arXiv subjects

Ronnie de Souza Santos

Publications and source records attributed to Ronnie de Souza Santos.

At least 19 recordsLinked to original sources

arXiv2026

Extending LLM-based support for software engineers with ADHD

Software engineering workflows are often not designed to accommodate the needs of developers with Attention Deficit Hyperactivity Disorder (ADHD), despite known challenges related to task initiation, sustained attention, and completion. At the same time, large language models (LLMs) are increasingly integrated into programming tools, but existing systems do not account for neurodiversity or support structured progression across development tasks. In this work, we present Tether 2.0, an LLM based assistant designed to support software engineers with ADHD through workflow oriented interaction across planning, coding, debugging, and review. The tool was named Tether 2.0 because it builds upon the open source code and foundations of the original Tether system. Our approach combines structured interaction modes, activity aware context, and persistent memory to support task progression and continuity. We evaluate the system through expert feedback and hands on use with a software engineer with ADHD. Our results indicate that Tether 2.0 supports requirement clarification, task decomposition, incremental implementation, debugging, and completion, while enabling users to maintain progress and resume work after interruptions. These findings suggest that LLM based assistants can support direction, sustained progress, and learning in software development when designed with workflow structure and context aware interaction.

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arXiv2026

Technostress in the Age of AI: A Preliminary Study with Software Professionals

The rapid adoption and evolution of AI are changing software engineering work and requiring professionals to repeatedly adapt their knowledge, practices, and skills. Although technological adaptation has long characterized software development, less is known about how these new and recurring pressures manifest as technostress. This preliminary exploratory study investigates AI related technostress among software professionals. We conducted a survey and performed a thematic analysis of responses from 121 software professionals across 26 countries reporting their recent experiences with AI at work. Our findings suggest that AI related technostress emerges not only from adapting to rapidly changing technologies, but also from having to manage the work, technical responsibilities, and professional changes that accompany their adoption. This characterization shows that AI introduces pressures beyond learning and using new tools, affecting how software professionals perform and remain accountable for technical work and how they prepare for the future of their careers.

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arXiv2026

Software Project Management with LLM-Based Automation: Coordination, Validation, and Governance in Practice

As software engineering has evolved, development environments have increasingly integrated automated support for tasks such as testing, analysis, and code generation, requiring project management to coordinate both human work and automated workflows. In this paper, we investigate how software project managers perceive changes in their work and learning demands associated with LLM-based automation. Motivated by a gap in software engineering research, which has largely focused on task-level and developer-centered uses of LLMs, we adopted an exploratory case study approach to capture managerial perspectives on automation in practice. Based on the experience of software project managers working in a large, multi-project software organization, our analysis indicates that LLM-based automation influences planning, estimation, coordination, monitoring, and governance activities rather than introducing new formal management practices. Participants described LLMs as becoming embedded in everyday project work, producing uneven effects on productivity, increasing the need for review and validation, and reducing visibility into task execution. These effects contribute to greater reliance on managerial judgment and coordination. Learning demands were perceived as experiential and incremental, centered on understanding LLM capabilities and limitations, critically assessing generated artifacts, and guiding responsible use within teams. The findings provide empirical evidence on how software project management work is adapted in contexts where LLM-based automation is integrated into ongoing software development practice.

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arXiv2026

Exploring Quantum Software Testing Across Research and Practice: Emerging Results from a Multivocal Literature Review

This paper presents preliminary findings from a multivocal literature review investigating how quantum software testing is characterized across academic and practitioner-oriented sources. Our study integrated peer-reviewed studies with gray literature, including blogs, tutorials, forums, technical reports, documentation pages, and company webpages. Our results indicate a rapidly evolving but fragmented ecosystem involving classical adapted testing approaches, quantum-specific techniques, statistical validation methods, simulators, debugging environments, and verification frameworks. The reviewed material also revealed recurring challenges related to scalability limitations, hardware noise, probabilistic execution, limited observability, and immature tooling ecosystems. These findings provide an initial characterization of how research and practice currently discuss quantum software testing challenges, techniques, and tooling.

