|
--- |
|
license: mit |
|
--- |
|
|
|
TeCo is a deep learning model using code semantics to automatically complete the next statement in a test method. Completing tests requires reasoning about the execution of the code under test, which is hard to do with only syntax-level data that existing code completion models use. To solve this problem, we leverage the fact that tests are readily executable. TeCo extracts and uses execution-guided code semantics as inputs for the ML model, and performs reranking via test execution to improve the outputs. On a large dataset with 131K tests from 1270 open-source Java projects, TeCo outperforms the state-of-the-art by 29% in terms of test completion accuracy. |
|
|
|
TeCo is presented in the following ICSE 2023 paper: |
|
|
|
Title: [Learning Deep Semantics for Test Completion](https://arxiv.org/pdf/2302.10166.pdf) |
|
|
|
Authors: Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond Mooney, Milos Gligoric |
|
|
|
--- |
|
|
|
**TeCo's code is hosted on GitHub: https://github.com/EngineeringSoftware/teco** |
|
|
|
**This repo hosts the model we trained, but it should be used together with our codebase; please read the README there, which describes how to [download](https://github.com/EngineeringSoftware/teco/blob/main/README.md#downloading-model) and use this model.** |
