Search for: All records

Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers (1) develop a more comprehensive understanding of the libraries’ similarities and distinctions and (2) make more robust, appropriate library selections. We designed a novel interactive interface, ParaLib, and used it as a technical probe to explore to what extent many side-by-side concepted-annotated examples can facilitate the library comparison and selection process. A within-subjects user study with 20 programmers shows that, when using ParaLib, participants made more consistent, suitable library selections and provided more comprehensive summaries of libraries’ similarities and differences. 

APIs are becoming the fundamental building block of modern software and their usability is crucial to programming efficiency and software quality. Yet API designers find it hard to gather and interpret user feedback on their APIs. To close the gap, we interviewed 23 API designers from 6 companies and 11 open-source projects to understand their practices and needs. The primary way of gathering user feedback is through bug reports and peer reviews, as formal usability testing is prohibitively expensive to conduct in practice. Participants expressed a strong desire to gather real-world use cases and understand users' mental models, but there was a lack of tool support for such needs. In particular, participants were curious about where users got stuck, their workarounds, common mistakes, and unanticipated corner cases. We highlight several opportunities to address those unmet needs, including developing new mechanisms that systematically elicit users' mental models, building mining frameworks that identify recurring patterns beyond shallow statistics about API usage, and exploring alternative design choices made in similar libraries.