Difference between revisions of "2015 IntelligentCodeCompletionwithBa"

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* ([[2015_IntelligentCodeCompletionwithBa|Proksch et al., 2015]]) ⇒ [[author::Sebastian Proksch]], [[author::Johannes Lerch]], and [[author::Mira Mezini]]. ([[year::2015]]). “[http://www.st.informatik.tu-darmstadt.de/artifacts/pbn/proksch-2015-Intelligent-Code-Completion-with-Bayesian-Networks.pdf Intelligent Code Completion with Bayesian Networks].” In: ACM Transactions on Software Engineering and Methodology (TOSEM) Journal, 25(1). [http://dx.doi.org/10.1145/2744200 doi:10.1145/2744200]  
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* ([[2015_IntelligentCodeCompletionwithBa|Proksch et al., 2015]]) [[author::Sebastian Proksch]], [[author::Johannes Lerch]], and [[author::Mira Mezini]]. ([[year::2015]]). “[http://www.st.informatik.tu-darmstadt.de/artifacts/pbn/proksch-2015-Intelligent-Code-Completion-with-Bayesian-Networks.pdf Intelligent Code Completion with Bayesian Networks].” In: ACM Transactions on Software Engineering and Methodology (TOSEM) Journal, 25(1). [http://dx.doi.org/10.1145/2744200 doi:10.1145/2744200]  
  
<B>Subject Headings:</B> [[Intelligent Code Completion Task]]; [[Bayesian Network Based Intelligent Code Completion Task]]; [[Code Completion Task]]
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<B>Subject Headings:</B> [[Intelligent Code Completion System]]; [[Bayesian Network Based Intelligent Code Completion System]]; [[Code Completion System]]
  
==Notes==
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== Notes ==
  
 
==Cited By==
 
==Cited By==
* http://scholar.google.com/scholar?q=%222015%22+Intelligent+Code+Completion+with+Bayesian+Networks
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* [[Google Scholar]]: ~ 29 [http://scholar.google.com/scholar?q=%222015%22+Intelligent+Code+Completion+with+Bayesian+Networks Citations] (Retrieved:2019-10-12).
* http://dl.acm.org/citation.cfm?id=2852270.2744200&preflayout=flat#citedby
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* [[ACM DL]]: ~ 9 [http://dl.acm.org/citation.cfm?id=2852270.2744200&preflayout=flat#citedby Citations] (Retrieved:2019-10-12).
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* [[Semantic Scholar]]: ~ 18 [https://www.semanticscholar.org/paper/Intelligent-Code-Completion-with-Bayesian-Networks-Proksch-Lerch/66911442b67460f0e602a1f56b7342dd593c1993#citing-papers Citations] (Retrieved:2019-10-12).
  
  
==Quotes==
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== Quotes ==
  
  
===Abstract===
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=== Abstract ===
  
[[Code completion]] is an [[integral part of modern]] <i> [[Integrated Development Environments </i> (IDEs)]]. </s>
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[[Code completion]] is an [[integral part of modern]] [[Integrated Development Environment|<i>Integrated Development Environments </i> (IDEs)]]. </s>
Developers often use it to explore <i> [[Application Programming Interfaces </i> (APIs)]]. </s>
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Developers often use it to explore [[Application Programming Interface|<i>Application Programming Interfaces </i> (APIs)]]. </s>
 
It is also useful to [[reduce]] the required amount of [[typing]] and to help avoid typos. </s>
 
It is also useful to [[reduce]] the required amount of [[typing]] and to help avoid typos. </s>
 
Traditional [[code completion system]]s propose all [[type-correct method]]s to the [[developer]]. </s>
 
Traditional [[code completion system]]s propose all [[type-correct method]]s to the [[developer]]. </s>
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More intelligent [[code completion system]]s have been proposed in [[prior work]] to [[reduce the list of proposed method]]s to [[relevant item]]s. </s>
 
More intelligent [[code completion system]]s have been proposed in [[prior work]] to [[reduce the list of proposed method]]s to [[relevant item]]s. </s>
  
[[This work]] extends one of these [[existing approach]]es, the <i> [[Best Matching Neighbor </i> (BMN) algorithm]]. </s>
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[[This work]] extends one of these [[existing approach]]es, the [[Best Matching Neighbor Algorithm|<i>Best Matching Neighbor </i> (BMN) algorithm]]. </s>
 
[[We]] introduce [[Bayesian network]]s as an [[alternative underlying model]], use additional [[context information]] for more precise [[recommendation]]s, and [[apply clustering technique]]s to improve [[model size]]s. </s>
 
[[We]] introduce [[Bayesian network]]s as an [[alternative underlying model]], use additional [[context information]] for more precise [[recommendation]]s, and [[apply clustering technique]]s to improve [[model size]]s. </s>
[[We]] compare our new [[approach]], <i> [[Pattern-based Bayesian Networks </i> (PBN)]], to the existing [[BMN algorithm]]. </s>
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[[We]] compare our new [[approach]], [[Pattern-based Bayesian Network|<i>Pattern-based Bayesian Networks </i> (PBN)]], to the existing [[BMN algorithm]]. </s>
 
[[We]] extend previously used [[evaluation methodologi]]es and, in addition to [[prediction quality]], [[we]] also [[evaluate model size]] and [[inference speed]]. </s>
 
