Reducing overfitting in genetic programming models for software quality classification

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

@InProceedings{liu:2004:rogp,

• author = "Yi Liu and Taghi Khoshgoftaar",
• title = "Reducing overfitting in genetic programming models for software quality classification",
• booktitle = "Proceedings of the Eighth IEEE Symposium on International High Assurance Systems Engineering",
• year = "2004",
• month = "25-26 " # mar,
• pages = "56--65",
• address = "Tampa, Florida, USA",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1530-2059",
• DOI = "doi:10.1109/HASE.2004.1281730",
• DOI = "doi:10.1109/HASE.2004.1281730",
• size = "10 pages",
• abstract = "A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. In this paper, we present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The overfitting problem has not been addressed in depth for GP-based models. The general aim of classifying software modules as fault-prone (fp) and not fault-prone (nfp) is to aid software management in expending its limited resources toward improving only the fp modules. The presence of overfitting in such a software quality model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen data, i.e., generalisation performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We use a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we use random sampling to reduce overfitting with the aim of improving the generalization capability of our GP models. A case study of an industrial high-assurance software system is used to demonstrate the effectiveness of the random sampling technique.",
• notes = "HASE 2004",

}

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar

Citations