A code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. Clones generated by a large number of changes to copy-and-pasted code fragments are called large-variance (modifications are scattered) or large-gap (modifications are in one place) clones. It is difficult for general clone detection techniques to detect such clones and thus specialized techniques are necessary. In addition, with the rapid growth of software development, scalable clone detectors that can detect clones in large codebases are required. However, there are no existing techniques for quickly detecting large-variance or large-gap clones in large codebases. In this paper, we propose a scalable clone detection technique that can detect large-variance clones from large codebases and describe its implementation, called NIL. NIL is a token-based clone detector that efficiently identifies clone candidates using an N-gram representation of token sequences and an inverted index. Then, NIL verifies the clone candidates by measuring their similarity based on the longest common subsequence between their token sequences. We evaluate NIL in terms of large- variance clone detection accuracy, general Type-1, Type-2, and Type- 3 clone detection accuracy, and scalability. Our experimental results show that NIL has higher accuracy in terms of large-variance clone detection, equivalent accuracy in terms of general clone detection, and the shortest execution time for inputs of various sizes (1–250 MLOC) compared to existing state-of-the-art tools.

A code clone (clone) is known as one of the factors that makes software maintenance difficult. Thus, in software maintenance, clone modification is essential. An existing study proposed a tool that notifies developers of information about clone changes so that the developers can modify clones efficiently. However, the existing tool is premised on regular execution and not designed to be triggered by external factors except for time. Hence, the existing tool is difficult to be executed triggered by development workflow, such as modifying source code or merging branches, and we think this causes some issues. Consequently, in this study, we propose a new clone modification support technique aimed to integrate into pull request (PR) based development for solving those issues. The proposed technique detects code fragments that need modifications by tracking clones at the time of creating PRs. Moreover, we made three improvements for more accurate clone change tracking. Additionally, we implemented the proposed technique as a software tool, CLIONE. To evaluate CLIONE, we investigated the proportion of PRs in which clones have been modified non-simultaneously, and also we compared the results of clone change tracking with the existing tool. As a result, 11.9%~30.4% of PRs included non-simultaneously modified clones, and we confirmed that CLIONE was able to track clone changes more accurately than the existing tool. CLIONE is available at https://github.com/T45K/CLIONE.

A code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. The presence of clone is known as bad smell, which is phenomena of source code to be refactored. A motivation of refactoring (merging) clones is to reduce the size of source code. An existing study proposed a technique to estimate reduced lines of code by merging clones; however, there are two issues in the existing technique: (1) the existing technique does not consider the refactorability of clones in spite that it is difficult or even impossible to merge some clones due to the limitation of programming languages; (2) in the case that multiple clones are overlapping, the existing technique only considers one of them can be merged. Due to the above issues, estimated reducible LoC is occasionally different from the actual number. Consequently, in this research, we propose a new technique to calculate a reducible LoC. The proposed technique is free from the two issues, and it calculates a reducible LoC fully automatically. The proposed technique performs a loop processing of (a) detecting clones, (b) merging them, (c) compiling the edited source files, and (d) testing them. After finishing the loop, reducible LoC is calculated from the edited source files. This paper also includes comparison results of the proposed technique and the existing one. In the comparisons, we confirmed that a reducible LoC which was calculated with considering refactorability is 25% of a reducible LoC which was estimated without considering refactorability. We also confirmed that the proposed technique was able to merge clones that were not counted in the existing technique.