Shadi Ghaith

Profile-Based, Load-Independent Anomaly Detection and Analysis in Performance Regression Testing of Software Systems

Performance evaluation through regression testing is an important step in the software production process. It aims to make sure that the performance of new releases do not regress under a field-like load. The main outputs of regression tests are the metrics that represent the response time of various transactions as well as the resource utilization (CPU, disk I/Oand Network). In this paper, we propose to use a concept known as Transaction Profile, which can provide a detailed representation for the transaction in a load independent manner, to detect anomalies through performance test runs. The approach uses data readily available in performance regression tests and a queueing network model of the system under test to infer the Transactions Profiles. Our initial results show that the Transactions Profiles calculated from load regression test data uncover the performance impact of any update to the software. Therefore we conclude that using Transactions Profiles is an effective approach to allow testing teams to easily assure each new software release does not suffer performance regression.

Analysis of performance regression testing data by transaction profiles

Performance regression testing is an important step in the software development lifecycle, especially for enterprise applications. Commonly the analysis of performance regression testing to find anomalies is carried out manually and therefore can be error-prone, time consuming and sensitive to the input load. In our research, we propose a new technique that overcomes the above problems which helps the performance testing teams to improve their process and speeds up the entire software production process.

Anomaly detection in performance regression testing by transaction profile estimation

As part of the process to test a new release of an application, the performance testing team need to confirm that the existing functionalities do not perform worse than those in the previous release, a problem known as performance regression anomaly. Most existing approaches to analyse performance regression testing data vary according to the applied workload, which usually leads to the need for an extra performance testing run. To ease such lengthy tasks, we propose a new workload‐independent, automated technique to detect anomalies in performance regression testing data using the concept known as transaction profile (TP). The TP is inferred from the performance regression testing data along with the queueing network model of the testing system. Based on a case study conducted against two web applications, one open source and one industrial, we have been able to automatically generate the ‘TP run report’ and verify that it can be used to uncover performance regression anomalies caused by software updates. In particular, the report helped us to isolate the real anomaly issues from those caused by workload changes with an average F1 measure of 85% for the open source application and 90% for the industrial application. Such results support our proposal to use the TP as a more efficient technique in identifying performance regression anomalies than the state of the art industry and research techniques. Copyright