An abundant number of clone detection tools have been proposed in the literature due to the many applications and benefits of clone detection. However, there has been difficulty in the performance evaluation and comparison of these clone detectors. This is due to a lack of reliable benchmarks, and the manual efforts required to validate a large number of candidate clones. In particular, there has been a lack of a synthetic benchmark that can precisely and comprehensively measure clone-detection recall. In this paper, we present a mutation-analysis based benchmarking framework that can be used not only to evaluate the recall of clone detection tools for different types of clones but also for specific kinds of clone edits and without any manual efforts. The framework uses an editing taxonomy of clone synthesis for generating thousands of artificial clones, injects into code bases and automatically evaluates the subject clone detection tools following the mutation analysis approach. Additionally, the framework has features where custom clone pairs could also be used in the framework for evaluating the subject tools. This gives the opportunity of evaluating specialized tools for specialized contexts such as evaluating a tool’s capability for the detection of complex Type-4 clones or real world clones without writing complex mutation operators for them. We demonstrate this framework by evaluating the performance of ten modern clone detection tools across two clone granularities (function and block) and three programming languages (Java, C and C#). Furthermore, we provide a variant of the framework that can be used to evaluate specialized tools such as for large gaped clone detection. Our experiments demonstrate confidence in the accuracy of our Mutation and Injection Framework when comparing against the expected results of the corresponding tools, and widely used real-world benchmarks such as Bellon’s benchmark and BigCloneBench. We provide features so that most clone detection tools that report clones in the form of clone pairs (either in filename/line numbers or filename/tokens) could be evaluated using the framework.

Abstract A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, several code clone detection techniques and tools have been proposed and studied over the last decade. However, the clone detection tools are not always perfect and their clone detection reports often contain a number of false positives or irrelevant clones from specific project management or user perspective. To detect all possible similar source code patterns in general, the clone detection tools work on the syntax level while lacking user-specific preferences. This often means the clones must be manually inspected before analysis in order to remove those false positives from consideration. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning approach for automating the validation process. First, a training dataset is built by taking code clones from several clone detection tools for different subject systems and then manually validating those clones. Second, several features are extracted from those clones to train the machine learning model by the proposed approach. The trained algorithm is then used to automatically validate clones without human inspection. Thus the proposed approach can be used to remove the false positive clones from the detection results, automatically evaluate the precision of any clone detectors for any given set of datasets, evaluate existing clone benchmark datasets, or even be used to build new clone benchmarks and datasets with minimum effort. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method also shows better results in several comparative studies with the existing related approaches for clone classification.

A code clone is a pair of similar code fragments, within or between software systems. To detect each possible clone pair from a software system while handling the complex code structures, the clone detection tools undergo a lot of generalization of the original source codes. The generalization often results in returning code fragments that are only coincidentally similar and not considered clones by users, and hence requires manual validation of the reported possible clones by users which is often both time-consuming and challenging. In this paper, we propose a machine learning based tool 'CloneCognition' (Open Source Codes: https://github.com/pseudoPixels/CloneCognition ; Video Demonstration: https://www.youtube.com/watch?v=KYQjmdr8rsw) to automate the laborious manual validation process. The tool runs on top of any code clone detection tools to facilitate the clone validation process. The tool shows promising clone classification performance with an accuracy of up to 87.4%. The tool also exhibits significant improvement in the results when compared with state-of-the-art techniques for code clone validation.

A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning based approach for automating the validation process. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method shows promising results in several comparative studies with the existing related approaches for automatic code clone validation. We also present our experimental results in terms of different code clone detection tools, machine learning algorithms and open source software systems.

Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.

Despite the great number of clone detection approaches proposed in the literature, few have the scalability and speed to analyze large inter-project source datasets, where clone detection has many potential applications. Furthermore, because of the many uses of clone detection, an approach is needed that can adapt to the needs of the user to detect any kind of clone. We propose a clone detection approach designed for user-guided clone detection by exploiting the power of source transformation in a plugin based source processing pipeline. Clones are detected using a simple Jaccard-based clone similarity metric, and users customize the representation of their source code as sets of terms to target particular types or kinds of clones. Fast and scalable clone detection is achieved with indexing, sub-block filtering and input partitioning.