A Textual-based Technique for Smell Detection

Fabio Palomba; Annibale Panichella; Andrea De Lucia; Rocco Oliveto; Andy Zaidman

doi:10.1109/ICPC.2016.7503704

A Textual-based Technique for Smell Detection

Fabio Palomba, Annibale Panichella, Andrea De Lucia, Rocco Oliveto, Andy Zaidman

Software Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

101 Citations (Scopus)

Abstract

In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE 24th International Conference on Program Comprehension (ICPC)
Place of Publication	Danvers, MA
Publisher	IEEE
Pages	1-10
Number of pages	10
ISBN (Electronic)	978-1-5090-1428-6
DOIs	https://doi.org/10.1109/ICPC.2016.7503704
Publication status	Published - May 2016
Event	24th International Conference on Program Comprehension: ICPC 2016 - Austin, TX, United States Duration: 16 May 2016 → 17 May 2016

Conference

Conference	24th International Conference on Program Comprehension
Abbreviated title	ICPC
Country/Territory	United States
City	Austin, TX
Period	16/05/16 → 17/05/16

Keywords

Detectors
Measurement
Feature extraction
Algorithm design and analysis
Vocabulary
Large scale integration
Programming

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10.1109/ICPC.2016.7503704

Cite this

@inproceedings{10d20ef8160841d8929fb08c7febbb2e,

title = "A Textual-based Technique for Smell Detection",

abstract = "In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.",

keywords = "Detectors, Measurement, Feature extraction, Algorithm design and analysis, Vocabulary, Large scale integration, Programming",

author = "Fabio Palomba and Annibale Panichella and {De Lucia}, Andrea and Rocco Oliveto and Andy Zaidman",

year = "2016",

month = may,

doi = "10.1109/ICPC.2016.7503704",

language = "English",

pages = "1--10",

booktitle = "Proceedings of the 2016 IEEE 24th International Conference on Program Comprehension (ICPC)",

publisher = "IEEE",

address = "United States",

note = "24th International Conference on Program Comprehension : ICPC 2016, ICPC ; Conference date: 16-05-2016 Through 17-05-2016",

}

Palomba, F, Panichella, A, De Lucia, A, Oliveto, R & Zaidman, A 2016, A Textual-based Technique for Smell Detection. in Proceedings of the 2016 IEEE 24th International Conference on Program Comprehension (ICPC). IEEE, Danvers, MA, pp. 1-10, 24th International Conference on Program Comprehension, Austin, TX, United States, 16/05/16. https://doi.org/10.1109/ICPC.2016.7503704

TY - GEN

T1 - A Textual-based Technique for Smell Detection

AU - Palomba, Fabio

AU - Panichella, Annibale

AU - De Lucia, Andrea

AU - Oliveto, Rocco

AU - Zaidman, Andy

PY - 2016/5

Y1 - 2016/5

N2 - In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.

AB - In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.

KW - Detectors

KW - Measurement

KW - Feature extraction

KW - Algorithm design and analysis

KW - Vocabulary

KW - Large scale integration

KW - Programming

U2 - 10.1109/ICPC.2016.7503704

DO - 10.1109/ICPC.2016.7503704

M3 - Conference contribution

SP - 1

EP - 10

BT - Proceedings of the 2016 IEEE 24th International Conference on Program Comprehension (ICPC)

PB - IEEE

CY - Danvers, MA

T2 - 24th International Conference on Program Comprehension

Y2 - 16 May 2016 through 17 May 2016

ER -

A Textual-based Technique for Smell Detection

Abstract

Conference

Keywords

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