Code completion from abbreviated input

ASE2009 - 24th IEEE/ACM International Conference on Automated Software Engineering

Volumn , Issue , 2009, Pages 332-343

  Han, Sangmok ^a   Wallace, David R ^a   Miller, Robert C ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Abbreviation; Code assistants; Code completion; Data mining; Hidden Markov model; Multiple keywords

Indexed keywords

ABBREVIATION; CODE ASSISTANTS; CODE LINE; CODE-WRITING; NOVEL TECHNIQUES; OPEN SOURCE PROJECTS; USER STUDY;

COMPUTER SOFTWARE; DATA MINING; USER INTERFACES;

HIDDEN MARKOV MODELS;

EID: 77952122971     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASE.2009.64     Document Type: Conference Paper

References (10)

1. L. R. Rabiner, "Tutorial on hidden Markov models and selected applications in speech recognition," In Proc. IEEE, vol.77, 1989.
2. "Abbrevs," in GNU Emacs Manual. http://www.gnu.org/software/ emacs/manual/emacs.html.
3. "Editor Template," in Eclipse Ganymede Documentation. http://help.eclipse.org/ganymede/index.jsp.
4. G. Little and R. C. Miller, "Keyword programming in Java," In Proc. ASE, vol.16, pp. 37-71, 2007.
5. N. Sahavechaphan and K. Claypool, "XSnippet: Mining For sample code," OOPSLA, pp 413-430, 2006.
6. D. Mandelin, L. Xu, R. Bodik, and D. Kimelman, "Jungloid mining: Helping to navigate the API jungle," In Proc. PLDI, 2005.
7. R. Robbes and M. Lanza, "How Program History Can Improve Code Completion," In Proc. ASE, 2008.
8. M. Kersten and G. C. Murphy, "Using task context to improve programmer productivity," In Proc. FSE, 2006.
9. F. K. Soong and E. F. Huang, "A tree-trellis based fast search for finding the n-best sentence hypotheses in continuous speech recognition," In Proc. ICASSP, vol.1, pp 705-708, 1991.
10. D. Nilsson and J. Goldberger, "An efficient algorithm for sequentially finding the n-best list," In Proc. IJCAI, 2001.