Gesellschaft für Informatik e.V.

Lecture Notes in Informatics


Software Engineering 2016 P-252, 25-26 (2016).

Gesellschaft für Informatik, Bonn
2016


Copyright © Gesellschaft für Informatik, Bonn

Contents

Intelligent code completion with Bayesian networks

Sebastian Proksch , Johannes Lerch and Mira Mezini

Abstract


Code completion is an integral part of modern Integrated Development Environments (IDEs). Intelligent code completion systems can reduce long lists of type-correct proposals to relevant items. In this work, we replace an existing code completion engine named Best-Matching Neighbor (BMN) by an approach using Bayesian Networks named Pattern-based Bayesian Network (PBN).We use additional context information for more precise recommendations and apply clustering techniques to improve model sizes and to increase speed. We compare the new approach with the existing algorithm 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, and that PBN can obtain comparable prediction quality to BMN, while model size and inference speed scale better with large input sizes.


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Gesellschaft für Informatik, Bonn
ISBN 978-3-88579-646-6


Last changed 25.02.2016 18:38:59