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

Boyang Zhang

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

Boyang Zhang

Information and Knowledge Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

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.

Original language	English
Title of host publication	SSSME-2019
Subtitle of host publication	Joint Proceedings of the Inforte Summer School on Software Maintenance and Evolution
Publisher	CEUR-WS
Pages	71-77
Number of pages	7
Publication status	Published - 2019
Publication type	A4 Article in conference proceedings
Event	Joint of the Summer School on Software Maintenance and Evolution - Tampere, Finland Duration: 2 Sept 2019 → 4 Sept 2019

Publication series

Name	CEUR Workshop Proceedings
Volume	2520
ISSN (Print)	1613-0073

Conference

Conference	Joint of the Summer School on Software Maintenance and Evolution
Country/Territory	Finland
City	Tampere
Period	2/09/19 → 4/09/19

Keywords

Code smells
NLP
Phrase structure rules
Semantic patterns

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

General Computer Science

Access to Document

paper4bFinal published version, 504 KBLicence: CC BY

http://urn.fi/urn:nbn:de:0074-2520-6Licence: CC BY
http://urn.fi/URN:NBN:fi:tuni-202001211433Licence: CC BY

Cite this

@inproceedings{d0903db196a648d4a14f3d71268f172c,

title = "Semantic patterns extraction of code smells: Retrieving the solutions of bugs",

abstract = "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.",

keywords = "Code smells, NLP, Phrase structure rules, Semantic patterns",

author = "Boyang Zhang",

note = "jufoid=53269; Joint of the Summer School on Software Maintenance and Evolution ; Conference date: 02-09-2019 Through 04-09-2019",

year = "2019",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

pages = "71--77",

booktitle = "SSSME-2019",

}

Zhang, B 2019, Semantic patterns extraction of code smells: Retrieving the solutions of bugs. in SSSME-2019: Joint Proceedings of the Inforte Summer School on Software Maintenance and Evolution. CEUR Workshop Proceedings, vol. 2520, CEUR-WS, pp. 71-77, Joint of the Summer School on Software Maintenance and Evolution, Tampere, Finland, 2/09/19. <http://urn.fi/urn:nbn:de:0074-2520-6>

Semantic patterns extraction of code smells: Retrieving the solutions of bugs. / Zhang, Boyang.
SSSME-2019: Joint Proceedings of the Inforte Summer School on Software Maintenance and Evolution. CEUR-WS, 2019. p. 71-77 (CEUR Workshop Proceedings; Vol. 2520).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Semantic patterns extraction of code smells

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PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Code smells

KW - NLP

KW - Phrase structure rules

KW - Semantic patterns

M3 - Conference contribution

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EP - 77

BT - SSSME-2019

PB - CEUR-WS

Y2 - 2 September 2019 through 4 September 2019

ER -

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

Abstract

Publication series

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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