Assessing and Improving an Evaluation Dataset for Detecting Semantic Code Clones via Deep Learning

Abstract

In recent years, applying deep learning to detect semantic code clones has received substantial attention from the research community. Accordingly, various evaluation benchmark datasets, with the most popular one as BigCloneBench, are constructed and selected as benchmarks to assess and compare different deep learning models for detecting semantic clones. However, there is no study to investigate whether an evaluation benchmark dataset such as BigCloneBench is properly used to evaluate models for detecting semantic code clones. In this article, we present an experimental study to show that BigCloneBench typically includes semantic clone pairs that use the same identifier names, which however are not used in non-semantic-clone pairs. Subsequently, we propose an undesirable-by-design Linear-Model that considers only which identifiers appear in a code fragment; this model can achieve high effectiveness for detecting semantic clones when evaluated on BigCloneBench, even comparable to state-of-the-art deep learning models recently proposed for detecting semantic clones. To alleviate these issues, we abstract a subset of the identifier names (including type, variable, and method names) in BigCloneBench to result in AbsBigCloneBench and use AbsBigCloneBench to better assess the effectiveness of deep learning models on the task of detecting semantic clones.