This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
||||||||
|
Paper Details
Paper Title
A Systematic Review Based On Machine Learning Techniques for Software Defect Predication
Authors
Karishma Manchanda, Heena Wadhwa, Harsimran Kaur
Abstract
Software Defect Predication is an important step in software engineering. A meta-analysis of all relevant data, high quality primary studies of defect prediction has been used to determine that what factors are influencing the predictive performance. This paper reflects various methods of default prediction in Software module by using feature extraction and machine learning techniques and provides a systematic approach to build a defect-free system. .
Keywords- Feature Extraction, Machine Learning, Software Defect Predication, Software Engineering.
Publication Details
Unique Identification Number - IJEDR1602227Page Number(s) - 1290-1292Pubished in - Volume 4 | Issue 2 | May 2016DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Karishma Manchanda, Heena Wadhwa, Harsimran Kaur, " A Systematic Review Based On Machine Learning Techniques for Software Defect Predication", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.1290-1292, May 2016, Available at :http://www.ijedr.org/papers/IJEDR1602227.pdf
Article Preview
|
|
||||||
|
|
||||||||
