A Textual-based Technique for Smell Detection

Conference paper (2016)

Authors

F. Palomba University of Salerno
A. Panichella Software Engineering - EEMCS
Andrea De Lucia University of Salerno
Rocco Oliveto University of Molise
A.E. Zaidman Software Engineering - EEMCS
Research Group
Software Engineering (EEMCS) (TU Delft)
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Published Date

2016-5

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

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.

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