Paper Info
Title
Smells are sensitive to developers!: on the efficiency of (un)guided customized detection.
Abstract
Code smells indicate poor implementation choices that may hinder program comprehension and maintenance. Their informal definition allows developers to follow different heuristics to detect smells in their projects. Machine learning has been used to customize smell detection according to the developer's perception. However, such customization is not guided (i.e. constrained) to consider alternative heuristics used by developers when detecting smells. As a result, their customization might not be efficient, requiring a considerable effort to reach high effectiveness. In fact, there is no empirical knowledge yet about the efficiency of such unguided approaches for supporting developer-sensitive smell detection. This paper presents Histrategy, a guided customization technique to improve the efficiency on smell detection. Histrategy considers a limited set of detection strategies, produced from different detection heuristics, as input of a customization process. The output of the customization process consists of a detection strategy tailored to each developer. The technique was evaluated in an experimental study with 48 developers and four types of code smells. The results showed that Histrategy is able to outperform six widely adopted machine learning algorithms - used in unguided approaches - both in effectiveness and efficiency. It was also confirmed that most developers benefit from using alternative heuristics to: (i) build their tailored detection strategies, and (ii) achieve efficient smell detection.
Year
DOI
Venue
2017
10.1109/ICPC.2017.32
ICPC
Keywords
Field
DocType
unguided customized detection,program comprehension,program maintenance,developer perception,smell detection customization,Histrategy,guided customization technique,detection strategies,detection heuristics,code smells
Data mining,Programming language,Empirical evidence,Software engineering,Computer science,Reverse engineering,Static analysis,Heuristics,Program comprehension,Perception,Code smell,Personalization
Conference
ISSN
ISBN
Citations 
1092-8138
978-1-5386-0536-3
2
PageRank 
References 
Authors
0.37
22
5
Name
Order
Citations
PageRank
Mario Hozano120.37
Alessandro Garcia22231143.70
Nuno Antunes318424.38
Baldoino Fonseca410316.57
Evandro de Barros Costa5307.12