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Recommending Posts Concerning API Issues in Developer Q&A Sites
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Wei Malik, Haroon Godfrey, Michael W. |
| Abstract | Abstract—API design is known to be a challenging craft, as API designers must balance their elegant ideals against “real-world” concerns, such as utility, performance, backwards compatibility, and unforeseen emergent uses. However, to date, there is no principled method to collect or analyze API usability information that incorporates input from typical developers. In practice, developers often turn to Q&A websites such as stackoverflow.com (SO) when seeking expert advice on API use; the popularity of such sites has thus led to a very large volume of unstructured information that can be searched with diligence for answers to specific questions. The collected wisdom within such sites could, in principle, be of great help to API designers to better support developer needs, if only it could be collected, analyzed, and distilled for practical use. In this paper, we present a methodology that combines several techniques, including social network analysis and topic mining, to recommend SO posts that are likely to concern API design-related issues. To establish a comparison baseline, we introduce two more recommendation approaches: a reputation-based recommender and a random recommender. We have found that when applied to Q&A discussion of two popular mobile platforms, Android and iOS, our methodology achieves up to 93 % accuracy and is more stable with its recommendations when compared to the two baseline techniques. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Api Issue Developer Site Api Designer Expert Advice Baseline Technique Recommendation Approach Abstract Api Design Api Use Backwards Compatibility Typical Developer Elegant Ideal Principled Method Real-world Concern Api Usability Information Practical Use Challenging Craft Social Network Analysis Collected Wisdom Concern Api Design-related Issue Great Help Reputation-based Recommender Large Volume Unforeseen Emergent Us Unstructured Information Several Technique Popular Mobile Platform Specific Question Support Developer Need Random Recommender Topic Mining Comparison Baseline |
| Content Type | Text |
