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Big Data Analysis of Software Performance Trend using SPC with Flexible Moving Window and Fuzzy Theory

가변 윈도우 기법을 적용한 통계적 공정 제어와 퍼지추론 기법을 이용한 소프트웨어 성능 변화의 빅 데이터 분석

  • Received : 2012.08.27
  • Accepted : 2012.09.25
  • Published : 2012.11.01

Abstract

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.

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