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Semantic patterns extraction of code smells: Retrieving the solutions of bugs

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationSSSME-2019
Subtitle of host publicationJoint Proceedings of the Inforte Summer School on Software Maintenance and Evolution
Number of pages7
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventJoint of the Summer School on Software Maintenance and Evolution - Tampere, Finland
Duration: 2 Sep 20194 Sep 2019

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


ConferenceJoint of the Summer School on Software Maintenance and Evolution


The understanding of code smells have exerted profound influence in the quality and the performance of programming codes. There are various type of code smells require various solutions. In order to interpret the solutions available in code smells, this research uses NLP (natural language programming) techniques to comprehend contents of messages from Technical Debt Dataset. Based on phrase structure rules, semantic patterns were extracted from the Dataset to build connection between trigger words and dependency tree. Verb Phrases are considered as the actions taken by programmers encountering code smells.

ASJC Scopus subject areas


  • Code smells, NLP, Phrase structure rules, Semantic patterns

Publication forum classification

Field of science, Statistics Finland

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