On the Use of Smelly Examples to Detect Code Smells in JavaScript

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Shoenberger:2017:evoApplications,

• author = "Ian Shoenberger and Mohamed Wiem Mkaouer and Marouane Kessentini",
• title = "On the Use of Smelly Examples to Detect Code Smells in {JavaScript}",
• booktitle = "20th European Conference on the Applications of Evolutionary Computation",
• year = "2017",
• editor = "Giovanni Squillero",
• series = "LNCS",
• volume = "10200",
• publisher = "Springer",
• pages = "20--34",
• address = "Amsterdam",
• month = "19-21 " # apr,
• organisation = "Species",
• keywords = "genetic algorithms, genetic programming, SBSE",
• DOI = "doi:10.1007/978-3-319-55792-2_2",
• size = "16 pages",
• abstract = "JavaScript has become one of the widely-used languages. However, as the size of JavaScript-based applications grows, the number of defects grows as well. Recent studies have produced a set of manually defined rules to identify these defects. We propose, in this work, the automation of deriving these rules to ensure scalability and potentially the detection of a wider set of defects without requiring any extensive knowledge on rules tuning. To this end, we rely on a base of existing code smells that is used to train the detection rules using Genetic Programming and find the best threshold of metrics composing the rules. The evaluation of our work on 9 JavaScript web projects has shown promising results in terms of detection precision of 92percent and recall of 85percent, with no threshold tuning required.",
• notes = "EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.",

}

Genetic Programming entries for Ian Shoenberger Mohamed Wiem Mkaouer Marouane Kessentini

Citations