Kattiana Constantino

O objetivo deste artigo é saber como os Modelos de Linguagem de Grande Escala (Large Language Models -LLMs) impactam no ensino de programação. Atualmente muitos iniciantes nas linguagens de programação, em termos de criação, correção e explicação de código, vêm recorrendo às Large Language Models devido à sua facilidade em obter códigos prontos e explicações completas. Mas será que as LLMs causam impacto negativo ou positivo no ensino de linguagens de programação? Isso que esse artigo irá discutir.

A colaboração ativa é essencial para o sucesso de projetos de software em todo o ciclo de vida do desenvolvimento. Infelizmente, em plataformas de codificação social, como o GitHub, ainda é desafiador para os desenvolvedores identificar potenciais colaboradores e por conseguinte, melhorar a qualidade das contribuições. Para esse fim, implementamos estratégias de recomendação de desenvolvedores, apoiadas por uma ferramenta visual e interativa, chamada COOPFINDER, para conectar colaboradores com base em um conjunto de arquivos de seus interesses. Além disso, a ferramenta fornece metadados e vincula diferentes atributos que não podem ser analisados usando a interface do GitHub, facilitando a tomada de decisão dos colaboradores.

Active collaboration is essential for the success of software projects across the development life-cycle. Unfortunately, in social coding platforms, such as GitHub, it is still challenging for developers to identify potential collaborators with whom they could engage to create new/stronger ties and enhance the quality of contributions. To this end, we implemented developer recommendation strategies and prototype tool to help project contributors improve their collaborations. Thus, in this work, we described a controlled experimental study concerned usability and user satisfaction to investigate the developers’ perceptions of using CoopFinder, a prototype tool to support two strategies for recommending collaborations. These developer recommendation strategies aim to connect developers of a specific project based on their similar interests. The study involved 35 participants, 18 of which were GitHub users, and 17 were non–GitHub users. We asked participants to perform the experiment tasks to find collaborators with similar interests using a prototype recommendation tool and GitHub. We reported a quantitative and qualitative evaluation of strategies and tool using the state of the practice as a baseline. As a result, we observed that recommender based on co–changed files can provide suitable collaborator recommendations to developers of a specific project. About 66% of the participants confirmed they would use or recommend this tool.

Based on openness and transparency for good governance, unimpeded and verifiable access to legal and regulatory information is essential. With such access, we can monitor government actions to ensure that public financial resources are not improperly or inconsistently used. This facilitates, for example, the detection of unlawful behavior in public actions, such as bidding processes and auctions. However, different public agencies have their own criteria for standardizing the models and formats used to make information available, as exemplified in the varying styles observed in municipal, state, and union (federal) documents. In this context, we aim to minimize the effort to deal with public documents, notably official gazettes. For this, we propose a structure-oriented heuristic for extracting relevant excerpts from their texts. We then characterize these excerpts through morphosyntactic analysis and entity recognition. Subsequently, we semantically classify the extracted fragments into "sections of interest" (e.g., bids, laws, personnel, budget) using an active learning strategy to reduce the manual labeling effort. We also improve the classification process by incorporating transformers, stacking, and by combining different types of representations (e.g., frequentist, static, and contextual semantic embeddings). Furthermore, we exploit oversampling based on semi-supervised learning to deal with (labeled) data scarceness and skewness. Finally, we combine all these contributions in a real-time annotation tool with active learning support that achieves 100% accuracy in extraction and an overall accuracy of 85% in classification with very little labeling effort.

Active collaboration is essential for the success of software projects across the development life-cycle. However, social coding platforms, like GitHub, present challenges in finding potential collaborators with whom they could create new/stronger ties and enhance the quality of contributions. Thus, we conducted a controlled experiment with 35 participants. We asked participants to perform the experiment tasks to find collaborators with similar interests using a prototype recommendation tool and GitHub. We observed that results confirm that recommender based on co–changed files can provide suitable collaborator recommendations to developers of a specific project.

