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 |
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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 | |
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 |
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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