NDLI: Learning to Rank for Question-Oriented Software Text Retrieval (T)

Content Provider	IEEE Xplore Digital Library
Author	Yanzhen Zou Ting Ye Yangyang Lu Mylopoulos, J. Lu Zhang
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Toronto, Toronto, ON, Canada (Mylopoulos, J.) \|\| Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China (Yanzhen Zou; Ting Ye; Yangyang Lu; Lu Zhang)
Abstract	Question-oriented text retrieval, aka natural language-based text retrieval, has been widely used in software engineering. Earlier work has concluded that questions with the same keywords but different interrogatives (such as how, what) should result in different answers. But what is the difference? How to identify the right answers to a question? In this paper, we propose to investigate the "answer style" of software questions with different interrogatives. Towards this end, we build classifiers in a software text repository and propose a re-ranking approach to refine search results. The classifiers are trained by over 16,000 answers from the StackOverflow forum. Each answer is labeled accurately by its question's explicit or implicit interrogatives. We have evaluated the performance of our classifiers and the refinement of our re-ranking approach in software text retrieval. Our approach results in 13.1% and 12.6% respectively improvement with respect to text retrieval criteria nDCG@1 and nDCG@10 compared to the baseline. We also apply our approach to FAQs of 7 open source projects and show 13.2% improvement with respect to nDCG@1. The results of our experiments suggest that our approach could find answers to FAQs more precisely.
Starting Page	1
Ending Page	11
File Size	477217
Page Count	11
File Format	PDF
e-ISBN	9781509000258
DOI	10.1109/ASE.2015.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software Feature extraction Indexes Software engineering Buildings Training Search engines rank text retrieval interrogative classifier
Content Type	Text
Resource Type	Article

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