Detecting Performance Antipatterns in Com-ponent Based Enterprise Systems.
Trevor Parsons, John Murphy
Abstract
Open-access reader
Trevor Parsons, John Murphy
Abstract
Open-access reader
We introduce an approach for automatic detection of performance antipatterns.The approach is based on a number of advanced monitoring and analysis techniques.The advanced analysis is used to identify relationships and patterns in the monitored data.This information is subsequently used to reconstruct a design model of the underlying system, which is loaded into a rule engine in order to identify predefined antipatterns.We give results of applying this approach to identify a number of antipatterns in two JEE applications.Finally, this work also categorises JEE antipatterns into categories based on the data needed to detect them.
OpenAlex reports 101 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
We introduce an approach for automatic detection of performance antipatterns.The approach is based on a number of advanced monitoring and analysis techniques.The advanced analysis is used to identify relationships and patterns in the monitored data.This information is subsequently used to reconstruct a design model of the underlying system, which is loaded into a rule engine in order to identify predefined antipatterns.We give results of applying this approach to identify a number of antipatterns in two JEE applications.Finally, this work also categorises JEE antipatterns into categories based on the data needed to detect them.
Key concepts: Computer science, Data mining, Software engineering