caa a22 4500 44583529X CHVBK 20180317145328.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s10515-011-0083-2 doi (NATIONALLICENCE)springer-10.1007/s10515-011-0083-2 Code completion of multiple keywords from abbreviated input [Elektronische Daten] [Sangmok Han, David Wallace, Robert Miller] 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. Springer Science+Business Media, LLC, 2011 Abbreviation completion nationallicence Multiple keywords nationallicence Code completion nationallicence Hidden Markov model nationallicence Code assistants nationallicence Abbreviation nationallicence Extrapolation nationallicence Data mining nationallicence Han Sangmok MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut Wallace David MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut Miller Robert MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut Automated Software Engineering Springer US; http://www.springer-ny.com 18/3-4(2011-12-01), 363-398 0928-8910 18:3-4<363 2011 18 10515 https://doi.org/10.1007/s10515-011-0083-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10515-011-0083-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Han Sangmok MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut NATIONALLICENCE 700 1- Wallace David MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut NATIONALLICENCE 700 1- Miller Robert MIT, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA aut NATIONALLICENCE 773 0- Automated Software Engineering Springer US; http://www.springer-ny.com 18/3-4(2011-12-01), 363-398 0928-8910 18:3-4<363 2011 18 10515 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer