Software test processes are complex and costly. To reduce testing effort without compromising effectiveness and product quality, automation of test activities has been adopted as a popular approach in software industry. However, since test automation usually requires substantial upfront investments, automation is not always more cost-effective than manual testing. To support decision-makers in finding the optimal degree of test automation in a given project, we propose in this paper a simulation model using the System Dynamics (SD) modeling technique. With the help of the simulation model, we can evaluate the performance of test processes with varying degrees of automation of test activities and help testers choose the most optimal cases. As the case study, we describe how we used our simulation model in the context of an Action Research (AR) study conducted in collaboration with a software company in Calgary, Canada. The goal of the study was to investigate how the simulation model can help decision-makers decide whether and to what degree the company should automate their test processes. As a first step, we compared the performances of the current fully manual testing with several cases of partly automated testing as anticipated for implementation in the partner company. The development of the simulation model as well as the analysis of simulation results helped the partner company to get a deeper understanding of the strengths and weaknesses of their current test process and supported decision-makers in the cost effective planning of improvements of selected test activities.

No abstract

No abstract

Web services have increasingly begun to rely on public cloud platforms. The virtualization technologies employed by public clouds can however trigger contention between virtual machines (VMs) for shared physical machine (PM) resources thereby leading to performance problems for the Web service. Past studies have exploited PM level performance metrics such as Clock Cycles Per Instruction to detect such platform induced performance interference. Unfortunately, public cloud customers do not have access to such metrics. They can typically only access VM-level metrics and application level metrics such as transaction response times and such metrics alone are often not useful for detecting inter-VM contention. This poses a difficult challenge to Web service operators for detecting and managing platform induced performance interference issues inside the cloud. We propose a machine learning based interference detection technique to address this problem. The technique applies collaborative filtering to predict whether a given transaction being processed by a Web service is suffering adversely from interference. The results can then be used by a management controller to trigger remedial actions, e.g., reporting problems to the system manager or switching cloud providers. Results using a realistic Web benchmark show that the approach is effective. The most effective variant of our approach is able to detect about 96% of performance interference events with almost no false alarms.

is a Ph.D. candidate and sessional instructor at the University of Calgary. Her research focuses on creativity in electrical and computer engineering. Ms. Marasco is also an education specialist with EZ Robot Inc. and co-hosts The Robot Program, an educational webseries for teaching robotics through technology to thousands of students, educators, and hobbyists around the globe. Ms. Marasco speaks regularly at conferences and in the community on topics from technical work to technological impact. She has won ASTech and 3-Minute Thesis awards for her work in science communication and outreach, and received the 2016 CEMF Claudette MacKay-Lassonde Graduate Award for her work relating to the promotion of women in engineering.