Vol. 10 Issue 3
Year:2016
Issue:Jan-Mar
Title: A Survey of Genetic Feature Selection for Software Defect Prediction
Author Name:R. Reena, and R. Thirumalai Selvi
Synopsis:
Software defect prediction is an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of the existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high-dimensional and noisy attribute datasets. In this survey, a Genetic Algorithm and a bagging technique is a research topic for Software Defect Prediction. The survey of publications on this topic leads to the conclusion that the field of genetic algorithms applications is growing fast. The authors overall aim is to provide an efficient feature selection for further development of the research.
Year:2016
Issue:Jan-Mar
Title: A Survey of Genetic Feature Selection for Software Defect Prediction
Author Name:R. Reena, and R. Thirumalai Selvi
Synopsis:
Software defect prediction is an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of the existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high-dimensional and noisy attribute datasets. In this survey, a Genetic Algorithm and a bagging technique is a research topic for Software Defect Prediction. The survey of publications on this topic leads to the conclusion that the field of genetic algorithms applications is growing fast. The authors overall aim is to provide an efficient feature selection for further development of the research.
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