UMA FERRAMENTA DE ENSINO DE CIRCUITOS LÓGICOS PARA DEFICIENTES VISUAIS

Graphical representation of logical expressions is one essential part of digital circuits learning process. This kind of representation is an obstacle to teachers when it is necessary to teach blind students. The use of screen readers, a type of software that takes information delivered to the screen and redirects it to a synthetic speech, is a way to make the blind people part of the studies in a classroom. However, these softwares are not able to reproduce graphical representation. This paper presents a tool that supports digital logic teaching to blind people using non-visual mechanisms to represent digital circuits and allows non-blind and blind  people to access the same content in different ways.O ensino de lógica digital inclui a representação de expressões lógicas de maneira gráfica. Este tipo de representação se torna um obstáculo quando é necessário o ensino a deficientes visuais. O uso de softwares leitores de tela, que reproduzem conteúdos textuais apresentados na tela em áudio, são uma forma de inclusão dos deficientes visuais em sala de aula. Porém, estes softwares não são capazes de reproduzir representações gráficas. Este artigo apresenta como uma ferramenta pode apoiar o ensino de lógica digital para deficientes visuais, utilizando mecanismos não visuais na representação de circuitos lógicos, possibilitando que usuários videntes e deficientes visuais acessem um mesmo conteúdo

Moving on from the software engineers' gambit: an approach to support the defense of software effort estimates

Pressure for higher productivity and faster delivery is increasingly pervading software organizations. This can lead software engineers to act like chess players playing a gambit -- making sacrifices of their technically sound estimates, thus submitting their teams to time pressure. In turn, time pressure can have varied detrimental effects, such as poor product quality and emotional distress, decreasing productivity, which leads to more time pressure and delays: a hard-to-stop vicious cycle. This reveals a need for moving on from the more passive strategy of yielding to pressure to a more active one of defending software estimates. Therefore, we propose an approach to support software estimators in acquiring knowledge on how to carry out such defense, by introducing negotiation principles encapsulated in a set of defense lenses, presented through a digital simulation. We evaluated the proposed approach through a controlled experiment with software practitioners from different companies. We collected data on participants' attitudes, subjective norms, perceived behavioral control, and intentions to perform the defense of their estimates in light of the Theory of Planned Behavior. We employed a frequentist and a bayesian approach to data analysis. Results show improved scores among experimental group participants after engaging with the digital simulation and learning about the lenses. They were also more inclined to choose a defense action when facing pressure scenarios than a control group exposed to questions to reflect on the reasons and outcomes of pressure over estimates. Qualitative evidence reveals that practitioners perceived the set of lenses as useful in their current work environments. Collectively, these results show the effectiveness of the proposed approach and its perceived relevance for the industry, despite the low amount of time required to engage with it.Comment: 12 pages, 3 figure

Can AI Serve as a Substitute for Human Subjects in Software Engineering Research?

Research within sociotechnical domains, such as Software Engineering, fundamentally requires a thorough consideration of the human perspective. However, traditional qualitative data collection methods suffer from challenges related to scale, labor intensity, and the increasing difficulty of participant recruitment. This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI), especially large language models (LLMs) like ChatGPT. We explore the potential of AI-generated synthetic text as an alternative source of qualitative data, by discussing how LLMs can replicate human responses and behaviors in research settings. We examine the application of AI in automating data collection across various methodologies, including persona-based prompting for interviews, multi-persona dialogue for focus groups, and mega-persona responses for surveys. Additionally, we discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations. By simulating human interaction and feedback, these AI models could offer scalable and efficient means of data generation, while providing insights into human attitudes, experiences, and performance. We discuss several open problems and research opportunities to implement this vision and conclude that while AI could augment aspects of data gathering in software engineering research, it cannot replace the nuanced, empathetic understanding inherent in human subjects in some cases, and an integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes

Barriers and Self-Efficacy: A Large-Scale Study on the Impact of OSS Courses on Student Perceptions

Open source software (OSS) development offers a unique opportunity for students in Software Engineering to experience and participate in large-scale software development, however, the impact of such courses on students' self-efficacy and the challenges faced by students are not well understood. This paper aims to address this gap by analyzing data from multiple instances of OSS development courses at universities in different countries and reporting on how students' self-efficacy changed as a result of taking the course, as well as the barriers and challenges faced by students

Comparing communication and development networks for predicting file change proneness: An exploratory study considering process and social metrics

Previous studies have shown that social factors of software engineering influence software quality. Communication and development networks represent the interactions among software developers. We explored the statistical relationships between file change proneness and a set metrics extracted from the issue tracker and version control system data to find the relative importance of each metric inunderstanding the evolution of file changes in the Rails project. Using hierarchical analysis, we found that code churn, number of past changes, and number of developers explain the evolution of changes in the Rails project better than Social NetworkAnalysis (SNA) metrics. Considering the relative importance of each predictor, wegot the same results. We also conducted a factor analysis and found that social metrics contribute to explain a group of files different from those explained by process metrics

Extensão da UML para modelagem orientada a aspectos baseada em aspectj

A Programação Orientada a Aspectos (AOP) visa reduzir algumas limitações encontradas na orientação a objetos, como o espalhamento e entrelaçamento de código. Isto é feito através do encapsulamento das preocupações ortogonais (crosscutting concerns) em módulos chamados aspectos. O uso de um modelo gráfico traria as facilidades da AOP para a fase de modelagem, além de, é claro, facilitar a fase de implementação. A proposta deste trabalho é estender o diagrama de classes da UML para apoiar o desenvolvimento de sistemas orientados a aspectos, com base na linguagem AspectJ.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking

Computational thinking, and by extension, computer programming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning process by offering personalized guidance, interactive learning experiences, and code generation. However, current genAI-based chatbots focus on professional developers and may not adequately consider educational needs. Involving educators in conceiving educational tools is critical for ensuring usefulness and usability. We enlisted \numParticipants{} instructors to engage in design fiction sessions in which we elicited abilities such a conversational agent supported by genAI should display. Participants envisioned a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring conversational agents oriented toward teaching computational thinking and computer programming.Comment: 17 pages, three figures, accepted at Conversations 2023 but not yet published in workshop proceeding

Google Summer of Code: Student Motivations and Contributions

Several open source software (OSS) projects expect to foster newcomers' onboarding and to receive contributions by participating in engagement programs, like Summers of Code. However, there is little empirical evidence showing why students join such programs. In this paper, we study the well-established Google Summer of Code (GSoC), which is a 3-month OSS engagement program that offers stipends and mentors to students willing to contribute to OSS projects. We combined a survey (students and mentors) and interviews (students) to understand what motivates students to enter GSoC. Our results show that students enter GSoC for an enriching experience, not necessarily to become frequent contributors. Our data suggest that, while the stipends are an important motivator, the students participate for work experience and the ability to attach the name of the supporting organization to their resum\'es. We also discuss practical implications for students, mentors, OSS projects, and Summer of Code programs.Comment: 30 page