Bootstrapping cookbooks for APIs from crowd knowledge on Stack Overflow

Abstract

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.

Topic modeling in software engineering research

“…To provide an example of how the number of topics differed even when the same type of data was analyzed with the same topic modeling technique, we looked at studies that applied LDA in textual data from developer communication (mostly Q&A posts) to propose an approach to support documentation. For these papers we found one paper that did not mention k (Henß et al 2012), one paper that modeled different numbers of topics (k = 10, 20, 30) (Asuncion et al 2010), one paper that modeled k = 15 (Souza et al 2019) and another paper that modeled k = 40 (Wang et al 2015). This illustrates that there is no common or recommended practice that can be derived from the papers.…”

“…-Architecting: tasks related to architecture decision making, such as selection of cloud or mash-up services (e.g., Belle et al (2016)); -Bug handling: bug-related tasks, such as assigning bugs to developers, prediction of defects, finding duplicate bugs, or characterizing bugs (e.g., Naguib et al (2013)); -Coding: tasks related to coding, e.g., detection of similar functionalities in code, reuse of code artifacts, prediction of developer behaviour (e.g., Damevski et al (2018)); -Documentation: support software documentation, e.g., by localizing features in documentation, automatic documentation generation (e.g., Souza et al (2019)); -Maintenance: software maintenance-related activities, such as checking consistency of versions of a software, investigate changes or use of a system (e.g., Silva et al (2019)); -Refactoring: support refactoring, such as identifying refactoring opportunities and removing bad smell from source code (e.g., Bavota et al (2014b)); -Requirements: related to software requirements evolution or recommendation of new features (e.g., Galvis Carreno and Winbladh ( 2012)); -Testing: related to identification or prioritization of test cases (e.g., Thomas et al (2014)).…”

Richen: Automated enrichment of Git documentation with usage examples and scenarios

“…Such studies have provided us with valuable conceptual blueprints and benchmarks. Specifically, the extracted knowledge can be generally bifurcated into API usage 11,22,35,[58][59][60][61] and API tips/opinions. 62,63 API usages offer procedural steps for employing the API to realize specific functionalities, commonly incorporating descriptive text and code samples.…”

“…62,63 API usages offer procedural steps for employing the API to realize specific functionalities, commonly incorporating descriptive text and code samples. Such usages frequently exhibit a task-oriented or best-practice approach, elucidating how to achieve a particular goal 11,35,58 or delineating best practices for API usage. 59 This aligns with the problem-centric nature of Stack Overflow as a resource.…”

Automatic API Usage Scenario Documentation from Technical Q&A Sites

“…Li et al[36] propose CnCxL2R, a software documentation recommendation engine by incorporating the content of the official documentation with social context from SO. Souza et al[79,80] propose a semi-automatic approach based on topic modeling to build cookbooks for APIs by organizing knowledge available in SO. Topic modeling was also applied by Campbell et al[11] on a combined dataset of SO, and PHP and Python official documentation to determine missing topic coverage in the official documentation.…”