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. 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. 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. 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. 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.
Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Overflow. We present an approach to automatically augment API documentation with "insight sentences" from Stack Overflow -- sentences that are related to a particular API type and that provide insight not contained in the API documentation of that type. Based on a development set of 1,574 sentences, we compare the performance of two state-of-the-art summarization techniques as well as a pattern-based approach for insight sentence extraction. We then present SISE, a novel machine learning based approach that uses as features the sentences themselves, their formatting, their question, their answer, and their authors as well as part-of-speech tags and the similarity of a sentence to the corresponding API documentation. With SISE, we were able to achieve a precision of 0.64 and a coverage of 0.7 on the development set. In a comparative study with eight software developers, we found that SISE resulted in the highest number of sentences that were considered to add useful information not found in the API documentation. These results indicate that taking into account the meta data available on Stack Overflow as well as part-of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Overflow data.