arXiv subjects

Allysson Allex Araújo

Publications and source records attributed to Allysson Allex Araújo.

12 recordsLinked to original sources

arXiv2026

Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study

Context: Generative Artificial Intelligence (GenAI) tools, such as GitHub Copilot and GPT tools, represent a paradigm shift in software engineering. While their impact is clear, most studies are short-term, focused on individual experiments. The sustained, team-level effects on productivity within industrial agile environments remain largely uncharacterized. Goal: This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams. We characterize its effect on developers' productivity by applying the multi-dimensional SPACE framework. Method: We conducted a multi-case longitudinal study involving 3 agile teams at a large technology consulting firm for around 13 months. We collected and compared quantitative telemetry (Jira, SonarQube, Git) and qualitative survey data from historical (pre-adoption) and research (post-adoption) sprints. Conclusion: GenAI tools can significantly improve team performance and well-being. Our key finding is a sharp increase in Performance and perceived Efficiency concurrent with flat developer Activity. This suggests GenAI increases the value density of development work, not its volume. This finding validates the necessity of multi-dimensional frameworks like SPACE to capture the true, nuanced impact of GenAI in situ, which would be invisible to studies measuring Activity alone.

cs.SE↗

arXiv2026

Constructive Patterns for Human-Centered Tech Hiring

[Context] Online Recruitment and Selection (R&S) processes are often the first point of contact between early-career software engineers and the tech industry. Yet many candidates experience these processes as opaque, inefficient, or even discouraging. While prior research has extensively documented the flaws and biases in online tech hiring, little is known about the practices that create positive candidate experiences. [Objective & Method] This paper explores such practices, referred to as Constructive Patterns (CPs), from the perspective of early-career software engineers. Guided by Applicant Attribution-Reaction Theory, we conducted 22 semi-structured interviews in which participants collectively described over 470 online R&S experiences. [Results] Through thematic analysis, we identified 22 CPs that reflect positive practices such as comprehensive and transparent job advertisements (CP01), specific and developmental feedback (CP03), humanized and respectful interaction (CP06), and framing the process as a two-way street (CP18). [Conclusion] Our findings extend the conversation on tech hiring beyond diagnosing dysfunctions toward designing for human-centered and growth-oriented candidate experiences. The resulting catalog of CPs provides a concrete and empirically grounded resource for organizations seeking to attract and support early-career software engineers more effectively.

cs.SE↗

arXiv2026

Adoption of Large Language Models in Scrum Management: Insights from Brazilian Practitioners

Scrum is widely adopted in software project management due to its adaptability and collaborative nature. The recent emergence of Large Language Models (LLMs) has created new opportunities to support knowledge-intensive Scrum practices. However, existing research has largely focused on technical activities such as coding and testing, with limited evidence on the use of LLMs in management-related Scrum activities. In this study, we investigate the use of LLMs in Scrum management activities through a survey of 70 Brazilian professionals. Among them, 49 actively use Scrum, and 33 reported using LLM-based assistants in their Scrum practices. The results indicate a high level of proficiency and frequent use of LLMs, with 85% of respondents reporting intermediate or advanced proficiency and 52% using them daily. LLM use concentrates on exploring Scrum practices, with artifacts and events receiving targeted yet uneven support, whereas broader management tasks appear to be adopted more cautiously. The main benefits include increased productivity (78%) and reduced manual effort (75%). However, several critical risks remain, as respondents report 'almost correct' outputs (81%), confidentiality concerns (63%), and hallucinations during use (59%). This work provides one of the first empirical characterizations of LLM use in Scrum management, identifying current practices, quantifying benefits and risks, and outlining directions for responsible adoption and integration in Agile environments.

cs.SE↗

arXiv2025

LLM-Assisted Thematic Analysis: Opportunities, Limitations, and Recommendations

[Context] Large Language Models (LLMs) are increasingly used to assist qualitative research in Software Engineering (SE), yet the methodological implications of this usage remain underexplored. Their integration into interpretive processes such as thematic analysis raises fundamental questions about rigor, transparency, and researcher agency. [Objective] This study investigates how experienced SE researchers conceptualize the opportunities, risks, and methodological implications of integrating LLMs into thematic analysis. [Method] A reflective workshop with 25 ISERN researchers guided participants through structured discussions of LLM-assisted open coding, theme generation, and theme reviewing, using color-coded canvases to document perceived opportunities, limitations, and recommendations. [Results] Participants recognized potential efficiency and scalability gains, but highlighted risks related to bias, contextual loss, reproducibility, and the rapid evolution of LLMs. They also emphasized the need for prompting literacy and continuous human oversight. [Conclusion] Findings portray LLMs as tools that can support, but not substitute, interpretive analysis. The study contributes to ongoing community reflections on how LLMs can responsibly enhance qualitative research in SE.

cs.SE↗

arXiv2025

Embracing Experiential Learning: Hackathons as an Educational Strategy for Shaping Soft Skills in Software Engineering

In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.

cs.SE↗

arXiv2025

Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience

[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

cs.SE↗

arXiv2024

Teaching Survey Research in Software Engineering

In this chapter, we provide advice on how to effectively teach survey research based on lessons learned from several international teaching experiences on the topic and from conducting large-scale surveys published at various scientific conferences and journals. First, we provide teachers with a potential syllabus for teaching survey research, including learning objectives, lectures, and examples of practical assignments. Thereafter, we provide actionable advice on how to teach the topics related to each learning objective, including survey design, sampling, data collection, statistical and qualitative analysis, threats to validity and reliability, and ethical considerations. The chapter is complemented by online teaching resources, including slides covering an entire course.

