← 研踪 PaperTrailarXiv:2606.25272 · v2
Characterizing Robustness in Semantic Code Clone Detection
首次提交 2026-06-24 09:11
版本更新 2026-11-05 16:02
雷达观测 2026-11-06 12:09
摘要
Semantic code clone detection seeks to recognize shared functionality across different implementations, making robustness to behavior-preserving changes central to its effectiveness. Prior studies have exposed failures under such changes, motivating a systematic examination of how sensitivity varies with the rewrites used for evaluation. We present Systematic Code Transformation (SCT), a framework organizing 51 operators by code level, target, and edit category, and evaluate 14 token, tree, graph, and prompt-based detectors on BigCloneBench, Google Code Jam, and OJClone. By comparing transformation groups, repeated application, and alternative constructions, we connect overall performance changes with specific rewriting conditions. Of 25 token, tree, and graph detector and dataset combinations starting above 90% F1, 21 lose at least 10 percentage points under the composition of all operators. These losses vary with transformation type and application intensity, but are not determined by edit counts or code growth alone. Even at matched locations and edit counts, alternative constructions can expose different errors despite similar aggregate losses. Further analysis distinguishes reduced score separability from changed fixed-threshold decisions with retained ranking ability, revealing different scope for threshold recovery. Overall, these findings show why benchmark effectiveness needs to extend to stable discrimination across equivalent implementations and how systematic transformation testing can assess that capability.
作者备注：31 pages, 10 figures, 9 tables
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v2 · 2026-11-05 16:02 · 最新观测版本
Characterizing Robustness in Semantic Code Clone Detection
Semantic code clone detection seeks to recognize shared functionality across different implementations, making robustness to behavior-preserving changes central to its effectiveness. Prior studies have exposed failures under such changes, motivating a systematic examination of how sensitivity varies with the rewrites used for evaluation. We present Systematic Code Transformation (SCT), a framework organizing 51 operators by code level, target, and edit category, and evaluate 14 token, tree, graph, and prompt-based detectors on BigCloneBench, Google Code Jam, and OJClone. By comparing transformation groups, repeated application, and alternative constructions, we connect overall performance changes with specific rewriting conditions. Of 25 token, tree, and graph detector and dataset combinations starting above 90% F1, 21 lose at least 10 percentage points under the composition of all operators. These losses vary with transformation type and application intensity, but are not determined by edit counts or code growth alone. Even at matched locations and edit counts, alternative constructions can expose different errors despite similar aggregate losses. Further analysis distinguishes reduced score separability from changed fixed-threshold decisions with retained ranking ability, revealing different scope for threshold recovery. Overall, these findings show why benchmark effectiveness needs to extend to stable discrimination across equivalent implementations and how systematic transformation testing can assess that capability.
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