4.6 Review

Lightweight Software Architecture Evaluation for Industry: A Comprehensive Review

Journal

SENSORS
Volume 22, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/s22031252

Keywords

software architectural evaluation; lightweight software architecture; heavyweight software architecture; software quality

Funding

  1. Ministry of Higher Education of Malaysia [KKP/2020/UKM-UKM/4/3]

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Software architecture evaluation is important for identifying problems and risks, but heavy evaluation methods are not widely adopted by industrial practitioners. This study proposes a lightweight evaluation method based on practitioners' concerns to improve the existing architectural evaluation methods.
Processes for evaluating software architecture (SA) help to investigate problems and potential risks in SA. It is derived from many studies that proposed a plethora of systematic SA evaluation methods, while industrial practitioners currently refrain from applying them since they are heavyweight. Nowadays, heterogeneous software architectures are organized based on the new infrastructure. Hardware and associated software allow different systems, such as embedded, sensor-based, modern AI, and cloud-based systems, to cooperate efficiently. It brings more complexities to SA evaluation. Alternatively, lightweight architectural evaluation methods have been proposed to satisfy the practitioner's concerns, but practitioners still do not adopt these methods. This study employs a systematic literature review with a text analysis of SA's definitions to propose a comparison framework for SA. It identifies lightweight features and factors to improve the architectural evaluation methods among industrial practitioners. The features are determined based on the practitioner's concerns by analyzing the architecture's definitions from stakeholders and reviewing architectural evaluation methods. The lightweight factors are acquired by studying the five most commonly used lightweight methods and the Architecture-based Tradeoff Analysis Method (ATAM), the most well-known heavyweight method. Subsequently, the research addresses these features and factors.

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