cs.SE↗

arXiv2024

Towards Effective Collaboration between Software Engineers and Data Scientists developing Machine Learning-Enabled Systems

Incorporating Machine Learning (ML) into existing systems is a demand that has grown among several organizations. However, the development of ML-enabled systems encompasses several social and technical challenges, which must be addressed by actors with different fields of expertise working together. This paper has the objective of understanding how to enhance the collaboration between two key actors in building these systems: software engineers and data scientists. We conducted two focus group sessions with experienced data scientists and software engineers working on real-world ML-enabled systems to assess the relevance of different recommendations for specific technical tasks. Our research has found that collaboration between these actors is important for effectively developing ML-enabled systems, especially when defining data access and ML model deployment. Participants provided concrete examples of how recommendations depicted in the literature can benefit collaboration during different tasks. For example, defining clear responsibilities for each team member and creating concise documentation can improve communication and overall performance. Our study contributes to a better understanding of how to foster effective collaboration between software engineers and data scientists creating ML-enabled systems.

cs.SE↗

arXiv2024

Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean R&D Meets Problem-Based Learning in Software Engineering Education

[Context] Software Engineering (SE) education constantly seeks to bridge the gap between academic knowledge and industry demands, with active learning methods like Problem-Based Learning (PBL) gaining prominence. Despite these efforts, recent graduates struggle to align skills with industry needs. Recognizing the relevance of Industry-Academia Collaboration (IAC), Lean R&D has emerged as a successful agile-based research and development approach, emphasizing business and software development synergy. [Goal] This paper aims to extend Lean R&D with PBL principles, evaluating its application in an educational program designed by ExACTa PUC- Rio for Americanas S.A., a large Brazilian retail company. [Method] The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems, coordinators and mentors, and company stakeholders in industry projects. Empirical evaluation, through a case study approach, utilized structured questionnaires based on the Technology Acceptance Model (TAM). [Results] Stakeholders were satisfied with Lean R&D PBL for problem-solving. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning. [Conclusion] This research contributes to academia by sharing Lean R&D PBL as an educational IAC approach. For industry, we discuss the implementation of this proposal in an IAC program that promotes workforce skill development and innovative solutions.

cs.SE↗

arXiv2024

Understanding and measuring software engineer behavior: What can we learn from the behavioral sciences?

This paper explores the intricate challenge of understanding and measuring software engineer behavior. More specifically, we revolve around a central question: How can we enhance our understanding of software engineer behavior? Grounded in the nuanced complexities addressed within Behavioral Software Engineering (BSE), we advocate for holistic methods that integrate quantitative measures, such as psychometric instruments, and qualitative data from diverse sources. Furthermore, we delve into the relevance of this challenge within national and international contexts, highlighting the increasing interest in understanding software engineer behavior. Real-world initiatives and academic endeavors are also examined to underscore the potential for advancing this research agenda and, consequently, refining software engineering practices based on behavioral aspects. Lastly, this paper addresses different ways to evaluate the progress of this challenge by leveraging methodological skills derived from behavioral sciences, ultimately contributing to a deeper understanding of software engineer behavior and software engineering practices.

cs.SE↗

arXiv2024

Investigating the Online Recruitment and Selection Journey of Novice Software Engineers: Anti-patterns and Recommendations

[Context] The growing software development market has increased the demand for qualified professionals in Software Engineering (SE). To this end, companies must enhance their Recruitment and Selection (R&S) processes to maintain high quality teams, including opening opportunities for beginners, such as trainees and interns. However, given the various judgments and sociotechnical factors involved, this complex process of R&S poses a challenge for recent graduates seeking to enter the market. [Objective] This paper aims to identify a set of anti-patterns and recommendations for early career SE professionals concerning R&S processes. [Method] Under an exploratory and qualitative methodological approach, we conducted six online Focus Groups with 18 recruiters with experience in R&S in the software industry. [Results] After completing our qualitative analysis, we identified 12 anti-patterns and 31 actionable recommendations regarding the hiring process focused on entry level SE professionals. The identified anti-patterns encompass behavioral and technical dimensions innate to R&S processes. [Conclusion] These findings provide a rich opportunity for reflection in the SE industry and offer valuable guidance for early-career candidates and organizations. From an academic perspective, this work also raises awareness of the intersection of Human Resources and SE, an area with considerable potential to be expanded in the context of cooperative and human aspects of SE.

cs.SE↗

arXiv2024

Can participation in a hackathon impact the motivation of software engineering students? A preliminary case study analysis

[Background] Hackathons are increasingly gaining prominence in Software Engineering (SE) education, lauded for their ability to elevate students' skill sets. [Objective] This paper investigates whether hackathons can impact the motivation of SE students. [Method] We conducted an evaluative case study assessing students' motivations before and after a hackathon, combining quantitative analysis using the Academic Motivation Scale (AMS) and qualitative coding of open-ended responses. [Results] Pre-hackathon findings reveal a diverse range of motivations with an overall acceptance, while post-hackathon responses highlight no statistically significant shift in participants' perceptions. Qualitative findings uncovered themes related to networking, team dynamics, and skill development. From a practical perspective, our findings highlight the potential of hackathons to impact participants' motivation. [Conclusion] While our study enhances the comprehension of hackathons as a motivational tool, it also underscores the need for further exploration of psychometric dimensions in SE educational research.

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