Extracting problematic API features from forum discussions

Software engineering activities often produce large amounts of unstructured data. Useful information can be extracted from such data to facilitate software development activities, such as bug reports management and documentation provision. Online forums, in particular, contain extensive valuable inf...

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Bibliographic Details
Main Authors: Yingying Zhang, Daqing Hou
Format: Conference Proceeding
Language:English
Subjects:
APIs
AWT/Swing
Data mining
Design Feedback
Dictionaries
Feature extraction
Information Extraction
Message systems
Online Forums
Pattern matching
Software
Tutorials
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Summary:Software engineering activities often produce large amounts of unstructured data. Useful information can be extracted from such data to facilitate software development activities, such as bug reports management and documentation provision. Online forums, in particular, contain extensive valuable information that can aid in software development. However, no work has been done to extract problematic API features from online forums. In this paper, we investigate ways to extract problematic API features that are discussed as a source of difficulty in each thread, using natural language processing and sentiment analysis techniques. Based on a preliminary manual analysis of the content of a discussion thread and a categorization of the role of each sentence therein, we decide to focus on a negative sentiment sentence and its close neighbors as a unit for extracting API features. We evaluate a set of candidate solutions by comparing tool-extracted problematic API design features with manually produced golden test data. Our best solution yields a precision of 89%. We have also investigated three potential applications for our feature extraction solution: (i) highlighting the negative sentence and its neighbors to help illustrate the main API feature; (ii) searching helpful online information using the extracted API feature as a query; (iii) summarizing the problematic features to reveal the "hot topics" in a forum.
ISSN:1092-8138
DOI:10.1109/ICPC.2013.6613842