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 languageEnglish
Title of host publicationProceedings of the 2016 IEEE 24th International Conference on Program Comprehension (ICPC)
Place of PublicationDanvers, MA
Number of pages10
ISBN (Electronic)978-1-5090-1428-6
Publication statusPublished - May 2016
Event24th International Conference on Program Comprehension: ICPC 2016 - Austin, TX, United States
Duration: 16 May 201617 May 2016


Conference24th International Conference on Program Comprehension
Abbreviated titleICPC
CountryUnited States
CityAustin, TX

    Research areas

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

ID: 9160043