Software functional testing can unveil a wide range of potential malfunctions in applications. However, there is a significant fraction of errors that will be hardly detected through a traditional testing process. Problems such as memory corruptions, memory leaks, performance bottlenecks, low;level system call failures and I/O errors might not surface any symptoms in a tester’s machine while causing disasters in production. On the other hand, many handy tools have been showing up in all popular platforms allowing a tester or an analyst to monitor the behavior of an application with respect to these dark areas in order to identify potential fatal problems that would go unnoticed otherwise. Unfortunately, these tools are not yet in widespread use due to few reasons. First, the usage of tools requires a certain amount of expertise on system internals. Furthermore, these monitoring tools generate a vast amount of data even with elegant filtering and thereby demand a significant amount of time for an analysis even from experts. As the end result, using monitoring tools to improve software quality becomes a costly operation. Another facet of this problem is the lack of infrastructure to automate recurring analysis patterns.

This work involved developing a framework that automates a significant part of the process of monitoring various quality aspects of a software application with the utilization of tools and deriving conclusions based on results. According to our knowledge this is the first framework to do this. It formulates infrastructure for analysts to extract relevant data from monitoring tool logs, process those data, make inferences and present analysis results to a wide range of stakeholders in a project.

Collaborated with: Department of Electrical Engineering, University of Moratuwa