How Dolos works

Conceptually, the algorithm of Dolos can be split up in four steps:

  1. Tokenization
  2. Fingerprinting
  3. Indexing
  4. Reporting


To be immune to plagiarism where variables and functions are renamed, Dolos doesn't run directly on the source code subjected to the test. First a tokenization step is performed using Tree-sitteropen in new window. Tree-sitter can generate syntax trees for many languages and converts source code to a more structured form, free of naming variabilities.

For example, the code

function sum(a, b) {
  return a + b;

is converted to something like this:

program ([0, 0] - [3, 0])
  function ([0, 0] - [2, 1])
    identifier ([0, 9] - [0, 12])
    formal_parameters ([0, 12] - [0, 18])
      identifier ([0, 13] - [0, 14])
      identifier ([0, 16] - [0, 17])
    statement_block ([0, 19] - [2, 1])
      return_statement ([1, 2] - [1, 15])
        binary_expression ([1, 9] - [1, 14])
          identifier ([1, 9] - [1, 10])
          identifier ([1, 13] - [1, 14])

Next, we can start looking for similarities in the submitted files.


To measure similarities between (converted) files, Dolos tries to find common substrings between them. We use substrings of a fixed length called k-grams. To efficiently make these comparisons and reduce the memory usage, all k-grams are hashed using a rolling hash function (the one used by Rabin-Karp in their string matching algorithm). The length k of k-grams can be with the -k option.

To further reduce the memory usage, only a subset of all hashes are stored. The selection of which hashes to store is done by the Winnowing algorithm as described by (Schleimer, Wilkerson and Aiken)open in new window. In short: only the hash with the smallest numerical value is kept for each window. The window length (in k-grams) can be altered with the -w option.

The remaining hashes are the fingerprints of the analyzed files. Internally, these are stored as simple integers.


Because we want to compare all files with each other, it is more efficient to first create an index containing the fingerprints of all files. For each of the fingerprints encountered in any of the files, we store the file and corresponding line number where we encountered that fingerprint.

Once a fingerprint is stored twice in the index, we can record this as a match between the two files because they share at least one k-gram.


Finally, we collect all fingerprints that occur in more than one file and aggregate the results into a report.

This report will contain all file pairs that have at least one common fingerprint, together with some metrics:

  • similarity: which represents the fraction of shared fingerprints between the two files
  • total overlap: which is the absolute value of shared fingerprints, useful for larger projects
  • longest fragment: the length (in fingerprints) of the longest subsequent list of fingerprints matching between the two files, useful for when not the whole source code file is copied