Combining Program Analysis and Statistical Language Model for Code Statement Completion
This work addresses improving developer productivity in programming tasks through more accurate code completion, representing an incremental advance by hybridizing existing techniques.
The paper tackles the problem of automatic code statement completion by introducing AutoSC, which combines program analysis and statistical language models to generate frequent and valid code statements, achieving 38.9-41.3% top-1 accuracy and outperforming a state-of-the-art approach by 9X-69X in top-1 accuracy.
Automatic code completion helps improve developers' productivity in their programming tasks. A program contains instructions expressed via code statements, which are considered as the basic units of program execution. In this paper, we introduce AutoSC, which combines program analysis and the principle of software naturalness to fill in a partially completed statement. AutoSC benefits from the strengths of both directions, in which the completed code statement is both frequent and valid. AutoSC is first trained on a large code corpus to derive the templates of candidate statements. Then, it uses program analysis to validate and concretize the templates into syntactically and type-valid candidate statements. Finally, these candidates are ranked by using a language model trained on the lexical form of the source code in the code corpus. Our empirical evaluation on the large datasets of real-world projects shows that AutoSC achieves 38.9-41.3% top-1 accuracy and 48.2-50.1% top-5 accuracy in statement completion. It also outperforms a state-of-the-art approach from 9X-69X in top-1 accuracy.