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arXiv2026

Understanding LLM Usage Among Early-Career Software Engineers in Practice

Despite the rapid adoption of Large Language Models in professional software engineering, limited research has investigated how early career professionals develop effective AI assisted work practices during their transition into industry. We report findings from a mixed methods survey with 75 novice software engineers who actively use LLM supported tools in their daily work. Our results show that LLMs are embedded in routine software engineering activities, including coding, debugging, testing, documentation, and problem solving. Effective use depends on traditional software engineering competencies, such as debugging, testing, and architectural reasoning, together with critical thinking, output verification, prompt engineering, and continuous human oversight. We also identify a gap between workplace expectations and university preparation, with most participants reporting limited formal education on practical LLM use. These findings have implications for software engineering education, organizational onboarding, and workforce development in AI assisted software engineering.

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arXiv2026

Bias Smells in AI Software Development: Recognizing Potential Sources of Fairness Debt

Context: Fairness debt arises from AI development shortcomings that may lead to societal harms. While technical and social debt concern software design decisions and team dynamics, respectively, fairness debt captures the long-term consequences of development decisions that may reinforce bias and inequities in AI-based software systems. Despite growing attention to AI fairness, limited evidence exists on how practitioners recognize potential sources of fairness debt during development. Aim: This study investigates the indicators practitioners recognize as signaling potential sources of fairness debt in AI-based software projects. Method: We conducted an exploratory case study of four AI projects within one organization. Data were collected from 25 professionals through semi-structured interviews and open-ended questionnaires, complemented by observation of internal communication channels and project documentation, and analyzed using iterative qualitative coding, memoing, and constant comparison. Results: We identified six recurring indicators, termed bias smells: Context Oversimplification, Dataset Imbalance, Metrics Inadequacies, Ad hoc Testing, Individual Diversity Unawareness, and Homogeneous Team Composition. These smells span technical and human aspects of software development and signal conditions that may introduce or reinforce bias and contribute to fairness debt. Conclusion: Bias smells extend the software smell paradigm to fairness and provide a foundation for incorporating fairness into software quality assurance through observable indicators.

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arXiv2026

Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers

The widespread adoption of Large Language Models (LLMs) has changed how software engineers perform everyday development activities. While these systems provide substantial support for tasks such as code generation, debugging, and documentation, their increasing integration into professional workflows has also raised questions regarding developers' reliance on these tools and the emergence of dependence, overreliance, and addiction-related behaviors. This study investigates how software engineers experience the use of LLMs during professional software development, with attention to behavioral patterns associated with dependence, overreliance, and addiction-related behaviors. An exploratory survey was conducted with 119 software practitioners. The data were analyzed using descriptive statistics and qualitative thematic analysis of participants' open-ended responses. Participants primarily described functional dependence, with LLMs becoming integrated into routine software engineering activities because of the productivity and efficiency they provide. Responses also suggested patterns consistent with overreliance, particularly through prioritizing LLMs over documentation or peer consultation while continuing to verify generated outputs. Reports associated with addiction-related behaviors were less common and primarily reflected difficulty moderating use or emotional attachment to the technology rather than impaired control. The findings suggest that LLMs are becoming a habitual component of professional software engineering practice. While most reported use appears functional, the results indicate the importance of promoting appropriate reliance by supporting trust calibration, professional judgment, and verification throughout software development.

