Marcos Kalinowski

• D.Sc.
• Professor at Pontifical Catholic University of Rio de Janeiro

The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsider their attitude toward remote work and opt for structured return-to-office mandates. Media headlines repeatedly emphasize that the corporate world is returning to full-...

Generative AI (GenAI) introduces transformative challenges and opportunities to Intellectual Property (IP) processes in countries like Brazil, where existing laws, such as Copyright and IP Laws, do not explicitly account for AI-generated nuances. This paper explores the emerging idea behind GenAI’s impact and disruptive potential on the current Bra...

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinve...

[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...

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investi...

[Background] Systematic literature reviews (SLRs) are essential for synthesizing evidence in Software Engineering (SE), but keeping them up-to-date requires substantial effort. Study selection, one of the most labor-intensive steps, involves reviewing numerous studies and requires multiple reviewers to minimize bias and avoid loss of evidence. [Obj...

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 th...

[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. De...

Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's ef...

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical sof...

Increasingly, courses on empirical software engineering research methods are being offered in higher education institutes across the world, mostly at the MSc and PhD levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventin...

Empirical software engineering has received much attention in recent years and became a de-facto standard for scientific practice in software engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical sof...

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, incl...

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating...

O uso cada vez mais comum da Inteligência Artificial (IA) como ferramenta potencializadora de novos negócios, traz na sua incorporação em sistemas desafios únicos. Por anos, o Modelo de Referência MPS-SW apoiou a construção e evolução de sistemas de software de qualidade, contudo, o mesmo não contempla particularidades de soluções de IA. Neste arti...

[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, us...

Esta pesquisa propõe um método para o enriquecimento de conjuntos de dados tabulares utilizando estatísticas de grafo, visando melhorar o desempenho de modelos de ML supervisionados de classificação. O método constrói um grafo a partir da similaridade entre as instâncias do conjunto de dados e extrai características do grafo para enriquecer o conju...

Engineering machine learning (ML)-enabled systems poses various challenges from both a theoretical and a practical side. This thesis presents PerSpecML, a perspective-based approach for specifying ML-enabled systems that helps practitioners identify which attributes are important to contribute to the overall system’s quality. We evaluated PerSpecML...

The Impostor Phenomenon (IP) is the feeling of inaccurately perceiving oneself and frequently facing a significant fear of being exposed as a fraud, with impacts that can lead to mental disorders such as depression and burnout. This dissertation describes research that investigates the extent of impostor feelings among software engineers, consideri...

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...

[Context] The adoption of micro-frontends architectures has gained traction as a promising approach to enhance modularity, scalability, and maintainability of web applications. [Goal] The primary aim of this research is to investigate the benefits and limitations of migrating a real-world application to a micro-frontends architecture from the pers...

[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate t...

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 Colla...

Context: Rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing software engineering in every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adoption of ML-based technologies, mainly...

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 judgmen...

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, incl...

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...

[Context] The adoption of micro-frontends architectures has gained traction as a promising approach to enhance modularity, scalability, and maintainability of web applications. [Goal] The primary aim of this research is to investigate the benefits and limitations of migrating a real-world application to a micro-frontends architecture from the persp...

[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 Coll...

[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate t...

Resumo. Este artigo relata a experiência da implementação de quatro novos cursos de pós-graduação lato sensu do Departamento de Informática da PUC-Rio. Os cursos foram concebidos com uma metodologia inovadora, incluindo a organização dos cursos em Sprints, nos quais os alunos elaboram Produtos Mínimos Viáveis (MVPs) que avaliam hipóteses de aprendi...

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 meth...

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 meth...

[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 vario...

Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven acad...

This paper introduces the "Caminhos do Interior", a proposal of web-based solution aimed at promoting regional development through tourism in the lesser-explored areas of Brazil. Drawing from an interdisciplinary and sociotechnical perspective, this project merges concepts of under tourism and city branding to curate and spotlight tourist attractio...

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, littl...

Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and limitations of using the MLOps principles in online supervised learning. Method: We conducted two focus group ses...

In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely...

[Context] Systems that incorporate Machine Learning (ML) models, often referred to as ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited; this is especially true for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial...

The Impostor Phenomenon (IP) is widely discussed in Science, Technology, Engineering, and Mathematics (STEM) and has been evaluated in Computer Science students. However, formal research on IP in software engineers has yet to be conducted, although its impacts may lead to mental disorders such as depression and burnout. This study describes a surve...

Context: The constant growth of primary evidence and Systematic Literature Reviews (SLRs) publications in the Software Engineering (SE) field leads to the need for SLR Updates. However, searching and selecting evidence for SLR updates demands significant effort from SE researchers. Objective: We present emerging results on an automated approach to...

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...

Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scien...

In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and new products and services are continuously being delivered, ensuring that AI systems are designed and developed responsibly throughout their entire lifecycle is crucial. To this end, several AI ethics principles and guidelines have been issued to which AI...

In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely...

Context: The growing challenge in attracting employees to Voluntary Resignation Programs (VRP) lies in the need to balance the company's cost control with the goal of increasing participation from the target audience. Problem: It is essential to ensure that the process occurs smoothly, reducing tension during the separation and fostering a more coo...

Context: Given the significant expenses incurred by the General Social Security Regime (RGPS) in disability retirement benefit payments , accurately assessing the costs associated with new concessions is crucial for maintaining the financial and actuarial balance of the system. Entry tables for disability retirement are essential instruments for th...

