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Intelligent Code Completion with Bayesian Networks

Proksch, Sebastian and Lerch, Johannes and Mezini, Mira :
Intelligent Code Completion with Bayesian Networks.
[Online-Edition: http://doi.acm.org/10.1145/2744200]
In: ACM Transactions on Software Engineering and Methodology (TOSEM), 25 (1) 3:1-3:31.
[Article] , (2015)

Official URL: http://doi.acm.org/10.1145/2744200

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.

Item Type: Article
Erschienen: 2015
Creators: Proksch, Sebastian and Lerch, Johannes and Mezini, Mira
Title: Intelligent Code Completion with Bayesian Networks
Language: English
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.

Journal or Publication Title: ACM Transactions on Software Engineering and Methodology (TOSEM)
Volume: 25
Number: 1
Place of Publication: New York, NY, USA
Publisher: ACM Press
Uncontrolled Keywords: Content assist, code completion, code recommender, evaluation, integrated development environments, machine learning, productivity; Engineering; E1
Divisions: Department of Computer Science
Department of Computer Science > Software Technology
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1119: CROSSING – Cryptography-Based Security Solutions: Enabling Trust in New and Next Generation Computing Environments
Event Location: New York, NY, USA
Date Deposited: 29 Jan 2016 13:22
Official URL: http://doi.acm.org/10.1145/2744200
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