Class-based n-gram models of natural language
Computational Linguistics
Proceedings of the 19th IEEE international conference on Automated software engineering
Jungloid mining: helping to navigate the API jungle
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
How Are Java Software Developers Using the Eclipse IDE?
IEEE Software
XSnippet: mining For sample code
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Using task context to improve programmer productivity
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Predicting sentences using N-gram language models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Automated Software Engineering
Learning from examples to improve code completion systems
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Sequentially finding the N-best list in hidden Markov models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
How Program History Can Improve Code Completion
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Code Completion from Abbreviated Input
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
A probabilistic flexible abbreviation expansion system for users with motor disabilities
Accessible Design'05 Proceedings of the 2005 international conference on Accessible Design in the Digital World
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Abbreviation Completion is a novel technique to improve the efficiency of code-writing by supporting code completion of multiple keywords based on non-predefined abbreviated input--a different approach from conventional code completion that finds one keyword at a time based on an exact character match. Abbreviated input consisting of abbreviated keywords and non-alphanumeric characters between each abbreviated keyword (e.g. pb st nm) is expanded into a full expression (e.g. public String name) by a Hidden Markov Model learned from a corpus of existing code and abbreviation examples. The technique does not require the user to memorize abbreviations and provides incremental feedback of the most likely completions.In addition to code completion by disabbreviation of multiple keywords, abbreviation completion supports prediction of the next keywords and non-alphanumeric characters of a code completion candidate, a technique called code completion by extrapolation. The system finds the most likely next keywords and non-alphanumeric characters using an n-gram model of programming language. This enables a code completion scenario in which a user first types a short abbreviated expression to complete the beginning part of a desired full expression and then uses the extrapolation feature to complete the remaining part without further typing.This paper presents the algorithm for abbreviation completion, integrated with a new user interface for multiple-keyword code completion. We tested the system by sampling 4919 code lines from open source projects and found that more than 99% of the code lines could be resolved from acronym-like abbreviations. The system could also extrapolate code completion candidates to complete the next one or two keywords with the accuracy of 96% and 82%, respectively. A user study of code completion by disabbreviation found 30% reduction in time usage and 41% reduction of keystrokes over conventional code completion.