The modernization of monolithic legacy systems with microservices has been a trend in recent years. As part of this modernization, identifying microservice candidates starting from legacy code is challenging, as maintainers may consider many criteria simultaneously. Multi-objective search-based approaches represent a promising state-of-the-art solu...

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enab...

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enab...

Software is strategic for Brazil's development, but the lack of a larger qualified workforce limits the country's productive capacity. This paper reports the experience of deploying a large-scale distance learning education program to meet the practical needs of the software industry. We applied design thinking involving software engineering and co...

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate...

Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which has later been complemented with snowball searches. Our conjecture is that we can perform even better searches if combining the two search approaches, referred to as a hybrid search strategy. Object...

Context: While most research shows positive effects of gamification, the focus on its adverse effects is considerably smaller and further understanding is needed. Objective: To provide a comprehensive overview on research reporting negative effects of game design elements and to provide insights into the awareness of developers on these effects and...

Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Indeed, internal attributes can become critical if their measurements assume anomalous values. Yet, current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolvi...

[Background] The MVP concept has influenced the way in which development teams apply Software Engineering practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to characterize the publication landscape on practices that have been used in the context of software MVPs and to gat...

Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Indeed, internal attributes can become critical if their measurements assume anomalous values. Yet, current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolvi...

Context User-Centered Design (UCD) and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users (they use the software and have low decision power) and the agile team is mediated by customers (they have high de...

The process of experimentation is one of several scientific methods that can provide evidence for a proof of a theory. This process is counterpoint to the real world observation method, thus providing a reliable body of knowledge. However, in the experimentation for emerging areas and in the consolidation process in scientific and industrial commun...

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the agile team is mediated by customers. However, they do not know the problems end users face in their routines. H...

Context: Although software development is a human activity, Software Engineering (SE) research has focused mostly on processes and tools, making human factors underrepresented. This kind of research may be improved using knowledge from human-focused disciplines. An example of missed opportunities is how SE employs psychometric instruments. Objectiv...

Investments in Research, Development, and Innovation (R&D&I) from Brazil's oil and gas sector are substantial due to the obligation established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP). Identifying the expectation of funding in an agile and simple way enables better planning, increasing the effectiveness of expenditures....

Com o crescente interesse da sociedade na aplicação dos recursos públicos, a atividade de auditoria interna governamental ganha importância. Entretanto, devido ao crescimento da integração e da complexidade dos sistemas de informação, que processam grandes quantidade de dados, os auditores enfrentam novos desafios ao analisar controles internos. Es...

Descrições de produtos dos estabelecimentos varejistas são informações utilizadas em análises de mercado, mas normalmente, essas descrições são mal estruturadas, não-padronizadas e variam muito para um mesmo produto. Este artigo propõe o uso de técnicas de processamento de linguagem natural, mais especificamente, um ramo conhecido como NER (Named E...

Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks, experimenting with algorithms, evaluating models, capturing data from users, among others. Literature has shown...

Context: New scientific evidence continuously arises with advances in Software Engineering (SE) research. Conventionally , Systematic Literature Reviews (SLRs) are not updated or updated intermittently, leaving gaps between updates, during which time the SLR may be missing crucial new evidence. Goal: We propose and evaluate a concept and process ca...

Context: The number of TV series offered nowadays is very high. Due to its large amount, many series are canceled due to a lack of originality that generates a low audience. Problem: Having a decision support system that can show why some shows are a huge success or not would facilitate the choices of renewing or starting a show. Solution: We studi...

Requirements engineering (RE) activities for machine learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus on RE for ML can help to develop more reliable ML-enabled systems. Based on insights collected from previous w...

As Tábuas de Mortalidade são tabelas estruturadas contendo dados epidemiológicos traduzidos em probabilidades de morte associada a cada idade de vida, utilizadas no mercado de previdência e seguros. Este artigo discorre sobre a aplicação do modelo de redes neurais para a construção de tábuas de mortalidade futuras, tendo como comparação o modelo Le...

A materialização da proteção social, prevista no capítulo da Seguridade Social da Constituição do Brasil, especificamente no âmbito da Previdência, se dá através da concessão e manutenção de benefícios a todos os brasileiros que necessitem dessa proteção. Esse direito gera uma demanda enorme de milhões de requisições de benefícios anuais ao INSS (I...

Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks, experimenting with algorithms, evaluating models, capturing data from users, among others. Literature has shown...

Context: New scientific evidence continuously arises with advances in Software Engineering (SE) research. Conventionally, Systematic Literature Reviews (SLRs) are not updated or updated intermittently, leaving gaps between updates, during which time the SLR may be missing crucial new evidence. Goal: We propose and evaluate a concept and process cal...

Context: The number of TV series offered nowadays is very high. Due to its large amount, many series are canceled due to a lack of originality that generates a low audience. Problem: Having a decision support system that can show why some shows are a huge success or not would facilitate the choices of renewing or starting a show. Solution: We studi...

Requirements engineering (RE) activities for Machine Learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus on RE for ML can help to develop more reliable ML-enabled systems. Based on insights collected from previous w...

Background A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which was later complemented with snowball searches. Our conjecture is that we can perform even better searches if combining these two search approaches, referred to as a hybrid search strategy. Objective...

Context Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide higher modularization. However, bad DI practices often lead to negative consequences, such as increasing coupling. Although white literature conjectures about the existence of DI anti-patterns, there is no evidence on th...