Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

Intent classification for a management conversational assistant

Intent classification is an essential step in processing user input to a conversational assistant. This work investigates techniques of intent classification of chat messages used for communication among software development teams with the aim of building an intent classifier for a management conversational assistant integrated into modern communication platforms used by developers. Experiments conducted using rule-based and common ML techniques have shown that careful choice of classification features has a significant impact on performance, and the best performing model was able to obtain a classification accuracy of 72%. A set of techniques for extracting useful features for text classification in the software engineering domain was also implemented and tested

Qual a relevância da literatura open-source sob a perspectiva de profissionais e estudantes de graduação

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Com um aumento significativo de trabalhos de pesquisa em Engenharia de Software nos últimos anos, especialmente daqueles focados no modelo Open-Source, devido à sua ascendência relativamente recente, uma questão que naturalmente surge diz respeito à sua relevância. Diante desse questionamento, esse trabalho busca investigar se a pesquisa em Engenharia de Software, focada particularmente no modelo Open-Source, produz resultados considerados relevantes na percepção dos usuários. Para investigar a relevância percebida da literatura disponível nós conduzimos dois Surveys: um na Universidade de Brasília (UnB), em que nós convidamos os estudantes a avaliar a relevância de ideias e resultados contidos em sumários construídos a partir de trabalhos de pesquisa publicados em um período de dez anos, e outro com profissionais e pesquisadores que contribuem de alguma forma com as comunidades Open-Source, onde a relevância de artigos publicados em um período de cinco anos foi avaliada com base na leitura do título e resumo originais dos trabalhos. Dessa forma, é possível apresentar um feedback dos estudantes, profissionais e pesquisadores, possibilitando o discernimento de questões de pesquisa que são consideradas relevantes e consequentemente passíveis de serem disseminadas dentro da comunidade Open-Source e acadêmica. Durante a investigação da relevância dos trabalhos selecionados, a abordagem proposta considerou duas questões: Uma sobre o escopo dos trabalhos identificados e outra sobre a relevância percebida desses trabalhos. Para a primeira questão, foram conduzidos dois mapeamentos sistemáticos da literatura em bases distintas, os quais revelaram um conjunto de trabalhos compostos por uma grande diversidade de resultados. Utilizando sumários elaborados a partir desses trabalhos para o primeiro Survey e os próprios resumos para o segundo, foram então aplicados os Surveys aos estudantes, profissionais e pesquisadores. Nossos achados representam um cenário muito favorável para a pesquisa voltada ao modelo open source, indicando que 77.01% dos estudantes consideram os trabalhos relevantes e que 80.56% dos pesquisadores e desenvolvedores também consideraram os trabalhos como relevantes.The number of Software Engineering research papers has grown significantly over the last few years, especially those related to the open source model. Naturally, this fact raises the question of whether the research on these areas are considered to be relevant or not. This paper aims at accessing the perspective of the open source community as well as the perspective of undergraduate students regarding the relevance of the open source research. To answer about the relevance of available work, two questions were addressed: one about the scope of the studies and another about the perceived quality of these works. For the first one, two Systematic Literature Mappings were performed, each for a different survey to be conducted, revealing two set of works composed by a great diversity of results. Using these identified works, two different surveys were conducted, one with developers and researchers from several open source communities around the world and another at University of Brasília (UnB) where undergraduate students of Computer Science and related courses were invited to rate the relevance of the selected research papers. Both surveys revealed a very positive outlook on the relevance of this research area, where 77.01% of the students and 80.56% of the the open source practitioners rated the works as relevant. With these results, in addition to providing an overview of the current open source research scenario, it is also possible to give feedback from the open source community and students, providing a way to produce useful and, consequently, more disseminated works among open source practitioners

A Microstructural Approach to Self-Organizing:The Emergence of Attention Networks

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants' resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large opensource software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost.</p