Details

Autor(en) / Beteiligte

• Nam, Daye
• Horvath, Amber
• Macvean, Andrew
• Myers, Brad
• Vasilescu, Bogdan

Titel

MARBLE: mining for boilerplate code to identify API usability problems

Ist Teil von

• 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2019, p.615-627

Ort / Verlag

Piscataway, NJ, USA: IEEE Press

Erscheinungsjahr

2019

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• Designing usable APIs is critical to developers' productivity and software quality, but is quite difficult. One of the challenges is that anticipating API usability barriers and real-world usage is difficult, due to a lack of automated approaches to mine usability data at scale. In this paper, we focus on one particular grievance that developers repeatedly express in online discussions about APIs: "boilerplate code." We investigate what properties make code count as boilerplate, the reasons for boilerplate, and how programmers can reduce the need for it. We then present MARBLE, a novel approach to automatically mine boilerplate code candidates from API client code repositories. MARBLE adapts existing techniques, including an API usage mining algorithm, an AST comparison algorithm, and a graph partitioning algorithm. We evaluate MARBLE with 13 Java APIs, and show that our approach successfully identifies both already-known and new API-related boilerplate code instances.