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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhan, Yun Wang, Tao Li, Zhixing Yin, Gang Zhang, Yang |
| Abstract | The Internet-scale open source software (OSS) production in various communities are generating abundant reusable resources for software developers. However, how to retrieve and reuse the desired and mature software from huge amounts of candidates is a great challenge: there are usually big gaps between the user application contexts (that often used as queries) and the OSS key words (that often used to match the queries). In this paper, we define the scenario-based query problem for OSS retrieval, and then we propose a novel approach to reformulate the raw query by leveraging the crowd wisdom from millions of developers to improve the retrieval results. We build a software-specific domain lexical database based on the knowledge in open source communities, by which we can expand and optimize the input queries. The experiment results show that, our approach can reformulate the initial query effectively and outperforms other existing search engines significantly at finding mature software. |
| Starting Page | 36 |
| Ending Page | 44 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450348294 |
| DOI | 10.1145/2993717.2993723 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-09-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Query reformulation Software retrieval Crowd wisdom |
| Content Type | Text |
| Resource Type | Article |
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