Experiences of Fault Data in a Large Software System

Abstract

Early identification of fault-prone modules is desirable both from developer and customer perspectives since it supports planning and scheduling activities that facilitate cost avoidance and improved time to market. Large scale software systems are rarely built from scratch, and usually involve modification and enhancement of existing systems. This suggest that development planning and software quality could greatly be enhanced, since knowledge about product complexity and quality of previous releases can be taken into account when making improvements in subsequent projects. In this paper we present results from empirical studies at Ericsson Telecom AB which examine the use of metrics to predict fault-prone modules in successive product releases. The results show that such prediction appears to be possible and has the potential to enhance project maintenance. Tables wrongly numbered and text is missing. See paper copy!