Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Performance and scalability of EJB applications
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Estimates of distributions of random variables for certain computer communications traffic models
Proceedings of the first ACM symposium on Problems in the optimization of data communications systems
ICSE '76 Proceedings of the 2nd international conference on Software engineering
RUBiS revisited: why J2EE benchmarking is hard
OOPSLA '04 Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Exploring Software Evolution Using Spectrographs
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Profiling Deployed Software: Assessing Strategies and Testing Opportunities
IEEE Transactions on Software Engineering
Ensembles of Models for Automated Diagnosis of System Performance Problems
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
The Future of Software Performance Engineering
FOSE '07 2007 Future of Software Engineering
Monitoring multi-tier clustered systems with invariant metric relationships
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Proceedings of the ACM/IFIP/USENIX 2007 International Conference on Middleware
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Communications of the ACM
Practical performance models for complex, popular applications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
SASO '10 Proceedings of the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Automatic Comparison of Load Tests to Support the Performance Analysis of Large Enterprise Systems
CSMR '10 Proceedings of the 2010 14th European Conference on Software Maintenance and Reengineering
Are hardware performance counters a cost effective way for integrity checking of programs
Proceedings of the sixth ACM workshop on Scalable trusted computing
Catch me if you can: performance bug detection in the wild
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
Identifying performance deviations in thread pools
ICSM '11 Proceedings of the 2011 27th IEEE International Conference on Software Maintenance
Automated detection of performance regressions using statistical process control techniques
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Uncovering performance problems in Java applications with reference propagation profiling
Proceedings of the 34th International Conference on Software Engineering
Execution profiling blueprints
Software—Practice & Experience
On using incremental profiling for the performance analysis of shared memory parallel applications
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Locating performance improvement opportunities in an industrial software-as-a-service application
ICSM '12 Proceedings of the 2012 IEEE International Conference on Software Maintenance (ICSM)
Discovering, reporting, and fixing performance bugs
Proceedings of the 10th Working Conference on Mining Software Repositories
| Hi-index | 0.00 |
The goal of performance maintenance is to improve the performance of a software system after delivery. As the performance of a system is often characterized by unexpected combinations of metric values, manual analysis of performance is hard in complex systems. In this paper, we propose an approach that helps performance experts locate and analyze spots - so called performance improvement opportunities (PIOs) - for possible performance improvements. PIOs give performance experts a starting point for performance improvements, e.g., by pinpointing the bottleneck component. The technique uses a combination of association rules and performance counters to generate the rule coverage matrix, a matrix which assists with the bottleneck detection. In this paper, we evaluate our technique in two case studies. In the first one, we show that our technique is accurate in detecting the time frame during which a PIO occurs. In the second one, we show that the starting point given by our approach is indeed useful and assists a performance expert in diagnosing the bottleneck component in a system with high precision.