Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection

• URL: http://arxiv.org/abs/2309.02182v1
• Date: Tue, 5 Sep 2023 12:38:55 GMT
• Title: Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection
• Authors: Muslim Chochlov (1), Gul Aftab Ahmed (2), James Vincent Patten (1), Guoxian Lu (3), Wei Hou (4), David Gregg (2), Jim Buckley (1) ((1) Deptment of Computer Science and Information Systems, University of Limerick, Ireland, (2) Deptment of Computer Science, Trinity College Dublin, Ireland, (3) WN Digital IPD and Trustworthiness Enabling, Huawei Technologies Co., Ltd., Shanghai, China, (4) Huawei Vulnerability Management Center, Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China)
• Abstract summary: SSCD is a BERT-based clone detection approach that targets high recall of Type 3 and Type 4 clones at scale. It does so by computing a representative embedding for each code fragment and finding similar fragments using a nearest neighbour search. This paper details the approach and an empirical assessment towards configuring and evaluating that approach in industrial setting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code clones can detrimentally impact software maintenance and manually detecting them in very large codebases is impractical. Additionally, automated approaches find detection of Type 3 and Type 4 (inexact) clones very challenging. While the most recent artificial deep neural networks (for example BERT-based artificial neural networks) seem to be highly effective in detecting such clones, their pairwise comparison of every code pair in the target system(s) is inefficient and scales poorly on large codebases. We therefore introduce SSCD, a BERT-based clone detection approach that targets high recall of Type 3 and Type 4 clones at scale (in line with our industrial partner's requirements). It does so by computing a representative embedding for each code fragment and finding similar fragments using a nearest neighbour search. SSCD thus avoids the pairwise-comparison bottleneck of other Neural Network approaches while also using parallel, GPU-accelerated search to tackle scalability. This paper details the approach and an empirical assessment towards configuring and evaluating that approach in industrial setting. The configuration analysis suggests that shorter input lengths and text-only based neural network models demonstrate better efficiency in SSCD, while only slightly decreasing effectiveness. The evaluation results suggest that SSCD is more effective than state-of-the-art approaches like SAGA and SourcererCC. It is also highly efficient: in its optimal setting, SSCD effectively locates clones in the entire 320 million LOC BigCloneBench (a standard clone detection benchmark) in just under three hours.

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