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arXiv2026

How Many Interviews Are Enough in a Software Engineering Study? Preliminary Findings on Sample Size and Saturation

Background. Interview based studies are widely used in empirical software engineering to investigate human, organizational, and socio technical phenomena, yet interview sample adequacy and saturation are reported inconsistently across the literature. Aims. This paper investigates how interview sample adequacy and saturation are operationalized in empirical software engineering research. Method. We analyzed papers published between 2016 and 2025 across major software engineering venues, focusing on interview sample sizes, saturation discussions, and sample adequacy justifications. Results. Preliminary findings indicate substantial variation in sample sizes, from highly specialized small sample studies to broader investigations involving large interview datasets. Studies involving fewer than 12 interviewees were common and frequently associated with specialized industrial contexts or constrained organizational access. However, the most recurrent range was 13 to 24 interviewees, suggesting that moderate sized samples represent the most common configuration in empirical software engineering research. Saturation and sample adequacy justification were heterogeneous, with many studies relying on implicit or contextual reasoning rather than explicit methodological discussion. Conclusions. Our findings provide initial empirical insights into methodological reporting practices in interview based software engineering research and contribute to ongoing discussions regarding qualitative rigor and transparency.

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arXiv2026

Preliminary Guidelines for Using and Evaluating GenAI Tools to Support Systematic Literature Reviews

Context: Generative AI (GenAI) and Large Language Models (LLMs) are increasingly used for academic tasks in software engineering and beyond, including systematic literature reviews (SLRs). However, while capable of summarizing text, there is no guarantee they can meet the rigour, reliability, and transparency that SLRs require. Objectives: To support researchers intending to conduct SLRs using GenAI or those conducting empirical studies evaluating how well GenAI supports SLR tasks. Methods: First, we conducted a rapid review to identify studies that propose guidelines for evaluating and using GenAI and LLMs to support SLRs. Second, we drew on thought experiments, relevant guidance from the literature, and our own experience conducting SLRs and evaluating tools to develop recommendations for how to use and assess GenAI in the context of SLRs. Results: We discuss the problems researchers face when evaluating GenAI for SLRs. We identify and explain process issues to consider when planning, conducting, and reporting both SLRs using GenAI and evaluations of GenAI tools. Finally, we summarize our results as a set of process recommendations, which we name GUEST (GenAI Use and Evaluation in SLR Tasks). Conclusion: We argue that GenAI requires human oversight and is not currently capable of unsupervised systematic studies. However, it offers the prospect of cost-effective assistance for some repetitive tasks and for additional validation of some complex tasks. Our GUEST recommendations should help software engineering researchers both to conduct and report trustworthy SLRs using GenAI and to provide rigorous independent evaluation studies.

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arXiv2026

The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys

Background: Large Language Models (LLMs) introduce new concerns regarding fraudulent or AI assisted participation in software engineering surveys. Aims: This study investigates how suspicious or potentially AI assisted responses may affect the validity of software engineering survey findings. Method: We conducted a secondary analysis of four software engineering survey datasets using manual identification of suspicious responses, automated AI generated text detection, descriptive statistical analysis, and thematic analysis. We compared findings obtained from the original and manually cleaned datasets. Results: Quantitative findings generally remained stable after filtering suspicious responses, although some demographic and analytical variables showed moderate variation, affecting the interpretation of specific participant groups and contextual characteristics. In contrast, qualitative findings were more strongly influenced by changes in contextual framing, code prominence, and the nature of the evidence supporting interpretation, shaping how participants' experiences and study contexts were interpreted and characterized. Conclusions: AI assisted participation may influence software engineering survey findings differently depending on the type of analysis being conducted. The findings reinforce the importance of combining multiple validation procedures, particularly in studies relying on open ended responses.

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arXiv2026

What Does It Take to Research with AI? A Rapid Review of Competencies to Train LLM-Literate Researchers

The growing adoption of Large Language Models in scientific research has created a need to understand what competencies researchers and graduate students require to use these tools critically and responsibly. This rapid review analyzed 194 articles retrieved from Elicit and Google Scholar (2022 to 2025), from which 40 were selected for competency extraction and thematic analysis following independent dual screening (Gwet AC1: 0.76 to 0.83). Eight competencies were identified. The most prevalent was domain expertise and oversight of AI outputs (n = 123), encompassing subject matter mastery, systematic skepticism, source verification, and researcher accountability. Other key competencies include metacognition and decision making about AI use (n = 55), ethics and academic integrity (n = 53), prompt engineering for research (n = 38), and reproducibility of AI use (n = 29). AI literacy and technical knowledge (n = 16) was explicitly identified as a risk factor when absent, with domain expertise treated as a prerequisite for meaningful critical evaluation. The findings suggest that preparing researchers to use LLMs goes beyond technical instruction, requiring an integrated set of epistemic, ethical, and methodological competencies centered on human accountability for the knowledge produced. These results have direct implications for the design of graduate programs and AI literacy initiatives.

