DOI

Software documentation is a significant component of modern software systems. Each year it becomes more and more complicated, just as the software itself. One of the aspects that negatively impact documentation quality is the presence of textual duplicates. Textual duplicates encountered in software documentation are inherently imprecise, i.e. in a single document the same information may be presented many times with different levels of detail and in various contexts. Documentation maintenance is an acute problem, and there is a strong demand for automation tools in this domain. In this study we present the Duplicate Finder Toolkit, a tool which assists an expert with duplicate maintenance-related tasks. Our tool can facilitate the maintenance process in a number of ways: 1) detection of both exact and near duplicates 2) duplicate visualization via heat maps 3) duplicate analysis-comparison of several duplicate instances, evaluation of their differences, exploration of duplicate context 4) duplicate manipulation and extraction.

Original languageEnglish
Title of host publicationPROCEEDINGS 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING - COMPANION (ICSE-COMPANION
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages171-172
Number of pages2
ISBN (Electronic)9781450356633
ISBN (Print)9781450356633
DOIs
StatePublished - 27 May 2018
Event40th ACM/IEEE International Conference on Software Engineering, ICSE 2018 - Gothenburg, Sweden
Duration: 27 May 20183 Jun 2018

Publication series

NameProceedings of the IEEE-ACM International Conference on Software Engineering Companion
PublisherIEEE
ISSN (Print)2574-1926

Conference

Conference40th ACM/IEEE International Conference on Software Engineering, ICSE 2018
Country/TerritorySweden
CityGothenburg
Period27/05/183/06/18

    Scopus subject areas

  • Software

    Research areas

  • copy-paste, Meaningful search, Near duplicates, Software clone detection, Software documents, software clone detection, software documents, near duplicates, meaningful search, acm reference format

ID: 35272569