A colaboração é essencial no desenvolvimento de software, porém encontrar colaboradores adequados pode ser um desafio em grandes projetos de código aberto. Neste trabalho, investigamos o desenvolvimento colaborativo de código com base em interesses similares para ajudar os desenvolvedores a encontrar colaboradores adequados. Cinco estudos empíricos foram conduzidos, incluindo entrevistas e questionários. Duas estratégias baseadas em arquivos co-alterados e um protótipo denominada COOPFINDER foram propostas e avaliadas. Usuários ou não do GitHub acharam as estratégias e a ferramenta úteis. Os resultados sugerem que promover colaborações em projetos pode evitar o desperdício de recursos e manter a continuidade do projeto.

Summary: Software developers must collaborate at all stages of the software life‐cycle to create successful complex software systems. To enable this collaboration, social coding platforms, for example, GitHub, include an increasing number of tools to support collaboration. However, for large projects with hundreds of dynamic developers, such as several successful open–source projects, it can be complex to find developers with the same interest and familiarity and thus, gain suitable collaborations and new insights. In this context, resources and efforts may be wasted, discouraging many developers from contributing. Moreover, it can be costly to manage many contributions, which is another challenge for the maintainer who wants to take advantage of this small, timid, but valuable contribution made by a volunteer developer in a short time. In this context, this paper presents an empirical study aiming to evaluate two strategies to recommend collaborators based on co‐changed files. Inspired in the TF–IDF (Term Frequency–Inverse Document Frequency) weighting scheme established in the Information Retrieval field, these strategies first estimate the importance of relevant files modified by developers and use these estimates to represent each developer “profile”. As a second step, they estimate the similarity between developers using the Cosine metric, providing top‐ranked developers according to this measure as recommendations. We evaluated these strategies based on an extensive survey with 102 real–world developers. We observed that developers have interest and familiarity with the co‐changed files for all strategies evaluated. These considerations are of relevance because many opportunities for contributions to the project are linked to coding. Thus, theses results may indicate one less barrier for improving collaboration among developers. Overall, the strategies present an acceptance rate of up to 81%, contributing to the discovery of further collaborators.

Acesso irrestrito e monitorável a leis e regulamentações é pressuposto essencial da democracia. Isso permite, por exemplo, a detecção de ilícitos e o monitoramento de fraudes em ações públicas (e.g., licitações). Contudo, cada ente federado segue seus próprios critérios de padronização de modelos e formato na disponibilização dessas informações, por exemplo, nos diários oficiais municipais, estaduais e da União. Nesse contexto, nosso objetivo é minimizar o esforço para lidar com a extração textual desses dados ao propor uma heurística orientada à estrutura para segmentar os trechos de documentos públicos. Posteriormente, classificamos semanticamente os trechos extraídos com uma estratégia de aprendizado ativo que minimiza o esforço manual de rotulação. Como resultado desses esforços, desenvolvemos um protótipo de anotação integrado ao processo de classificação, obtendo uma acurácia de 100% na extração e de 85% na classificação com muito pouco esforço de rotulação.

With the emergence of social coding platforms, collaboration has become a key and dynamic aspect to the success of software projects. In such platforms, developers have to collaborate and deal with issues of collaboration in open‐source software development. Although collaboration is challenging, collaborative development produces better software systems than any developer could produce alone. Several approaches have investigated collaboration challenges, for instance, by proposing or evaluating models and tools to support collaborative work. Despite the undeniable importance of the existing efforts in this direction, there are few works on collaboration from perspectives of developers. In this work, we aim to investigate the perceptions of open‐source software developers on collaborations, such as motivations, techniques, and tools to support global, productive, and collaborative development. Following an ad hoc literature review, an exploratory interview study with 12 open‐source software developers fromGitHub, our novel approach for this problem also relies on an extensive survey with 121 developers to confirm or refute the interview results. We found different collaborative contributions, such as managing change requests. Besides, we observed that most collaborators prefer to collaborate with the core team instead of their peers. We also found that most collaboration happens in software development (60%) and maintenance (47%) tasks. Furthermore, despite personal preferences to work independently, developers still consider collaborating with others in specific task categories, for instance, software development. Finally, developers also expressed the importance of the social coding platforms, such asGitHub, to support maintainers, and contributors in making decisions and developing tasks of the projects. Therefore, these findings may help project leaders optimize the collaborations among developers and reduce entry barriers. Moreover, these findings may support the project collaborators in understanding the collaboration process and engaging others in the project.