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What’s Spain’s Paris? Mining analogical libraries from Q&A discussions

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Abstract

Third-party libraries are an integral part of many software projects. It often happens that developers need to find analogical libraries that can provide comparable features to the libraries they are already familiar with for different programming languages or different mobile platforms. Existing methods to find analogical libraries are limited by the community-curated list of libraries, blogs, or Q&A posts, which often contain overwhelming or out-of-date information. In this paper, we present a new approach to recommend analogical libraries based on a knowledge base of analogical libraries mined from tags of millions of Stack Overflow questions. The novelty of our approach is to solve analogical-library questions by combining state-of-the-art word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. Given a library and a recommended analogical library, our approach further extracts questions and answer snippets in Stack Overflow about comparison of analogical libraries, which can potentially offer useful information scents for developers to further their investigation of the recommended analogical libraries. We implement our approach in a proof-of-concept web application and more than 34.8 thousands of users visited our website from November 2015 to August 2017. Our evaluation shows that our approach can make accurate recommendation of analogical libraries. We also demonstrate the usefulness of our analogical-library recommendations by using them to answer analogical-library questions in Stack Overflow. Google Analytics of our website traffic and analysis of the visitors’ interaction with website contents provide the insights into the usage patterns and the system design of our web application.

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Notes

  1. https://en.wikipedia.org/wiki/List_of_unit_testing_frameworks#cite_note-496

  2. https://meta.stackexchange.com/questions/77808/does-it-matter-the-order-you-tag-your-question

  3. https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html

  4. A complete list can be found at https://graphofknowledge.appspot.com/libCategory

  5. https://code.google.com/p/word2vec/

  6. https://en.wikipedia.org/wiki/Search_engine_optimization

  7. https://webmasters.googleblog.com/2016/03/continuing-to-make-web-more-mobile.html

  8. https://goo.gl/nb5czF

  9. The detailed threshold to discriminate popular or unpopular queries is a commercial secret of Google.

  10. https://github.com/ClearTK/cleartk

  11. The list of sampled questions can be found at https://graphofknowledge.appspot.com/questions

  12. https://analytics.google.com/

  13. As most search engine robots do not activate javascript, robot traffic is not counted in Google Analytics (Google 2016).

  14. http://ipinfo.io/

  15. http://stackoverflow.com/questions/212151/

  16. www.similarweb.com/

  17. http://alternativeto.net/

  18. https://graphofknowledge.appspot.com/similartech

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Acknowledgements

We’d like to appreciate the valuable review from reviewers. This work is partially supported by the seed grant from Monash University.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Melbourne, Australia

    Chunyang Chen

  2. College of Engineering & Computer Science, Australian National University, Canberra, Australia

    Zhenchang Xing

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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  1. Chunyang Chen
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  2. Zhenchang Xing
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  3. Yang Liu
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Correspondence to Chunyang Chen.

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Communicated by: Yasutaka Kamei

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Chen, C., Xing, Z. & Liu, Y. What’s Spain’s Paris? Mining analogical libraries from Q&A discussions. Empir Software Eng 24, 1155–1194 (2019). https://doi.org/10.1007/s10664-018-9657-y

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