arXiv subjects

Rodrigo Spinola

Publications and source records attributed to Rodrigo Spinola.

11 recordsLinked to original sources

arXiv2026

Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers

AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted QDA. Based on our decades of qualitative SE research expertise and experience combined with an understanding of the emerging landscape of AI-assisted QDA, this paper presents a catalog of antipatterns in AI-assisted QDA - a set of assumptions and practices that initially appear advantageous but ultimately undermine analytical rigor and validity. The antipatterns are grouped into three categories reflecting escalating impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. As more SE researchers attempt AI-assisted QDA, these antipatterns will help them identify and avoid common temptations and pitfalls, while reviewers can be equipped with the vocabulary and criteria to call out problematic and failed practice. Ultimately, this catalog of antipatterns can serve as a stepping stone in our responsible methodological evolution toward principled and meaningful human-AI collaboration in qualitative research.

cs.SE↗

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.

cs.SE↗

arXiv2026

LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda

Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical workflows, yet their use remains fragmented, with no comprehensive synthesis to guide responsible adoption. Aims: This study analyzes how LLMs are used in ESE, examining supported tasks, phases of the empirical lifecycle, integration into workflows, reported benefits and limitations, and the extent of reproducibility-related reporting. It also identifies gaps and future research directions. Method: We conducted a systematic literature review of peer-reviewed papers (2020-2025) across 12 leading software engineering venues, resulting in 50 primary studies analyzed through qualitative and quantitative synthesis. Results: We identified 69 LLM-assisted tasks, mainly in mining software repositories and controlled experiments, focusing on classification, filtering, and evaluation. LLMs are used across multiple phases but are concentrated in data processing and analysis. Their integration is largely automation-oriented, with limited decision-support use. Benefits emphasize efficiency and scalability, while limitations include hallucinations, inconsistency, prompt sensitivity, and reproducibility issues. Reporting practices are often incomplete. Conclusion: LLM use in ESE is growing but remains automation-driven, with gaps in human-centered integration and transparency. We outline implications and research agenda for responsible use.

cs.SE↗

arXiv2026

A Survey on Large Language Model Impact on Software Evolvability and Maintainability: the Good, the Bad, the Ugly, and the Remedy

Context. Large Language Models (LLMs) are increasingly embedded in software engineering workflows for tasks including code generation, summarization, repair, and testing. Empirical studies report productivity gains, improved comprehension, and reduced cognitive load. However, evidence remains fragmented, and concerns persist about hallucinations, unstable outputs, methodological limitations, and emerging forms of technical debt. How these mixed effects shape long-term software maintainability and evolvability remains unclear. Objectives. This study systematically examines how LLMs influence the maintainability and evolvability of software systems. We identify which quality attributes are addressed in existing research, the positive impacts LLMs provide, the risks and weaknesses they introduce, and the mitigation strategies proposed in the literature. Method. We conducted a systematic literature review. Searches across ACM DL, IEEE Xplore, and Scopus (2020 to 2024) yielded 87 primary studies. Qualitative evidence was extracted through a calibrated multi-researcher process. Attributes were analyzed descriptively, while impacts, risks, weaknesses, and mitigation strategies were synthesized using a hybrid thematic approach supported by an LLM-assisted analysis tool with human-in-the-loop validation. Results. LLMs provide benefits such as improved analyzability, testability, code comprehension, debugging support, and automated repair. However, they also introduce risks, including hallucinated or incorrect outputs, brittleness to context, limited domain reasoning, unstable performance, and flaws in current evaluations, which threaten long-term evolvability. Conclusion. LLMs can strengthen maintainability and evolvability, but they also pose nontrivial risks to long-term sustainability. Responsible adoption requires safeguards, rigorous evaluation, and structured human oversight.

