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arXiv 2008.04891cs.SEcs.AI
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Semantic Clone Detection via Probabilistic Software Modeling

Johannes Kepler University Linz(林茨约翰·开普勒大学);Technical University of Braunschweig(布伦瑞克工业大学)

Hannes Thaller, Lukas Linsbauer, Alexander Egyed

英文摘要:

Semantic clone detection is the process of finding program elements with similar or equal runtime behavior. For example, detecting the semantic equality between the recursive and iterative implementation of the factorial computation. Semantic clone detection is the de facto technical boundary of clone detectors. In recent years, this boundary has been tested using interesting new approaches. This article contributes a semantic clone detection approach that detects clones that have 0% syntactic similarity. We present Semantic Clone Detection via Probabilistic Software Modeling (SCD-PSM) as a stable and precise solution to semantic clone detection. PSM builds a probabilistic model of a program that is capable of evaluating and generating runtime data. SCD-PSM leverages this model and its model elements for finding behaviorally equal model elements. This behavioral equality is then generalized to semantic equality of the original program elements. It uses the likelihood between model elements as a distance metric. Then, it employs the likelihood ratio significance test to decide whether this distance is significant, given a pre-specified and controllable false-positive rate. The output of SCD-PSM are pairs of program elements (i.e., methods), their distance, and a decision on whether they are clones or not. SCD-PSM yields excellent results with a Matthews Correlation Coefficient greater than 0.9. These results are obtained on classical semantic clone detection problems such as detecting recursive and iterative versions of an algorithm, but also on complex problems used in coding competitions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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