In academic courses, students frequently take advantage of someone else's work to improve their own evaluations or grades. This unethical behavior seriously threatens the integrity of the academic system, and teachers invest substantial effort in preventing and recognizing plagiarism. When students take examinations requiring the production of computer programs, plagiarism detection can be semiautomated using analysis techniques such as JPlag and Moss. These techniques are useful but lose effectiveness when the text of the exam suggests some of the elements that should be structurally part of the solution. A loss of effectiveness is caused by the many common parts that are shared between programs due to the suggestions in the text of the exam rather than plagiarism. In this article, we present the AuDeNTES anti-plagiarism technique. AuDeNTES detects plagiarism via the code fragments that better represent the individual students' contributions by filtering from students' submissions the parts that might be common to many students due to the suggestions in the text of the exam. The filtered parts are identified by comparing students' submissions against a reference solution, which is a solution of the exam developed by the teachers. Specifically, AuDeNTES first produces tokenized versions of both the reference solution and the programs that must be analyzed. Then, AuDeNTES removes from the tokenized programs the tokens that are included in the tokenized reference solution. Finally, AuDeNTES computes the similarity among the filtered tokenized programs and produces a ranked list of program pairs suspected of plagiarism. An empirical comparison against multiple state-of-the-art plagiarism detection techniques using several sets of real students' programs collected in early programming courses demonstrated that AuDeNTES identifies more plagiarism cases than the other techniques at the cost of a small additional inspection effort. (Contains 12 figures, 5 tables, and 6 footnotes.)