Addressing Leakage in Self-Supervised Contextualized Code Retrieval

• URL: http://arxiv.org/abs/2204.11594v1
• Date: Sun, 17 Apr 2022 12:58:38 GMT
• Title: Addressing Leakage in Self-Supervised Contextualized Code Retrieval
• Authors: Johannes Villmow, Viola Campos, Adrian Ulges, Ulrich Schwanecke
• Abstract summary: We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and targets. To combat leakage between the two, we suggest a novel approach based on mutual identifier masking, dedentation, and the selection of syntax-aligned targets.
• Score: 3.693362838682697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and targets. To combat leakage between the two, we suggest a novel approach based on mutual identifier masking, dedentation, and the selection of syntax-aligned targets. Our second contribution is a new dataset for direct evaluation of contextualized code retrieval, based on a dataset of manually aligned subpassages of code clones. Our experiments demonstrate that our approach improves retrieval substantially, and yields new state-of-the-art results for code clone and defect detection.

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