A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis

• URL: http://arxiv.org/abs/2410.17372v1
• Date: Tue, 22 Oct 2024 19:12:03 GMT
• Title: A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis
• Authors: Yutong Zhao, Lu Xiao, Chenhao Wei, Rick Kazman, Ye Yang,
• Abstract summary: This paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focus on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area.
• Score: 8.629569588488328
• License:
• Abstract: Software architecture is the foundation of a system's ability to achieve various quality attributes, including software performance. However, there lacks comprehensive and in-depth understanding of why and how software architecture and performance analysis are integrated to guide related future research. To fill this gap, this paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focused on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area. These questions addressed: a systematic mapping of related studies based on the high-level research purposes and specific focuses (RQ1), the software development activities these studies intended to facilitate (RQ2), the typical study templates of different research purposes (RQ3), the available tools and instruments for automating the analysis (RQ4), and the evaluation methodology employed in the studies (RQ5). Through these research questions, we also identified critical research gaps and future directions, including: 1) the lack of available tools and benchmark datasets to support replication, cross-validation and comparison of studies; 2) the need for architecture and performance analysis techniques that handle the challenges in emerging software domains; 3) the lack of consideration of practical factors that impact the adoption of the architecture and performance analysis approaches; and finally 4) the need for the adoption of modern ML/AI techniques to efficiently integrate architecture and performance analysis.

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