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A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

초록

영어

Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects.Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project.The software defectprediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality.In this paper, variousclassification techniquesare revisitedwhich are employed for software defect prediction using software metrics in the literature.

목차

Abstract
 1. Introduction
 2. Software Metric
 3. Software Defect Prediction
 4. Software Defect Prediction (SDP) using Different Classification Techniques
  4.1 SDP using Supervised Learning
  4.2 SDP using Semi-supervised Learning
  4.3. SDP using Unsupervised Learning
  4.4. SDP using Machine Learning Algorithm
 5. Conclusion and Future Scope
 References

저자정보

  • Manjula.C.M. Prasad Associate Professor, MCA Department, PESIT, BSC, Karnataka.
  • Lilly Florence Professor, MCA Department, Adiyamman College of Engineering, Tamil Nadu.
  • Arti Arya Professor, MCA Department, PESIT, BSC, Karnataka.

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