cs.SE↗

arXiv2025

On the Role and Impact of GenAI Tools in Software Engineering Education

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

cs.SE↗

arXiv2025

Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems

[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks to productivity, quality, and team morale. Despite growing attention to TD in SE, the understanding of ML-specific code-related TD remains underexplored. [Objective] This paper aims to identify and discuss the relevance of code-related issues that lead to TD in ML code throughout the ML workflow. [Method] The study first compiled a list of 34 potential issues contributing to TD in ML code by examining the phases of the ML workflow, their typical associated activities, and problem types. This list was refined through two focus group sessions involving nine experienced ML professionals, where each issue was assessed based on its occurrence contributing to TD in ML code and its relevance. [Results] The list of issues contributing to TD in the source code of ML systems was refined from 34 to 30, with 24 of these issues considered highly relevant. The data pre-processing phase was the most critical, with 14 issues considered highly relevant. Shortcuts in code related to typical pre-processing tasks (e.g., handling missing values, outliers, inconsistencies, scaling, rebalancing, and feature selection) often result in "patch fixes" rather than sustainable solutions, leading to the accumulation of TD and increasing maintenance costs. Relevant issues were also found in the data collection, model creation and training, and model evaluation phases. [Conclusion] We have made the final list of issues available to the community and believe it will help raise awareness about issues that need to be addressed throughout the ML workflow to reduce TD and improve the maintainability of ML code.

cs.SE↗

arXiv2024

Preliminary Insights on Industry Practices for Addressing Fairness Debt

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We used a qualitative research method, gathering insights from industry professionals through interviews and employing thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and issues with training data. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and provides a foundation for developing structured guidelines to manage fairness-related issues in AI systems.

cs.SE↗

arXiv2024

Software Fairness Debt

As software systems continue to play a significant role in modern society, ensuring their fairness has become a critical concern in software engineering. Motivated by this scenario, this paper focused on exploring the multifaceted nature of bias in software systems, aiming to provide a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiency in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt, which complements the notions of technical and social debt, encapsulating the accumulation of biases in software engineering practices while emphasizing the societal ramifications of bias embedded within software systems. Our study contributes to a deeper understanding of fairness in software engineering and paves the way for the development of more equitable and socially responsible software systems.

cs.SE↗

arXiv2023

An initial Theory to Understand and Manage Requirements Engineering Debt in Practice

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners' understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

cs.SE↗

arXiv2021

Prevalence, Common Causes and Effects of Technical Debt: Results from a Family of Surveys with the IT Industry

The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development effort is spent, i.e. wasted, on TD caused issues in software development organizations. However, further research is needed to investigate the relations between various software development activities and TD. The objective of this study is twofold. First, to get empirical insight on the understanding and the use of the TD concept in the IT industry. Second, to contribute towards precise conceptualization of the TD concept through analysis of causes and effects. In order to address the research objective a family of surveys was designed as a part of an international initiative that congregates researchers from 12 countries -- InsighTD. At country level, national teams ran survey replications with industry practitioners from the respective countries. In total 653 valid responses were collected from 6 countries. Regarding the prevalence of the TD concept 22% of practitioners have only theoretical knowledge about it, and 47% have some practical experiences with TD identification or management. Further analysis indicated that senior practitioners who work in larger organizations, larger teams, and on larger systems are more likely to be experienced with TD management. Time pressure or deadline was the single most cited cause of TD. Regarding the effects of TD: delivery delay, low maintainability, and rework were the most cited. InsighTD is the first family of surveys on technical debt in software engineering. It provided a methodological framework that allowed multiple replication teams to conduct research activities and to contribute to a single dataset. Future work will focus on more specific aspects of TD management.

cs.SE↗

arXiv2016

Naming the Pain in Requirements Engineering: Comparing Practices in Brazil and Germany

As part of the Naming the Pain in Requirements Engineering (NaPiRE) initiative, researchers compared problems that companies in Brazil and Germany encountered during requirements engineering (RE). The key takeaway was that in RE, human interaction is necessary for eliciting and specifying high-quality requirements, regardless of country, project type, or company size.

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