Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2016

Abstract

Developers prefer to utilize third-party libraries when they implement some functionalities and Application Programming Interfaces (APIs) are frequently used by them. Facing an unfamiliar API, developers tend to consult tutorials as learning resources. Unfortunately, the segments explaining a specific API scatter across tutorials. Hence, it remains a challenging issue to find the relevant segments. In this study, we propose a more accurate model to find the exact tutorial fragments explaining APIs. This new model consists of a text classifier with domain specific features. More specifically, we discover two important indicators to complement traditional text based features, namely co-occurrence APIs and knowledge based API extensions. In addition, we incorporate Word2Vec, a semantic similarity metric to enhance the new model. Extensive experiments over two publicly available tutorial datasets show that our new model could find up to 90% fragments explaining APIs and improve the state-of-the-art model by up to 30% in terms of F-measure.

Keywords

Tutorials, Androids, Humanoid robots, Programming, Feature extraction, Semantics, Animation

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER): March 14-18, Osaka: Proceedings

ISBN

9781509018550

Identifier

10.1109/SANER.2016.59

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://doi.org/10.1109/SANER.2016.59

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