DOI

Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on source code maintainability and comprehen-sibility has been widely shown in the past and several techniques to automatically detect them have been devised. Most of these techniques are based on heuristics, namely they compute a set of code metrics and combine them by creating detection rules; while they have a reasonable accuracy, a recent trend is represented by the use of machine learning where code metrics are used as predictors of the smelliness of code artefacts. Despite the recent advances in the field, there is still a noticeable lack of knowledge of whether machine learning can actually be more accurate than traditional heuristic-based approaches. To fill this gap, in this paper we propose a large-scale study to empirically compare the performance of heuristic-based and machine-learning-based techniques for metric-based code smell detection. We consider five code smell types and compare machine learning models with DECOR, a state-of-the-art heuristic-based approach. Key findings emphasize the need of further research aimed at improving the effectiveness of both machine learning and heuristic approaches for code smell detection: while DECOR generally achieves better performance than a machine learning baseline, its precision is still too low to make it usable in practice.
Original languageEnglish
Title of host publicationin Proceedings of the 27th International Conference on Program Comprehension
PublisherIEEE, Piscataway, NJ, USA
Pages93-104
Number of pages12
DOIs
Publication statusPublished - May 2018
Event27th International Conference on Program Comprehension - Montreal, Canada
Duration: 25 May 201926 May 2019
Conference number: 27
https://conf.researchr.org/home/icpc-2019

Conference

Conference27th International Conference on Program Comprehension
Abbreviated titleICPC
CountryCanada
CityMontreal
Period25/05/1926/05/19
Internet address

    Research areas

  • Code Smells Detection, Heuristics, Machine Learning, Empirical Study

ID: 46199491