[[We]] extend previously used [[evaluation methodologi]]es and, in addition to [[prediction quality]], [[we]] also [[evaluate model size]] and [[inference speed]]. </s>
  
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[[We]] also show that [[PBN]] can obtain comparable [[prediction]] quality to [[BMN]], while [[model size]] and [[inference speed scale]] better with [[large input size]]s. </s>
 
[[We]] also show that [[PBN]] can obtain comparable [[prediction]] quality to [[BMN]], while [[model size]] and [[inference speed scale]] better with [[large input size]]s. </s>
  
==References==
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== References ==
 
{{#ifanon:|
 
{{#ifanon:|
 
* 1. M. Bruch, M. Mezini, Improving Code Recommender Systems by Incorporating Domain Knowledge and Graphical Models, Proceedings of the 2008 International Workshop on Recommendation Systems for Software Engineering, November 09-09, 2008, Atlanta, Georgia [http://doi.acm.org/10.1145/1454247.1454267 doi:10.1145/1454247.1454267]
 
* 1. M. Bruch, M. Mezini, Improving Code Recommender Systems by Incorporating Domain Knowledge and Graphical Models, Proceedings of the 2008 International Workshop on Recommendation Systems for Software Engineering, November 09-09, 2008, Atlanta, Georgia [http://doi.acm.org/10.1145/1454247.1454267 doi:10.1145/1454247.1454267]
 
* 2. Marcel Bruch, Martin Monperrus, Mira Mezini, 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, August 24-28, 2009, Amsterdam, The Netherlands [http://doi.acm.org/10.1145/1595696.1595728 doi:10.1145/1595696.1595728]
 
* 2. Marcel Bruch, Martin Monperrus, Mira Mezini, 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, August 24-28, 2009, Amsterdam, The Netherlands [http://doi.acm.org/10.1145/1595696.1595728 doi:10.1145/1595696.1595728]
* 3. Marcel Bruch, Thorsten Schäfer, Mira Mezini, FrUiT: IDE Support for Framework Understanding, Proceedings of the 2006 OOPSLA Workshop on Eclipse Technology EXchange, p.55-59, October 22-23, 2006, Portland, Oregon, USA [http://doi.acm.org/10.1145/1188835.1188847 doi:10.1145/1188835.1188847]
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* 3. Marcel Bruch, Thorsten Schäfer, Mira Mezini, FrUiT: IDE Support for Framework Understanding, Proceedings of the 2006 OOPSLA Workshop on Eclipse Technology Exchange, p.55-59, October 22-23, 2006, Portland, Oregon, USA [http://doi.acm.org/10.1145/1188835.1188847 doi:10.1145/1188835.1188847]
 
* 4. Raymond P. L. Buse, Westley Weimer, Synthesizing API Usage Examples, Proceedings of the 34th International Conference on Software Engineering, June 02-09, 2012, Zurich, Switzerland
 
* 4. Raymond P. L. Buse, Westley Weimer, Synthesizing API Usage Examples, Proceedings of the 34th International Conference on Software Engineering, June 02-09, 2012, Zurich, Switzerland
 
* 5. Olivier Chapelle, Ya Zhang, A Dynamic Bayesian Network Click Model for Web Search Ranking, Proceedings of the 18th International Conference on World Wide Web, April 20-24, 2009, Madrid, Spain [http://doi.acm.org/10.1145/1526709.1526711 doi:10.1145/1526709.1526711]
 
* 5. Olivier Chapelle, Ya Zhang, A Dynamic Bayesian Network Click Model for Web Search Ranking, Proceedings of the 18th International Conference on World Wide Web, April 20-24, 2009, Madrid, Spain [http://doi.acm.org/10.1145/1526709.1526711 doi:10.1145/1526709.1526711]

Revision as of 06:50, 12 October 2019

Subject Headings: Intelligent Code Completion System; Bayesian Network Based Intelligent Code Completion System; Code Completion System

Notes

Cited By


Quotes

Abstract

Code completion is an integral part of modern Integrated Development Environments (IDEs). Developers often use it to explore Application Programming Interfaces (APIs). It is also useful to reduce the required amount of typing and to help avoid typos. Traditional code completion systems propose all type-correct methods to the developer. Such a list is often very long with many irrelevant items. More intelligent code completion systems have been proposed in prior work to reduce the list of proposed methods to relevant items.

This work extends one of these existing approaches, the Best Matching Neighbor (BMN) algorithm. We introduce Bayesian networks as an alternative underlying model, use additional context information for more precise recommendations, and apply clustering techniques to improve model sizes. We compare our new approach, Pattern-based Bayesian Networks (PBN), to the existing BMN algorithm. We extend previously used evaluation methodologies and, in addition to prediction quality, we also evaluate model size and inference speed.

Our results show that the additional context information we collect improves prediction quality, especially for queries that do not contain method calls. We also show that PBN can obtain comparable prediction quality to BMN, while model size and inference speed scale better with large input sizes.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 IntelligentCodeCompletionwithBaSebastian Proksch
Johannes Lerch
Mira Mezini
Intelligent Code Completion with Bayesian Networks10.1145/27442002015
AuthorSebastian Proksch +, Johannes Lerch + and Mira Mezini +
doi10.1145/2744200 +
titleIntelligent Code Completion with Bayesian Networks +
year2015 +