning of new programming languages.

Heterogeneous Effects by User Experience Level

The paper reveals that LLMs impact developers differentially based on their experience levels. Novice developers primarily benefit from productivity gains, with access to LLMs narrowing the productivity gap with their more experienced counterparts. For this group, the temporary loss of LLM access led to a 14.8% drop in productivity during the ban, with a recovery activated upon the restoration of the tool. In contrast, intermediate and experienced developers substantially benefitted from LLMs in terms of knowledge sharing and skill acquisition, with a significant rise in activities upon re-activation, particularly among intermediate developers, who exhibited a 22.3% increase in knowledge sharing upon the restoration of ChatGPT.

Implications for Industry

The findings suggest that LLMs not only improve immediate developer productivity through code generation but also enhance skill acquisition and knowledge sharing, contributing to long-term productivity and resilience in software development communities. In environments like OSS, where collaboration and continuous learning are key, these tools are particularly beneficial. Organizations and managers should consider the potential long-term productivity advantages of integrating LLMs—including faster onboarding, improved knowledge sharing, and enhanced skill acquisition—in their strategic planning and talent development strategies. It is critical to recognize that LLMs offer particularly significant advantages in contexts requiring the navigation of large, technical, fragmented, or rapidly evolving domains, where traditional learning resources may be limited.

Concluding Insights

In conclusion, this paper provides compelling empirical evidence on the significant and multi-dimensional impacts that LLMs like ChatGPT exert on software development. By assessing developer productivity, knowledge sharing, and skill acquisition, the paper presents a comprehensive perspective on how LLMs can transform the software development landscape and enhance the productivity, collaboration, and learning of OSS developers, particularly at different experience levels. This analysis is pivotal for researchers and practitioners seeking a nuanced understanding of the role of LLMs in modern knowledge work and offers key insights for optimizing LLM deployment and integration strategies. The paper acknowledges limitations related to the unobserved use of alternative LLMs or circumvention tools like VPNs, positing that these factors might attenuate but likely do not bias the results. Furthermore, by highlighting the heterogeneous effects of LLMs on different developer subgroups, the paper provides actionable insights for managers aiming to align AI adoption strategies with workforce characteristics and to enhance organizational productivity, collaboration, and learning in the digital age.