Towards detecting software performance anti-patterns using classification techniques

Authors:
Manjula Peiris;James H. Hill
Affiliations:
Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA;Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
Venue:
ACM SIGSOFT Software Engineering Notes
Year:
2014

Citing 12
Cited 0

Support-Vector Networks

Machine Learning
Software performance antipatterns

Proceedings of the 2nd international workshop on Software and performance
Reconsidering custom memory allocation

OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A framework for detecting performance design and deployment antipatterns in component based enterprise systems

DSM '05 Proceedings of the 2nd international doctoral symposium on Middleware
Correlating instrumentation data to system states: a building block for automated diagnosis and control

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Unit Testing Non-functional Concerns of Component-based Distributed Systems

ICST '09 Proceedings of the 2009 International Conference on Software Testing Verification and Validation
A Bayesian Approach for the Detection of Code and Design Smells

QSIC '09 Proceedings of the 2009 Ninth International Conference on Quality Software
Fingerprinting the datacenter: automated classification of performance crises

Proceedings of the 5th European conference on Computer systems
Characterizing, modeling, and generating workload spikes for stateful services

Proceedings of the 1st ACM symposium on Cloud computing
Structured comparative analysis of systems logs to diagnose performance problems

NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Performance debugging in the large via mining millions of stack traces

Proceedings of the 34th International Conference on Software Engineering
An approach for modeling and detecting software performance antipatterns based on first-order logics

Software and Systems Modeling (SoSyM)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a non-intrusive machine learning approach called Non-intrusive Performance Anti-pattern Detecter (NiPAD) for identifying and classifying software performance anti-patterns. NiPAD uses only system performance metrics-as opposed to analyzing application level performance metrics or source code and the design of a software application to identify and classify software performance anti-patterns within an application. The results of applying NiPAD to an example application show that NiPAD is able to predict the One Lane Bridge software performance anti-pattern within a software application with 0.94 accuracy.