Quality assurance in performance: evaluating mono benchmark results

Authors:
Tomas Kalibera;Lubomir Bulej;Petr Tuma
Affiliations:
Distributed Systems Research Group,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;Distributed Systems Research Group,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;Distributed Systems Research Group,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
Venue:
QoSA'05 Proceedings of the First international conference on Quality of Software Architectures and Software Quality, and Proceedings of the Second International conference on Software Quality
Year:
2005

Citing 5
Cited 0

Extreme Programming Installed

Extreme Programming Installed
An Automated Benchmarking Toolset

HPCN Europe 2000 Proceedings of the 8th International Conference on High-Performance Computing and Networking
CORBA Benchmarking: A Course with Hidden Obstacles

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Skoll: Distributed Continuous Quality Assurance

Proceedings of the 26th International Conference on Software Engineering
Repeated results analysis for middleware regression benchmarking

Performance Evaluation - Performance modelling and evaluation of high-performance parallel and distributed systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Performance is an important aspect of software quality. To prevent performance degradation during software development, performance can be monitored and software modifications that damage performance can be reverted or optimized. Regression benchmarking provides means for an automated monitoring of performance, yielding a list of software modifications potentially associated with performance changes. We focus on locating individual modifications as causes of individual performance changes and present three methods that help narrow down the list of modifications potentially associated with a performance change. We illustrate the entire process on a real world project.