Automating the Characterization and Detection of Software Performance Antipatterns Using a Data-Driven Approach

• Main Author: Chalawadi, Ram Kishan
• Format: Others
• Language: English
• Published: Blekinge Tekniska Högskola, Institutionen för datavetenskap 2021
• Subjects: Antipatterns; Detection Of Software Performance Antipatterns; Machine learning; K Nearest Neighbours; Tool Automation; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-22352

Background: With the increase in automating the performance testing strategies, many efforts have been made to detect the Software Performance Antipatterns (SPAs). These performance antipatterns have become a major threat to software platforms at the enterprise level, and detecting these anomalies is essential in any company dealing with performance-sensitive software as these processes should be performed quite often. Due to the complexity of the process, the manual identification of performance issues has become challenging and time-consuming. Objectives: The thesis aims to address and solve the issues mentioned above by developing a tool that automatically Characterizes and Detects Software Performance Antipatterns. The goal is to automate the parameterization process of the existing approach that helps characterize SPAs and improve the interpretation of detection of SPAs. These two processes are integrated into the tool designed to be deployed in the CI/CD pipeline. The developed tool is named Chanterelle. Methods: A case study and a survey has been used in this research. A case study has been conducted at Ericsson. A similar process as in the existing approach has been automated using python. A literature review is conducted to identify an appropriate approach to improve the interpretation of the detection of SPAs. A static user validation has been conducted with the help of a survey consisting of Chanterelle feasibility and usability questions. The responses are provided by Ericsson staff (developers and tester in the field of Software performance) after the tool is presented. Results: The results indicate that the automated parameterization and detection process proposed in this thesis have a considerable execution time compared to the existing approaches and helps the developers interpret the detection results easily. Moreover, it does not include domain experts t run the tests. The results of the static user validation show that Chanterelle is feasible and usable as a tool to be used by the developers. Conclusions: The validation of the tool suggests that Chanterelle helps the developers to interpret the performance-related bugs easily. It performs the automated parameterization and detection process in a considerable time when compared with the existing approaches.