SourcererCC: Scalable and Accurate Clone Detection

Sajnani, Hitesh; Saini, Vaibhav; Roy, Chanchal K.; Lopes, Cristina Videira

doi:10.1007/978-981-16-1927-4_4

Cited by 24 publications

(61 citation statements)

References 21 publications

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“…Moreover, since the amount of code that has to be processed is in a scale of millions line of code (as shown in Table 1), a clone detector must be scalable enough to report clones in a reasonable amount of time. We have tried 7 state-of-the-art clone detectors including Simian [66], SourcererCC [64], NiCad [13], [62], CCFinder [29], iClones [25], DECKARD [28], and PMD-CPD [51] against the Stack Overflow and Qualitas datasets. NiCad failed to parse 44,960 Stack Overflow snippets while PMD CPD failed to complete the execution due to lexical errors.…”

Section: Phase 1: Clone Identificationmentioning

confidence: 99%

Toxic Code Snippets on Stack Overflow

Ragkhitwetsagul

Krinke

Paixao

et al. 2021

IIEEE Trans. Software Eng.

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Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow's CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects.We performed an empirical study of online code clones between Stack Overflow and 111 Java open source projects 8. https://ucl-crest.github.io/cloverflow-web This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

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Section: Phase 1: Clone Identificationmentioning

confidence: 99%

Toxic Code Snippets on Stack Overflow

Ragkhitwetsagul

Krinke

Paixao

et al. 2021

IIEEE Trans. Software Eng.

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“…Manual clone detection is a tedious and error-prone task, and known to be infeasible for larger systems [62]. Tool-based clone detection requires manual verification and postprocessing, since even the most advanced clone detection tools have imperfect precision and recall [63].…”

Section: Rq4mentioning

confidence: 99%

Seamless Variability Management with the Virtual Platform

Mahmood

Strüber

Berger

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone&own and software configuration with an integrated platform. Organizations often start with the former, which is cheap, agile, and supports quick innovation, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variantrich systems, exploiting a spectrum between both opposing strategies. We design, formalize, and prototype the variabilitymanagement framework virtual platform. It bridges clone&own and platform-oriented development. Relying on programminglanguage-independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. Our evaluation simulates the evolution of a real-world, clone-based system, measuring its costs and benefits.

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“…BigCloneBench has been primarily developed to measure and compare the recall of clone detection tools [70,[78][79][80]. However, it can also be used for other clone and so ware studies [80].…”

Section: Bigclonebench For Code Clonesmentioning

confidence: 99%

DeepClone: Modeling Clones to Generate Code Predictions

Hammad¹,

Babur²,

Basit³

et al. 2020

Preprint

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During so ware development, programmers o en tend to reuse the code for common functionalities, available in other source code repositories. is activity helps them to reduce time and e ort to develop code, instead of building it from scratch. Code clones are candidates for reuse in an exploratory or rapid development, as they represent o en repeated functionality in so ware systems. To facilitate code clone reuse, we propose a novel approach, Deep-Clone, where we utilize a deep learning algorithm for modeling code clones and predicting the next possible set of tokens (up to the cloned method body) based on the user input so far. e generated predictions aim to potentially help developers to write code rapidly with minimum tuning of values later on. DeepClone applies natural language processing techniques to learn from a large code corpus (the BigCloneBench dataset), and generates code tokens (full clone methods where applicable) using the model learned. We have quantitatively evaluated our solution to assess (1) our model's quality and its accuracy in token prediction, and (2) its performance and e ectiveness in clone method prediction. With a high quality and accurate model as the foundation, we further discuss scenarios for exploiting our approach. CCS CONCEPTS•Computing methodologies → Neural networks; •So ware and its engineering → Language features; Reusability; Automatic programming; So ware development methods;

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SourcererCC: Scalable and Accurate Clone Detection

Cited by 24 publications

References 21 publications

Toxic Code Snippets on Stack Overflow

Toxic Code Snippets on Stack Overflow

Seamless Variability Management with the Virtual Platform

DeepClone: Modeling Clones to Generate Code Predictions

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