Learning Semantically Related Words in Software Through Word Embedding

doi:10.3969/j.issn.1006-2475.2017.09.004

Abstract

Abstract: Searching for previously written code is important for software development and maintenance. The same as traditional information retrieval, the inherent difficulty of keyword based code search is vocabulary mismatch between user query and retrieved code. To improve the accuracy of code search, learning semantically related words in software for query expansion is needed. This paper designs a Word Embedding based method to learn semantically related words in software, and obtains semantically related words for 19332 words through training it on Stack Overflow documents. The experiment results show that the learned semantically related words can effectively improve code search accuracy.

Key words: code search, query expansion, semantically related words

CLC Number:

TP311

HU Wang-sheng. Learning Semantically Related Words in Software Through Word Embedding[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.09.004.

References

［1］ Shepherd D, Damevski K, Ropski B, et al. Sando: An extensible local code search framework［C］// Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering. 2012:1-2.
［2］ Furnas G W, Landauer T K, Gomez L M, et al. The vocabulary problem in human-system communication［J］. Communications of the ACM, 1987,30(11):964-971.
［3］ Shepherd D, Fry Z P, Hill E, et al. Using natural language program analysis to locate and understand action-oriented concerns［C］// Proceedings of the 6th International Conference on Aspect-oriented Software Development. 2007:212-224.
［4］ Hill E. Integrating Natural Language and Program Structure Information to Improve Software Search and Exploration［D］. University of Delaware, 2010.
［5］ Yang Jinqiu, Lin Tan. Inferring semantically related words from software context［C］// Proceedings of the 9th IEEE Working Conference on Mining Software Repositories. 2012:161-170.
［6］ Yang Jinqiu, Lin Tan. Swordnet: Inferring semantically related words from software context［J］. Empirical Software Engineering, 2014,19(6):1856-1886.
［7］ Tian Yuan, Lo D, Lawall J. Sewordsim: Software-specific word similarity database［C］// Proceedings of the 36th International Conference on Software Engineering. 2014:568-571.
［8］ Sando. Sando Code Search Tool［EB/OL］. http://sandosearch.weebly.com/, 2016-12-01.
［9］ Aragon Consulting Group. Krugle Code Search［EB/OL］. http://www.krugle.com/, 2016-12-01.
［10］ Linstead E, Bajracharya S, Ngo T, et al. Sourcerer: Mining and searching internet-scale software repositories［J］. Data Mining and Knowledge Discovery, 2009,18(2):300-336.
［11］ Merriam-Webster. Merriam-Webster English Dictionary and Thesaurus［EB/OL］. http://www.merriam-webster.com/, 2016-12-01.
［12］ Princeton University. WordNet［EB/OL］. http://wordnet.princeton.edu/, 2016-12-01.
［13］ Sridhara G, Hill E, Pollock L, et al. Identifying word relations in software: A comparative study of semantic similarity tools［C］// Proceedings of the 16th IEEE International Conference on Program Comprehension. 2008:123-132.
［14］ Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning［C］// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 2010:384-394.
［15］ Mikolov T, Chen Kai, Corrado G, et al. Efficient estimation of word representations in vector space［C］// Proceedings of the 1st International Conference on Learning Representations. 2013.
［16］ Martin Porter. The Porter Stemming Algorithm［EB/OL］. https://tartarus.org/martin/PorterStemmer/, 2016-12-01.
［17］ Word2vec. Tool for Computing Continuous Distributed Representations of Words［EB/OL］. https://code.google.com/archive/p/word2vec/, 2016-12-01.
［18］ Shepherd D. Action-oriented Concerns［EB/OL］. https://www.eecis.udel.edu/~gibson/context/action_oriented_concerns.txt, 2016-12-01.

[1]	WANG Qian-qian, LUO Wen-bing. Markov Network Query Expansion Model Based on Term Importance [J]. Computer and Modernization, 2017, 0(11): 89-94.
[2]	LYU Fei. An Efficient Code Search Algorithm [J]. Computer and Modernization, 2015, 0(3): 9-14.
[3]	FENG Jun， XU Xiao， TANG Zhixian， BIAN Yilu. Distributed Semantic Retrieval Method for Water Information Resources Directory Services [J]. Computer and Modernization, 2015, 0(2): 122-.
[4]	TIAN Yong-chang1, LI Ying2. Query Expansion Based on Interest Model [J]. Computer and Modernization, 2014, 0(7): 36-39.
[5]	WANG Man;WU Zhen-zhong. Domain Ontology-based Semantic Search: Weighted Shortest Path Method [J]. Computer and Modernization, 2013, 1(9): 1-7.
[6]	HUANG Ming-xuan;FENG Ping;XIE Tong-yi. [J]. Computer and Modernization, 2012, 1(200): 53-03.