Keqi Li
ABSTRACT
API recommendation has been widely used to enhance developers’ efficiency in software development. However, existing API recommendation methods for dynamic languages such as Python usually suffer from the limitations of incorrect type inference and lack of rich contextual semantics. To address these issues, we propose in this paper a novel approach, PyBartRec, to recommend APIs for Python programs concerning their rich semantics. Instead of analyzing the data flow information only, our approach utilizes a Transformer-based pre-trained model to extract the semantic features of Python code snippets. Such contextual information allows our approach to recommend correct APIs even when the APIs are not included in the local data flow information. We also use such information to perform a post-processing type inference. By narrowing the range of candidate types, our approach can recommend APIs accurately even in the failure scenarios of type inference. We evaluated PyBartRec in eight popular Python projects and the experimental results show that our approach significantly outperforms the state-of-the-art solutions. In particular, the average top-1 accuracy and average top-10 accuracy of PyBartRec within the same project are over 40%, and 60%, respectively. In cross-project recommendation, the MRR of PyBartRec is also over 40%.
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Index Terms
- PyBartRec: Python API Recommendation with Semantic Information
Index terms have been assigned to the content through auto-classification.
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