Incremental Feature Selection Method for Software Defect Prediction
N. Gayatri1, S. Nickolas2, A. Subbarao3

1N. Gayatri, Assistant Professor, Kakatiya Institute of Technology and Science, Warangal (Telangana), India.
2S. Nickolas, Professor, National Institute of Technology, Tiruchirappalli (Tamil Nadu), India.
3A. Subbarao, Associate Professor, S R Engineering College, Warangal (Telangana), India.
Manuscript received on 24 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 1345-1353 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B12520782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1252.0782S319
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Abstract: Software defect prediction models are essential for understanding quality attributes relevant for software organization to deliver better software reliability. This paper focuses mainly based on the selection of attributes in the perspective of software quality estimation for incremental database. A new dimensionality reduction method Wilk’s Lambda Average Threshold (WLAT) is presented for selection of optimal features which are used for classifying modules as fault prone or not. This paper uses software metrics and defect data collected from benchmark data sets. The comparative results confirm that the statistical search algorithm (WLAT) outperforms the other relevant feature selection methods for most classifiers. The main advantage of the proposed WLAT method is: The selected features can be reused when there is increase or decrease in database size, without the need of extracting features afresh. In addition, performances of the defect prediction models either remains unchanged or improved even after eliminating 85% of the software metrics.
Keywords: Software Defect Prediction, ANOVA, Wilk’s Lambda, Incremental Feature Selection.
Scope of the Article: Software Engineering & Its Applications