StackOverflow.com (SO) is a Question and Answer service oriented to support collaboration among developers in order to help them solving their issues related to software development. In SO, developers post questions related to a programming topic and other members of the site can provide answers to help them. The information available on this type of service is also known as "crowd knowledge" and currently is one important trend in supporting activities related to software development and maintenance.We present an approach that makes use of "crowd knowledge" available in SO to recommend information that can assist developers in their activities. This strategy recommends a ranked list of pairs of questions/answers from SO based on a query (list of terms). The ranking criteria is based on two main aspects: the textual similarity of the pairs with respect to the query (the developer's problem) and the quality of the pairs. Moreover, we developed a classifier to consider only "how-to" posts. We conducted an experiment considering programming problems on three different topics (Swing, Boost and LINQ) widely used by the software development community to evaluate the proposed recommendation strategy. The results have shown that for 77.14% of the assessed activities, at least one recommended pair proved to be useful concerning the target programming problem. Moreover, for all activities, at least one recommended pair had a source code snippet considered reproducible or almost reproducible.

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Stack Overflow (SO) is a question and answer service directed to issues related to software development. In SO, developers post questions related to a programming topic and other members of the site can provide answers to help them. The information available on this type of service is also known as 'crowd knowledge' and currently is one important trend in supporting activities related to software development.We present an approach that makes use of 'crowd knowledge' in SO to recommend information that can assist developer activities. This strategy recommends a ranked list of question-answer pairs from SO based on a query. The criteria for ranking are based on three main aspects: the textual similarity of the pairs with respect to the query related to the developer's problem, the quality of the pairs, and a filtering mechanism that considers only 'how-to' posts. We conducted an experiment considering programming problems on three different topics (Swing, Boost and LINQ) widely used by the software development community to evaluate the proposed recommendation strategy. The results have shown that for Lucene + Score + How-to approach, 77.14% of the assessed activities have at least one recommended pair proved to be useful concerning the target programming problem.

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Background: Automatic program repair aims to reduce costs associated with defect repair.The detection and characterization of common bug-fix patterns in software repositories play an important role in advancing this field. Aim: In this paper, we characterize the occurrence of known bug-fix patterns in Java repositories at an unprecedented large scale. Furthermore, we propose a novel automatic technique for unveiling frequent and isolated repair actions corresponding to realistic bug fixes in Java. Method: The study was conducted for Java GitHub projects organized in two distinct data sets. The first data set (Boa) contains more than 4 million bug-fix commits from 101 471 projects. The second data set (Defects4J) contains 369 real bug fixes from five open-source projects. Results: We characterized the prevalence of the five most common bug-fix patterns (identified in the work of Pan et al) in those bug fixes. The combined results showed direct evidence that developers often forget to add IF preconditions in the code.Conclusion: We discover a total of 155 repair actions from Defects4J patches and discuss 10 pervasive repair actions that occur across all analyzed Java projects. Moreover, the overall Precision and Recall values for the clustering approach were 0.62 and 0.64, respectively. KEYWORDS automated program repair, bug-fix patterns, locality-sensitive hashing

Q&A sites are attracting growing interest of software developers. The categorization of questions in terms of user concerns would open new opportunities to extract valuable information from millions of posts.This paper presents a comparison between different classification algorithms to find the one that best classifies questions from Q&A sites, such as, Stack Overflow. In the classification process, we used the following classification algorithms: Naive Bayes, Multilayer Perceptron, Support Vector Machine, K -Nearest Neighbors, J4.8 Decision Tree and Random Forests.We conducted an experimental study with Stack Overflow questions with posts equally divided into three domain categories: How-to-do-it, Need-to-know and Seeking-something. The attributes were extracted from a textual analysis of the title and body of each question. We considered a total of 8 attributes to get the data for each question. We found a classifier with an overall success rate of 84.16% and 92.5% on How-to-do-it category.

Agradeço... A Deus, por minha vida e pela minha saúde. Aos meus pais Olavo Luiz de Sousa Campos e Rose Mary Machado Cunha pelo amor incondicional, carinho, apoio, dedicação e perseverança em todos os momentos. Obrigado mãe por ter sido a fortaleza para a gente ter chegado até aqui. Sem o seu apoio, esse sonho não seria possível. Às minhas irmãs Tatiana Cunha Campos e Izabella Cunha Campos pelos conselhos, incentivos, carinho e amor durante esta etapa da minha vida. À minha namorada Lycea Maria Maciel Nogueira por ser a minha maior incentivadora a encarar este desaĄo e pela compreensão durante este projeto. Você me deu muita força, tranquilidade, paz, alegria e amor. A todos os professores da Faculdade de Computação (FACOM) que contribuíram no meu processo de graduação e pós

Context: Well established libraries typically have API documentation. However, they frequently lack examples and explanations, possibly making difficult their effective reuse. Stack Overflow is a question-and-answer website oriented to issues related to software development. Despite the increasing adoption of Stack Overflow, the information related to a particular topic (e.g., an API) is spread across the website. Thus, Stack Overflow still lacks organization of the crowd knowledge available on it.Objective: Our target goal is to address the problem of the poor quality documentation for APIs by providing an alternative artifact to document them based on the crowd knowledge available on Stack Overflow, called crowd cookbook. A cookbook is a recipe-oriented book, and we refer to our cookbook as crowd cookbook since it contains content generated by a crowd. The cookbooks are meant to be used through an exploration process, i.e. browsing.Method: In this paper, we present a semi-automatic approach that organizes the crowd knowledge available on Stack Overflow to build cookbooks for APIs. We have generated cookbooks for three APIs widely used by the software development community: SWT, LINQ and QT. We have also defined desired properties that crowd cookbooks must meet, and we conducted an evaluation of the cookbooks against these properties with human subjects.Results: The results showed that the cookbooks built using our approach, in general, meet those properties. As a highlight, most of the recipes were considered appropriate to be in the cookbooks and have self-contained information.Conclusions: We concluded that our approach is capable to produce adequate cookbooks automatically, which can be as useful as manually produced cookbooks. This opens an opportunity for API designers to enrich existent cookbooks with the different points of view from the crowd, or even to generate initial versions of new cookbooks.