Detecting Near Duplicates in Software Documentation

TL;DR

This paper introduces a novel algorithm for detecting near duplicates in software documentation by adapting clone detection techniques, demonstrating its effectiveness across various documentation types and highlighting its potential to improve documentation quality.

Contribution

It presents a new algorithm that adapts clone detection methods to identify near duplicates in software documents, with comprehensive evaluation on multiple projects.

Findings

Significant presence of duplicates in software documentation.

Algorithm effectively detects both exact and near duplicates.

Manual analysis confirms the algorithm's practical benefits.

Abstract

Contemporary software documentation is as complicated as the software itself. During its lifecycle, the documentation accumulates a lot of near duplicate fragments, i.e. chunks of text that were copied from a single source and were later modified in different ways. Such near duplicates decrease documentation quality and thus hamper its further utilization. At the same time, they are hard to detect manually due to their fuzzy nature. In this paper we give a formal definition of near duplicates and present an algorithm for their detection in software documents. This algorithm is based on the exact software clone detection approach: the software clone detection tool Clone Miner was adapted to detect exact duplicates in documents. Then, our algorithm uses these exact duplicates to construct near ones. We evaluate the proposed algorithm using the documentation of 19 open source and commercial projects. Our evaluation is very comprehensive - it covers various documentation types: design and requirement specifications, programming guides and API documentation, user manuals. Overall, the evaluation shows that all kinds of software documentation contain a significant number of both exact and near duplicates. Next, we report on the performed manual analysis of the detected near duplicates for the Linux Kernel Documentation. We present both quantative and qualitative results of this analysis, demonstrate algorithm strengths and weaknesses, and discuss the benefits of duplicate management in software documents.