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arXiv2026

Collaborative and AI-Supported Requirements Elicitation: An Empirical Study

Requirements elicitation requires stakeholders to communicate needs, negotiate priorities, and collaboratively construct knowledge that can be transformed into software requirements artifacts. Recent advances in LLMs have created opportunities to support these activities through AI-assisted collaboration and automated artifact generation. However, limited empirical evidence is available regarding how AI-supported collaborative environments influence requirements elicitation outcomes. In this study, we conducted a mixed-method controlled experiment comparing four requirements elicitation approaches: collaborative elicitation without AI support, collaborative elicitation supported by the Strateegia platform and its GPT-powered Writer applet, direct requirements generation using a Large Language Model, and requirements generation from collaborative discussion transcripts using a Large Language Model. We evaluated the resulting artifacts using quality criteria derived from ISO/IEC/IEEE 29148 and collected participant perceptions regarding the elicitation process. Our findings indicate that approaches combining stakeholder collaboration and AI-supported synthesis produced the highest-rated requirements artifacts and were perceived as clearer and easier to execute than traditional collaborative elicitation. The results suggest that generative AI can support the transformation of collaboratively generated knowledge into structured requirements documentation while preserving the value of stakeholder participation. We discuss implications for AI-supported requirements elicitation and human-AI collaboration in Requirements Engineering.

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arXiv2026

How Does LGBTQIA+ Identity Affect LLM Behavior? Implications for Requirements Engineering of Mental Health AI Systems

Large Language Models are now part of healthcare and mental health support systems, raising concerns regarding fairness toward vulnerable populations, including LGBTQIA+ individuals. However, limited empirical work has investigated how explicit LGBTQIA+ identity disclosure influences LLM-generated responses in mental health contexts. In this study, we extracted 50 real mental health questions from the Counsel Chat repository and constructed three prompt conditions for each question: no identity disclosure, explicit straight identity disclosure, and explicit LGBTQIA+ identity disclosure. We generated and analyzed 450 ChatGPT responses across these conditions using binary coding and comparative analysis. Our findings indicate that LGBTQIA+ identity disclosure did not substantially affect response completeness or supportive guidance. However, responses in the LGBTQIA+-explicit condition presented substantially more identity acknowledgment, contextual expansion, unsupported assumptions, and occasional stereotypical reasoning compared to both other conditions. These results suggest that fairness-related concerns in conversational AI systems may emerge through subtle differences in contextual interpretation and explanatory reasoning rather than through overtly harmful outputs. We discuss implications for fairness requirements and the development of LLM-based mental health support systems.

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arXiv2026

Practitioner Insights on Fairness Requirements in the AI Development Life Cycle: An Interview Study

Nowadays, Artificial Intelligence (AI), particularly Machine Learning (ML) and Large Language Models (LLMs), is widely applied across various contexts. However, the corresponding models often operate as black boxes, leading them to unintentionally act unfairly towards different demographic groups. This has led to a growing focus on fairness in AI software recently, alongside the traditional focus on the effectiveness of AI models. Through 26 semi-structured interviews with practitioners from different application domains and with varied backgrounds across 23 countries, we conducted research on fairness requirements in AI from software engineering perspective. Our study assesses the participants' awareness of fairness in AI / ML software and its application within the Software Development Life Cycle (SDLC), from translating fairness concerns into requirements to assessing their arising early in the SDLC. It also examines fairness through the key assessment dimensions of implementation, validation, evaluation, and how it is balanced with trade-offs involving other priorities, such as addressing all the software functionalities and meeting critical delivery deadlines. Findings of our thematic qualitative analysis show that while our participants recognize the aforementioned AI fairness dimensions, practices are inconsistent, and fairness is often deprioritized with noticeable knowledge gaps. This highlights the need for agreement with relevant stakeholders on well-defined, contextually appropriate fairness definitions, the corresponding evaluation metrics, and formalized processes to better integrate fairness into AI/ML projects.

