Paper Info
Title
A systematic review of machine learning techniques for software fault prediction.
Abstract
•Reviews studies from 1991-2013 to assess application of ML techniques for SFP.•Identifies seven categories of the ML techniques.•Identifies 64 studies to answer the established research questions.•Selects primary studies according to the quality assessment of the studies.•Systematic literature review performs the following:•Summarize ML techniques for SFP models.•Assess performance accuracy and capability of ML techniques for constructing SFP models.•Provide comparison between the ML and statistical techniques.•Provide comparison of performance accuracy of different ML techniques.•Summarize the strength and weakness of the ML techniques.•Provides future guidelines to software practitioners and researchers.
Year
DOI
Venue
2015
10.1016/j.asoc.2014.11.023
Applied Soft Computing
Keywords
Field
DocType
Machine learning,Software fault proneness,Systematic literature review
Software fault,Data mining,Systematic review,Fault prone,Computer science,Software,Statistical model,Systems development life cycle,Artificial intelligence,Strengths and weaknesses,Machine learning
Journal
Volume
ISSN
Citations 
27
1568-4946
82
PageRank 
References 
Authors
2.23
104
1
Search Limit
100104
Name
Order
Citations
PageRank
Ruchika Malhotra153335.12