@inproceedings{fcabc29d4ef04e2f93900c3b60d526a9,
title = "Software clone detection using clustering approach",
abstract = "Code clones are highly similar or identical code segments. Identification of clones helps improve software quality through managed evolution, refactoring, complexity reduction, etc. In this study, we investigate Type 1 and Type 2 function clones using a data mining technique. First, we create a dataset by collecting metrics for all functions in a software system. Second, we apply DBSCAN clustering algorithm on the dataset so that each cluster can be analysed to detect Type 1 and Type 2 function clones. We evaluate our approach by analyzing an open source software Bitmessage. We calculate the precision value to show the effectiveness of our approach in detecting function clones. We show that our approach for functional clone detection is effective with high precision value and number of function clones detected.",
keywords = "Clone detection, Data mining, Function clones, Software metrics",
author = "Bikash Joshi and Puskar Budhathoki and Woon, {Wei Lee} and Davor Svetinovic",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26535-3_59",
language = "British English",
isbn = "9783319265346",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "520--527",
editor = "Lai, {Weng Kin} and Qingshan Liu and Tingwen Huang and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
address = "Germany",
}