Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Using regression splines for software performance analysis
Proceedings of the 2nd international workshop on Software and performance
Java: performance tuning
Resource Function Capture for Performance Aspects of Software Components and Sub-Systems
Performance Engineering, State of the Art and Current Trends
SKaMPI: A Detailed, Accurate MPI Benchmark
Proceedings of the 5th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Evaluating the Performance of EJB Components
IEEE Internet Computing
Early performance testing of distributed software applications
WOSP '04 Proceedings of the 4th international workshop on Software and performance
SAP Performance Optimization Guide: Analyzing and Tuning SAP Systems
SAP Performance Optimization Guide: Analyzing and Tuning SAP Systems
Software performance in the real world: personal lessons from the performance trauma team
WOSP '07 Proceedings of the 6th international workshop on Software and performance
Mulini: an automated staging framework for QoS of distributed multi-tier applications
Proceedings of the 2007 workshop on Automating service quality: Held at the International Conference on Automated Software Engineering (ASE)
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools
Performance evaluation of component-based software systems: A survey
Performance Evaluation
Practical performance models for complex, popular applications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The Performance Cockpit Approach: A Framework For Systematic Performance Evaluations
SEAA '10 Proceedings of the 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications
Efficient experiment selection in automated software performance evaluations
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Systematic adoption of genetic programming for deriving software performance curves
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
A generic methodology to derive domain-specific performance feedback for developers
Proceedings of the 34th International Conference on Software Engineering
Systematic performance evaluation based on tailored benchmark applications
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
An experiment specification language for goal-driven, automated performance evaluations
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Context-sensitive delta inference for identifying workload-dependent performance bottlenecks
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Performance-Aware design of web application front-ends
ICWE'13 Proceedings of the 13th international conference on Web Engineering
Performance queries for architecture-level performance models
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Understanding the dependency between performance metrics (such as response time) and software configuration or usage parameters is crucial in improving software quality. However, the size of most modern systems makes it nearly impossible to provide a complete performance model. Hence, we focus on scenario-specific problems where software engineers require practical and efficient approaches to draw conclusions, and we propose an automated, measurement-based model inference method to derive goal-oriented performance prediction functions. For the practicability of the approach it is essential to derive functional dependencies with the least possible amount of data. In this paper, we present different strategies for automated improvement of the prediction model through an adaptive selection of new measurement points based on the accuracy of the prediction model. In order to derive the prediction models, we apply and compare different statistical methods. Finally, we evaluate the different combinations based on case studies using SAP and SPEC benchmarks.