On Precision of Code Clone Detection Tools

Farmahinifarahani, Farima; Saini, Vaibhav; Yang, Di; Sajnani, Hitesh; Lopes, Cristina Videira

doi:10.1109/saner.2019.8668015

Cited by 13 publications

(8 citation statements)

References 30 publications

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“…B5-8: NiCad is a tool that has achieved both high precision and recall for various types of code clones [30]. The tool compares a pair of code blocks or functions, and reports them as a clone pair if they are similar.…”

Section: Baselinesmentioning

confidence: 99%

NCDSearch: Sliding Window-Based Code Clone Search Using Lempel-Ziv Jaccard Distance

Ishio

Maeda²,

Shibuya³

et al. 2022

IEICE Trans. Inf. & Syst.

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Software developers may write a number of similar source code fragments including the same mistake in software products. To remove such faulty code fragments, developers inspect code clones if they found a bug in their code. While various code clone detection methods have been proposed to identify clones of either code blocks or functions, those tools do not always fit the code inspection task because a faulty code fragment may be much smaller than code blocks, e.g. a single line of code. To enable developers to search code clones of such a small faulty code fragment in a large-scale software product, we propose a method using Lempel-Ziv Jaccard Distance, which is an approximation of Normalized Compression Distance. We conducted an experiment using an existing research dataset and a user survey in a company. The result shows our method efficiently reports cloned faulty code fragments and the performance is acceptable for software developers.

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Section: Baselinesmentioning

confidence: 99%

NCDSearch: Sliding Window-Based Code Clone Search Using Lempel-Ziv Jaccard Distance

Ishio

Maeda²,

Shibuya³

et al. 2022

IEICE Trans. Inf. & Syst.

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“…They extrapolate the average disagreement to estimate that at least 15% of the clones across the benchmark are subjective or have validation errors. Manual validation of clone pairs is also subjective [8].…”

Section: Ground Truth Qualitymentioning

confidence: 99%

“…Only a small set of published work has been investigated. We used IEEE Xplore to search 8 for papers that mention "BigCloneBench". This resulted in 75 papers.…”

Section: Short Literature Studymentioning

confidence: 99%

BigCloneBench Considered Harmful for Machine Learning

Krinke

Ragkhitwetsagul

2022

2022 IEEE 16th International Workshop on Software Clones (IWSC)

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BigCloneBench is a well-known large-scale dataset of clones mainly targeted at the evaluation of recall of clone detection tools. It has been beneficial for research on clone detection and evaluating the performance of clone detection tools, for which it has become standard. It has also been used in machine learning approaches to clone detection or code similarity detection. However, the way BigCloneBench has been constructed makes it problematic to use as ground truth for learning code similarity. This paper highlights the features of BigCloneBench that affect the ground truth quality and discusses common misperceptions about the benchmark. For example, extending or replacing the ground truth without understanding the properties of BigCloneBench often leads to wrong assumptions which can lead to invalid results. Also, a manual investigation of a sample of Weak-Type-3/Type-4 clone pairs revealed 86% of pairs to be false positives, threatening the results of machine learning approaches using BigCloneBench. We call for a halt in using BigCloneBench as the ground truth for learning code similarity.

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“…Existing tools have limited or no capabilities to detect Type 4 clones [15]. Most current studies exclude Type 4 clones because of the lack of tool support [16], [12], [13], [17], [18]. Nevertheless, Type 4 clones exist, and recent research efforts try to deepen the understanding of them [15], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Towards Semantic Clone Detection via Probabilistic Software Modeling

Thaller

Linsbauer

Egyed

2020

2020 IEEE 14th International Workshop on Software Clones (IWSC)

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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. This boundary was tested over the last years with interesting new approaches. This work contributes a semantic clone detection approach that detects clones with 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 to 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 whether they are clones or not. SCD-PSM yields excellent results with a Matthews Correlation Coefficient greater 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.

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On Precision of Code Clone Detection Tools

Cited by 13 publications

References 30 publications

NCDSearch: Sliding Window-Based Code Clone Search Using Lempel-Ziv Jaccard Distance

NCDSearch: Sliding Window-Based Code Clone Search Using Lempel-Ziv Jaccard Distance

BigCloneBench Considered Harmful for Machine Learning

Towards Semantic Clone Detection via Probabilistic Software Modeling

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