Predicting Defects in Software Using Grammar-Guided Genetic Programming

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

@InProceedings{conf/setn/TsakonasD08,

• author = "Athanasios Tsakonas and Georgios Dounias",
• title = "Predicting Defects in Software Using Grammar-Guided Genetic Programming",
• booktitle = "Proceedings 5th Hellenic Conference on AI, SETN 2008",
• year = "2008",
• editor = "John Darzentas and George A. Vouros and Spyros Vosinakis and Argyris Arnellos",
• series = "Lecture Notes in Computer Science",
• volume = "5138",
• pages = "413--418",
• address = "Syros, Greece",
• month = oct # " 2-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE, Software engineering, defect prediction",
• isbn13 = "978-3-540-87880-3",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.3005",
• DOI = "doi:10.1007/978-3-540-87881-0_42",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• contributor = "CiteSeerX",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.149.3005",
• abstract = "The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules.",

}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias

Citations