arXiv subjects

Samuel Ferino

Publications and source records attributed to Samuel Ferino.

5 recordsLinked to original sources

arXiv2026

Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering

How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software developers on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can help practitioners, educators, and policymakers promote responsible and effective use of AI tools.

cs.SE↗

arXiv2025

Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective

Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed Socio-Technical Grounded Theory for Data Analysis (STGT4DA) to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain developer flow, improve developer mental models, and foster entrepreneurship) and challenges (e.g., damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as actionable guidances into how mitigate these challenges. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.

cs.SE↗

arXiv2025

Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review

Following the rise of large language models (LLMs), many studies have emerged in recent years focusing on exploring the adoption of LLM-based tools for software development by novice developers: computer science/software engineering students and early-career industry developers with two years or less of professional experience. These studies have sought to understand the perspectives of novice developers on using these tools, a critical aspect of the successful adoption of LLMs in software engineering. To systematically collect and summarise these studies, we conducted a systematic literature review (SLR) following the guidelines by Kitchenham et al. on 80 primary studies published between April 2022 and June 2025 to answer four research questions (RQs). In answering RQ1, we categorised the study motivations and methodological approaches. In RQ2, we identified the software development tasks for which novice developers use LLMs. In RQ3, we categorised the advantages, challenges, and recommendations discussed in the studies. Finally, we discuss the study limitations and future research needs suggested in the primary studies in answering RQ4. Throughout the paper, we also indicate directions for future work and implications for software engineering researchers, educators, and developers. Our research artifacts are publicly available at https://github.com/Samuellucas97/SupplementaryInfoPackage-SLR.

cs.SE↗

arXiv2023

Overcoming Challenges in DevOps Education through Teaching Methods

DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.

cs.SE↗

arXiv2022

DevOps Education: An Interview Study of Challenges and Recommendations

Over the last years, the software industry has adopted several DevOps technologies related to practices such as continuous integration and continuous delivery. The high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies. This work presents an interview study with 14 DevOps educators from different universities and countries, aiming to identify the main challenges and recommendations for DevOps teaching. Our study identified 83 challenges, 185 recommendations, and several association links and conflicts between them. Our findings can help educators plan, execute and evaluate DevOps courses. They also highlight several opportunities for researchers to propose new methods and tools for teaching DevOps.

cs.SE↗