Are there functionally similar code clones in practice?

Käfer, Verena; Wagner, Stefan; Koschke, Rainer

doi:10.1109/iwsc.2018.8327312

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…To find candidates, we explored code clone benchmarks [18], [19], [20], [21], [22], code search engines, open-source platforms like GitHub, and practice websites like Codewars 1 and LeetCode. 2 We manually modified some snippets to control our variables but wrote none from scratch.…”

Section: B Artifact Collectionmentioning

confidence: 99%

“…These resources altogether contain a massive quantity of diverse applications, but despite this diversity, a significant amount of code contains similar features [1]. This repetition occurs for a variety of reasons: for example, similar sub-problems reoccur in different contexts [2], development communities share linguistic conventions [3], and developers directly reuse and modify each other's code [4]. This repetition from a diversity of sources means not only that a given algorithm may exist somewhere already and be waiting for discovery, but also that there may exist multiple similar but divergent alternatives that satisfy the problem.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Similar Code through Comparative Comprehension

Middleton

Stolee

2022

2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Any problem in code may have multiple solutions that differ in details large and small. Because modern software development is characterized by an abundance of searchable and reusable code, effective developers must be able to judge not only the meaning of new algorithms but also the differences between alternatives. Therefore, we use a multi-method study to explore how developers perform comparative comprehensionthe cognitive activity of understanding how algorithms behave relative to each other.To explore how developers compare code, we performed a controlled experiment with 16 developers in a mixed think-aloud and interview format and another 95 developers in a survey format. In this experiment, participants investigated whether a pair of code snippets would demonstrate equivalent behavior when run, controlling for differences in behavior, programming languages, algorithmic structures, and meaningful names. Overall, our results describe how comparison fits into learning, reviewing, and reusing code. Our task observations shed light on how developers move between code similarities at different levels-textual, structural, and schematic-when simultaneously inspecting multiple snippets. In our experiment, developers made more accurate conclusions about behavior given similar languages and structures, with names acting as additional evidence in interaction with other cues, but they also overestimated whether behavior is equivalent in many cases. From this, we identify challenges developers face in comprehending alternatives and we highlight opportunities to better support developers in comparison activities.

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Section: B Artifact Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Similar Code through Comparative Comprehension

Middleton

Stolee

2022

2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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“…Code similarity has been extensively studied in literature [12], particularly type-4 (semantically similar) code clones. Through a user study, researchers showed that functionally similar code exists in practice [13]. While static token based approaches such as SourcererCC [14] and CCFinder [15] have been studied, advances in computing has paved the path for two other approaches to code clone detection.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Compiler Optimization for Code Clone Detection

Singh¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Finding similar code in software systems can guide several software engineering tasks such as code maintenance, program understanding, and code reuse. Similar code detection has been actively studied in the past. In the paper, we propose a novel approach that leverages compiler optimizations to transform semantically similar code and detect similar programs. The key observation of our work is that the compiler optimizations can be used to smooth out source code level idiosyncrasies introduced by the developers, thus making the optimized programs, for the same task, similar in structure. The similarity in structure can then be used to classify the programs. We conducted experiments on the Google CodeJam dataset to demonstrate the effectiveness of our approach. The experimental results show that our technique can achieve up to 85% accuracy on the program classification task, which is an improvement of more than 25% over the source code level classification.

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“…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]. This work provides a significant contribution to semantic clone detection in the form of novel concepts and a prototype implementing them.…”

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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Are there functionally similar code clones in practice?

Cited by 14 publications

References 13 publications

Understanding Similar Code through Comparative Comprehension

Understanding Similar Code through Comparative Comprehension

Leveraging Compiler Optimization for Code Clone Detection

Towards Semantic Clone Detection via Probabilistic Software Modeling

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