Danilo Monteiro Ribeiro

Danilo Ribeiro, Shen Wang, Xiaofei Ma et al. · amazon-science

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain and inspect a QA system's answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.

Rodrigo Siqueira, Danilo Monteiro Ribeiro

Technology organizations continuously invest in professional development, but face difficulties in transferring learning to project practice. This exploratory qualitative study investigates which improvements software engineering professionals suggest for organizational learning processes. 174 open-ended responses were analyzed through reflexive thematic analysis. Five themes emerged: practical applicability and alignment with needs; pedagogical quality and organization; time and structural conditions; incentives and institutional recognition; and interaction, mentoring, and social exchange. The results indicate that improving learning requires systemic interventions that integrate practical relevance, structural support, and a favorable institutional culture.

Rodrigo Siqueira, Antonio Oliveira, Breno Alves de Andrade et al.

Context: Quantitative studies can identify statistical predictors of training quality, but they often fail to capture what professionals themselves consider genuinely useful learning experiences and why. Objective: This study qualitatively investigates which types of learning experiences are perceived as most useful by Brazilian software engineering professionals and what characteristics define this usefulness. Method: Open-ended responses from 195 software engineering professionals were analyzed using Thematic Analysis, supported by frequency and lemmatization analysis using IRAMUTEQ and co-occurrence analysis between themes. Results: Five themes emerged: Continuous Technical Updating (T1), Practical and Applied Learning (T2), Formal Academic Education (T3), Social Learning and Networking (T4), and Leadership Development and Soft Skills (T5). Technical updating and practical application dominate professionals' accounts. Formal education, social learning, and soft skills are also valued as complementary dimensions. Conclusions: Perceived usefulness is strongly tied to alignment with daily work demands and immediate applicability. The convergence of technical updating (T1) and practical application (T2) in both frequency and co-occurrence reinforces the imperative of continuous learning in software engineering. Useful learning is not reducible to a single modality: genuinely valued experiences span technical, academic, social, and self-directed dimensions. Formal academic education and practical learning are perceived as complementary rather than competing. Organizations should design training ecosystems that integrate these dimensions rather than delivering isolated events.

Rodrigo Siqueira, Antonio Oliveira, Breno Alves Andrade et al.

Context: Strategic corporate training is essential for the sustained professional development of software engineers. However, there is a knowledge gap regarding the factors that drive quality and effectiveness of such training from the professionals' perspective, and no validated instrument exists for assessing these factors in the software engineering (SE) domain. Objective: This study aims to quantitatively analyze which factors influence SE professionals' perceptions of corporate training quality and effectiveness. Method: A quantitative survey was conducted with 282 Brazilian SE professionals. A structured questionnaire was developed and polychoric correlation was adopted for data analysis. Results: Three tightly correlated factors (cognitive engagement, variety of activities, and instructor performance) emerged as the strongest predictors of perceived training quality and effectiveness. Mandatory participation significantly reduces motivation and perceived training quality. Perceived impact on personal time proved to be largely independent of training quality. These findings are consistent with the general training effectiveness literature. Conclusions: Training effectiveness in the SE context is predominantly determined by three factors: cognitive engagement, variety of activities, and instructor performance. Mandatory participation negatively influences motivation, perceived relevance, and perceived training quality, while also amplifying the perception of time burden. The consistency with the general literature suggests that software organizations do not need to reinvent training design principles and can apply established guidelines with confidence. Salas and Cannon-Bowers' framework produced coherent results in the SE context, making it a promising candidate for future psychometric validation.

Rodrigo Siqueira, Danilo Monteiro Ribeiro

Context: Corporate education is a strategic investment in the software industry, but little is known about how different professional profiles perceive these initiatives. Objective: To investigate whether sociodemographic and professional variables influence the perception of quality and effectiveness of corporate training in Software Engineering (SE). Method: Non-parametric significance tests were applied to data from a survey with 282 Brazilian professionals, crossing 27 perception items with 9 sociodemographic variables (gender, age, education level, state, experience, professional level, company size, area of work, and nature of participation), totaling 243 combinations. Results: Of the 243 combinations tested, only 35 showed statistical significance. Training mandatoriness was the dominant factor, affecting 24 of 27 items. Length of experience revealed a non-linear descriptive pattern with a low-engagement zone between 3 and 6 years. Differences by area of work indicated an expressive gap in soft skills training for advanced technical roles. Personal profile variables and company size produced no relevant significant differences. Conclusion: Personal profile variables do not determine the perception of quality and effectiveness, while professional trajectory variables (experience, level, area of work) produce localized differences. The voluntariness of participation remains a determining factor, in line with the literature. The absence of gender differences in a sample with 23\% women suggests that barriers operate before training, in access and representation, not during the learning experience.

Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto et al.

Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations.