Bruch, Marcel ; Monperrus, Martin ; Mezini, Mira (2009)
Learning from Examples to Improve Code Completion Systems.
17th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-17). Amsterdam , Netherlands (24.-28. Aug 2009)
Conference or Workshop Item, Bibliographie
Abstract
The suggestions made by current IDE’s code completion features are based exclusively on static properties of the programming language. As a result, often proposals are made which are irrelevant for a particular working context. Also, these suggestions are ordered alphabetically rather than by their relevance in a particular context. In this paper, we present intelligent code completion systems that learn from existing code repositories. We have implemented three such systems, each using the information contained in repositories in a different way. We perform a large-scale quantitative evaluation of these systems, integrate the best performing one into Eclipse, and evaluate the latter also by a user study. Our experiments give evidence that intelligent code completion systems which learn from examples significantly outperform mainstream code completion systems in terms of the relevance of their suggestions and thus have the potential to enhance developers’ productivity.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2009 |
| Creators: | Bruch, Marcel ; Monperrus, Martin ; Mezini, Mira |
| Type of entry: | Bibliographie |
| Title: | Learning from Examples to Improve Code Completion Systems |
| Language: | English |
| Date: | 24 August 2009 |
| Book Title: | Proceedings of 17th ACM SIGSOFT Symposium on the Foundations of Software Engineering |
| Event Title: | 17th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-17) |
| Event Location: | Amsterdam , Netherlands |
| Event Dates: | 24.-28. Aug 2009 |
| Abstract: | The suggestions made by current IDE’s code completion features are based exclusively on static properties of the programming language. As a result, often proposals are made which are irrelevant for a particular working context. Also, these suggestions are ordered alphabetically rather than by their relevance in a particular context. In this paper, we present intelligent code completion systems that learn from existing code repositories. We have implemented three such systems, each using the information contained in repositories in a different way. We perform a large-scale quantitative evaluation of these systems, integrate the best performing one into Eclipse, and evaluate the latter also by a user study. Our experiments give evidence that intelligent code completion systems which learn from examples significantly outperform mainstream code completion systems in terms of the relevance of their suggestions and thus have the potential to enhance developers’ productivity. |
| Divisions: | 20 Department of Computer Science > Software Technology 20 Department of Computer Science |
| Date Deposited: | 10 Jul 2009 12:33 |
| Last Modified: | 05 Mar 2013 09:20 |
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