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arXiv2026

How Software Engineering Students Use LLMs to Write Research Papers: An Experience Report

Large language models are increasingly becoming part of software engineering education, including activities involving empirical software engineering and evidence synthesis. This paper reports an educational experience involving the integration of reflective LLM use into an empirical methods assignment in a third-year software architecture course. Students were asked to develop a short research paper using either a rapid review or a gray literature review methodology and to disclose how LLMs were used throughout the assignment. We analyzed 146 student disclosure statements using a cross-analysis process combining LLM-assisted categorization with manual verification and refinement by the researchers. The reflections describe how students incorporated LLMs during activities such as brainstorming, methodological clarification, organization of findings, and writing refinement, while also reporting concerns regarding inaccuracies and verification of generated content. This experience report discusses lessons learned and educational implications for integrating AI-assisted technologies into empirical software engineering education.

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arXiv2026

Academic Integrity and Emotional Responses to Inappropriate LLM Use in Software Engineering Education

Academic integrity in higher education is increasingly shaped by complex socio-technical environments marked by automated tools, evolving institutional practices, and heightened performance pressures. Within this context, large language models (LLMs) are becoming prevalent in software engineering education, further blurring boundaries around acceptable assistance and authorship. This study investigates how software engineering students describe their emotional experiences after using LLMs in ways they perceive as academically inappropriate. We conducted a cross-sectional survey with 116 undergraduate students. Results show emotionally heterogeneous responses. Indifference was most frequent, including among students who recognized risks to learning and academic standing. Guilt and anxiety were reported in relation to moral discomfort and concern about penalties. Relief and satisfaction were evident primarily in deadline-driven contexts and situations of unclear guidance.

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arXiv2026

SLRMentor: An LLM-Based Tool Supporting Learning of SLR in Software Engineering

This paper presents SLRMentor, a conversational assistant designed to support both learning about the systematic literature review process and the execution of planning activities in software engineering. The tool offers general guidance on SLR methodology and supports key planning tasks, including search string construction and reasoning about inclusion and exclusion criteria, with explanations grounded in established SLR guidelines. A pilot validation with graduate students suggests that SLRMentor helps clarify the SLR process and planning decisions, lowers initial barriers for novice researchers, and supports learning while still requiring active methodological judgment.

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arXiv2026

Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review

Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes. Despite this, there is no concise synthesis of how fairness is defined, measured, and tested in DRL-based drug discovery. In this rapid evidence review, we synthesize fairness definitions and metrics for DRL-driven molecule generation in healthcare. We focus on three questions: (i) how dataset composition and split strategies, especially scaffold versus random splits, affect evaluation and distribution shift; (ii) how reward design (e.g., QED, docking, toxicity, synthetic accessibility) can create or mitigate bias, with emphasis on cancer targets; and (iii) which measurable metrics best capture fairness. This includes parity across cancer versus non-cancer indications and across cancer subtypes. It also includes distributional balance in key physicochemical descriptors, scaffold/chemotype diversity, groupwise validity, toxicity, and synthetic accessibility. From 2017 onward, we searched major biomedical, computer science, and engineering literature databases and used arXiv for horizon scanning. Records were screened using PRISMA-style procedures and analyzed via content coding to link reported parity outcomes to dataset and reward choices. Our review provides a concise set of fairness definitions and metrics for DRL molecule generation. It offers practical guidance for reporting distribution parity and outcome parity. It also summarizes how dataset and reward choices relate to observed parity effects and identifies open gaps relevant to trustworthy, cancer-relevant DRL generation.

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