Indexing source code and clone detection

2021 ◽  
pp. 106805
Author(s):  
Zdenek Tronicek
Keyword(s):  
Source Code ◽  
Clone Detection

Finding Software License Violations Through Binary Code Clone Detection - A Retrospective

2021 ◽  
Vol 46 (3) ◽  
pp. 24-25
Author(s):  
Armijn Hemel ◽  
Karl Trygve Kalleberg ◽  
Rob Vermaas ◽  
Eelco Dolstra
Keyword(s):  
Open Source ◽  
Original Problem ◽  
Binary Code ◽  
Source Code ◽  
Clone Detection ◽  
Problem Statement ◽  
Open Source Code ◽  
Code Clone ◽  
Software License ◽  
The Impact

Ten years ago, we published the article Finding software license violations through binary code clone detection at the MSR 2011 conference. Our paper was motivated by the tendency of em- bedded hardware vendors to only release binary blobs of their rmware, often violating the licensing terms of open-source soft- ware present inside those blobs. The techniques presented in our paper were designed to accurately identify open-source code hid- den inside binary blobs. Here, we give our perspectives on the impact of our work, both industrially and academically, and re- visit the original problem statement to see what has happened in the eld of open-source compliance in the intervening decade.

scholarly journals How are functionally similar code clones different?

2015 ◽  
Author(s):  
Stefan Wagner ◽  
Asim Abdulkhaleq ◽  
Ivan Bogicevic ◽  
Jan-Peter Ostberg ◽  
Jasmin Ramadani
Keyword(s):  
Data Structure ◽  
Random Sample ◽  
Source Code ◽  
Clone Detection ◽  
Code Clones ◽  
Syntactic Similarity ◽  
Similar Code

Background. Today, redundancy in source code, so-called “clones”, caused by copy&paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, caused not by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in < 16 % of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

scholarly journals How are functionally similar code clones syntactically different? An empirical study and a benchmark

2016 ◽  
Vol 2 ◽  
pp. e49 ◽  
Author(s):  
Stefan Wagner ◽  
Asim Abdulkhaleq ◽  
Ivan Bogicevic ◽  
Jan-Peter Ostberg ◽  
Jasmin Ramadani
Keyword(s):  
Data Structure ◽  
Empirical Study ◽  
Random Sample ◽  
Source Code ◽  
Clone Detection ◽  
Code Clones ◽  
Syntactic Similarity ◽  
Similar Code

Background. Today, redundancy in source code, so-called “clones” caused by copy&paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how onlyfunctionally similar clones(FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research.Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs.Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories.Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

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