Predicting defects in SAP Java code: An experience report

Author

Holschuh, Tilman ; Päuser, Markus ; Herzig, Kim ; Zimmermann, Thomas ; Premraj, Rahul ; Zeller, Andreas

fYear

2009

fDate

16-24 May 2009

Firstpage

172

Lastpage

181

Abstract

Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50-60% of the 20% most defect-prone components.

Keywords

Java; object-oriented programming; program debugging; program diagnostics; quality assurance; software maintenance; software metrics; software quality; SAP Java code; complexity metric; defect prediction; large-software system component; software change frequency; software component import; software quality assurance; static error detector; Detectors; Frequency; History; Java; Power system modeling; Predictive models; Quality assurance; Resource management; Software systems; Software testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering - Companion Volume, 2009. ICSE-Companion 2009. 31st International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

978-1-4244-3495-4

Type

conf

DOI

10.1109/ICSE-COMPANION.2009.5070975

Filename

5070975