Multi-objective code-smells detection using good and bad design examples

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

@Article{Mansoor:2017:SQJ,

• author = "Usman Mansoor and Marouane Kessentini and Bruce R. Maxim and Kalyanmoy Deb",
• title = "Multi-objective code-smells detection using good and bad design examples",
• journal = "Software Quality Journal",
• year = "2017",
• volume = "25",
• number = "2",
• pages = "529--552",
• month = jun,
• keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Search-based software engineering, Software maintenance, Software metrics, NSGA-2",
• ISSN = "1573-1367",
• DOI = "doi:10.1007/s11219-016-9309-7",
• abstract = "Code-smells are identified, in general, by using a set of detection rules. These rules are manually defined to identify the key symptoms that characterize a code-smell using combinations of mainly quantitative (metrics), structural, and/or lexical information. We propose in this work to consider the problem of code-smell detection as a multi-objective problem where examples of code-smells and well-designed code are used to generate detection rules. To this end, we use multi-objective genetic programming (MOGP) to find the best combination of metrics that maximizes the detection of code-smell examples and minimizes the detection of well-designed code examples. We evaluated our proposal on seven large open-source systems and found that, on average, most of the different five code-smell types were detected with an average of 87percent of precision and 92percent of recall. Statistical analysis of our experiments over 51 runs shows that MOGP performed significantly better than state-of-the-art code-smell detectors.",

}

Genetic Programming entries for Usman Mansoor Marouane Kessentini Bruce R Maxim Kalyanmoy Deb

Citations