Detecting Android Smells Using Multi-Objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Kessentini:2017:ieeeMOBILESoft,

• author = "Marouane Kessentini and Ali Ouni",
• booktitle = "2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)",
• title = "Detecting {Android} Smells Using Multi-Objective Genetic Programming",
• year = "2017",
• pages = "122--132",
• abstract = "The evolution rate of mobile applications is much higher than regular software applications having shorter release deadlines and smaller code base. Mobile applications tend to be evolved quickly by developers to meet several new customer requirements and fix discovered bugs. However, evolving the existing features and design may introduce bad design practices, also called code smells, which can highly decrease the maintainability and performance of these mobile applications. However, unlike the area of object-oriented software systems, the detection of code smells in mobile applications received a very little of attention. Recent, few studies defined a set of quality metrics for Android applications and proposed a support to manually write a set of rules to detect code smells by combining these quality metrics. However, finding the best combination of metrics and their thresholds to identify code smells is left to the developer as a manual process. In this paper, we propose to automatically generate rules for the detection of code smells in Android applications using a multi-objective genetic programming algorithm (MOGP). The MOGP algorithm aims at finding the best set of rules that cover a set of code smell examples of Android applications based on two conflicting objective functions of precision and recall. We evaluate our approach on 184 Android projects with source code hosted in GitHub. The statistical test of our results show that the generated detection rules identified 10 Android smell types on these mobile applications with an average correctness higher than 82percent and an average relevance of 77percent based on the feedback of active developers of mobile apps.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/MOBILESoft.2017.29",
• month = may,
• notes = "Also known as \cite{7972726}",

}

Genetic Programming entries for Marouane Kessentini Ali Ouni

Citations