Paper Info
Title
Building Scalable Failure-proneness Models Using Complexity Metrics for Large Scale Software Systems
Abstract
Building statistical models for estimating failureproneness of systems can help software organizations make early decisions on the quality of their systems. Such early estimates can be used to help inform decisions on testing, refactoring, code inspections, design rework etc. This paper demonstrates the efficacy of building scalable failure-proneness models based on code complexity metrics across the Microsoft Windows operating system code base. We show the ability of such models to estimate failure-proneness and provide feedback on the complexity metrics to help guide refactoring and the design rework effort.
Year
DOI
Venue
2006
10.1109/APSEC.2006.25
APSEC
Keywords
Field
DocType
guide refactoring,complexity metrics,microsoft windows operating system,code inspection,code complexity metrics,early estimate,code base,large scale software systems,early decision,design rework effort,scalable failure-proneness model,building scalable failure-proneness,statistical model,software systems,operating system,software metrics
Static program analysis,Systems engineering,Computer science,Cyclomatic complexity,Real-time computing,Software system,Software,Software metric,Software construction,Code refactoring,Reliability engineering,Software development
Conference
ISBN
Citations 
PageRank 
0-7695-2685-3
2
0.38
References 
Authors
12
2
Name
Order
Citations
PageRank
Thirumalesh Bhat135215.29
Nachiappan Nagappan24602190.30