Reducing Corrective Maintenance Effort Considering Module's History
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper has used fault prediction techniques based both on classical complexity metrics and an additional, innovative factor related to the module's age in terms of release to estimate an optimal repartition of available testing time among software modules in a maintenance release.
Abstract
A software package evolves in time through various maintenance release steps whose effectiveness depends mainly on the number of faults left in the modules. The testing phase is therefore critical to discover these faults. The purpose of this paper is to show a criterion to estimate an optimal repartition of available testing time among software modules in a maintenance release. In order to achieve this objective we have used fault prediction techniques based both on classical complexity metrics and an additional, innovative factor related to the module's age in terms of release. This method can actually diminish corrective maintenance effort, while assuring a high reliability for the delivered software.
