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
Performance Engineering of Software Systems
Performance Engineering of Software Systems
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
Methods of inference and learning for performance modeling of parallel applications
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
The Future of Software Performance Engineering
FOSE '07 2007 Future of Software Engineering
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
A framework for measurement based performance modeling
WOSP '08 Proceedings of the 7th international workshop on Software and performance
Automatic request categorization in internet services
ACM SIGMETRICS Performance Evaluation Review
Estimating service resource consumption from response time measurements
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Methods for evolving robust programs
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
An analysis of diversity of constants of genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Performance evaluation of component-based software systems: A survey
Performance Evaluation
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
Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Statistical inference of software performance models for parametric performance completions
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
Efficient experiment selection in automated software performance evaluations
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Automated inference of goal-oriented performance prediction functions
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Measurement-based approaches to software performance engineering apply analysis methods (e.g., statistical inference or machine learning) on raw measurement data with the goal to build a mathematical model describing the performance-relevant behavior of a system under test (SUT). The main challenge for such approaches is to find a reasonable trade-off between minimizing the amount of necessary measurement data used to build the model and maximizing the model's accuracy. Most existing methods require prior knowledge about parameter dependencies or their models are limited to only linear correlations. In this paper, we investigate the applicability of genetic programming (GP) to derive a mathematical equation expressing the performance behavior of the measured system (software performance curve). We systematically optimized the parameters of the GP algorithm to derive accurate software performance curves and applied techniques to prevent overfitting. We conducted an evaluation with a representative MySQL database system. The results clearly show that the GP algorithm outperforms other analysis techniques like inverse distance weighting (IDW) and multivariate adaptive regression splines (MARS) in terms of model accuracy.