Abstract
Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and constraints imposed by other parts of the codebase and its external dependencies. Existing code completion benchmarks also lack such context. To resolve these restrictions we curate a new dataset of permissively licensed Python packages that includes full projects and their dependencies and provide tools to extract non-local information with the help of program analyzers. We then focus on the task of function call argument completion which requires predicting the arguments to function calls. We show that existing code completion models do not yield good results on our completion task. To better solve this task, we query a program analyzer for information relevant to a given function call, and consider ways to provide the analyzer results to different code completion models during inference and training. Our experiments show that providing access to the function implementation and function usages greatly improves the argument completion performance. Our ablation study provides further insights on how different types of information available from the program analyzer and different ways of incorporating the information affect the model performance.
Original language | English (US) |
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Title of host publication | AAAI-23 Technical Tracks 4 |
Editors | Brian Williams, Yiling Chen, Jennifer Neville |
Publisher | AAAI press |
Pages | 5230-5238 |
Number of pages | 9 |
ISBN (Electronic) | 9781577358800 |
State | Published - Jun 27 2023 |
Externally published | Yes |
Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: Feb 7 2023 → Feb 14 2023 |
Publication series
Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Volume | 37 |
Conference
Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Country/Territory | United States |
City | Washington |
Period | 2/7/23 → 2/14/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.