Finding similarities in source code through factorization The high availability of a huge number of documents on the Web makes plagiarism very attractive and easy. This plagiarism concerns any kind of document, natural language texts as well as more structured information such as programs. In order to cope with this problem, many tools and algorithms have been proposed to xefxacx81nd similarities. In this paper we present a new algorithm designed to detect similarities in source codes. Contrary to existing methods, this algorithm relies on the notion of function and focuses on obfuscation with inlining and outlining of functions. This method is also exefxacx83cient against insertions, deletions and permutations of instruction blocks. It is based on code factorization and uses adapted pattern matching algorithms and structures such as suffix arrays.

Aroma: Code Recommendation via Structural Code Search Programmers often write code that has similarity to existing code written somewhere. A tool that could help programmers to search such similar code would be immensely useful. Such a tool could help programmers to extend partially written code snippets to completely implement necessary functionality, help to discover extensions to the partial code which are commonly included by other programmers, help to crosscheck against similar code written by other programmers, or help to add extra code which would fix common mistakes and errors. We propose Aroma, a tool and technique for code recommendation via structural code search. Aroma indexes a huge code corpus including thousands of opensource projects, takes a partial code snippet as input, searches the corpus for method bodies containing the partial code snippet, and clusters and intersects the results of the search to recommend a small set of succinct code snippets which both contain the query snippet and appear as part of several methods in the corpus. We evaluated Aroma on 2000 randomly selected queries created from the corpus, as well as 64 queries derived from code snippets obtained from Stack Overflow, a popular website for discussing code. We implemented Aroma for 4 different languages, and developed an IDE plugin for Aroma. Furthermore, we conducted a study where we asked 12 programmers to complete programming tasks using Aroma, and collected their feedback. Our results indicate that Aroma is capable of retrieving and recommending relevant code snippets efficiently.