Semantic Clone Detection via Probabilistic Software Modeling

TL;DR

This paper introduces SCD-PSM, a probabilistic modeling approach that accurately detects semantic clones with 0% syntactic similarity by evaluating behavioral equivalence through likelihood ratios.

Contribution

The paper presents a novel probabilistic software modeling method for semantic clone detection that outperforms existing techniques, especially in identifying behaviorally equivalent but syntactically different code.

Findings

Achieved Matthews Correlation Coefficient > 0.9 in clone detection.

Effectively detects semantic clones with 0% syntactic similarity.

Works on complex and classical semantic clone detection problems.

Abstract

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.