You are in : Home » Results & submissions » Select UOA » 11 - Computer Science and Informatics » View submission: Lancaster University » Outputs » Detail

Output details

11 - Computer Science and Informatics

Lancaster University

Return to search Previous output Next output

Output 51 of 120 in the submission

Output title

Learning from examples to improve code completion systems

Type

E - Conference contribution

DOI

10.1145/1595696.1595728

Name of conference/published proceedings

Proceedings of the 7th joint meeting of the European Software Engineering Conference and the ACM Symposium on the Foundations of Software Engineering (ESEC/FSE '09)

Volume number

Issue number

First page of article

213

ISSN of proceedings

Year of publication

2009

URL

http://dl.acm.org/citation.cfm?doid=1595696.1595728

Number of additional authors

Additional information

<08> The first work to use machine learning on big data extracted by static analysis of software repositories to enable intelligent code completion. Recommends only frequently observed API usage patterns in a specific context, thus significantly increasing the efficiency of software development. The work initiated the Eclipse CodeRecommenders open-source project, now a popular feature of the Eclipse distribution, which is used by more than 6 million Java developers. The work was published in a leading software engineering conference (ESEC/FSE), acceptance rate 32/217 (15%) in 2009, and has found great attention in the scientific community as reflected by the citation rate.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract