Optimization and Evaluation in Software Engineering and Design

The field of software engineering and design is moving towards optimizing performance and efficiency while balancing trade-offs in correctness and usability. Researchers are exploring innovative methods to aggregate empirical evidence from data strategy studies, such as model quantization, to improve resource efficiency in deep learning systems. Additionally, there is a growing focus on integrating design for assembly and disassembly principles into modular product architectures to enhance flexibility and sustainability. Furthermore, new methodologies are being developed to evaluate software architectures and complex performance indicators, enabling more systematic and transparent reporting of complex evaluations. Noteworthy papers include: Evaluating an assembly- and disassembly-oriented expansion of Modular Function Deployment, which introduces an expanded method incorporating assembly and disassembly considerations, and ATRAF-driven IMRaD Methodology, which presents a concise method to align software architecture evaluation with academic research formats.

Sources

Aggregating empirical evidence from data strategy studies: a case on model quantization

Evaluating an assembly- and disassembly-oriented expansion of Modular Function Deployment through a workshop-based assessment

ATRAF-driven IMRaD Methodology: Tradeoff and Risk Analysis of Software Architectures Across Abstraction Levels

Uncovering Key Features for Model-Driven Engineering of Complex Performance Indicators: A Scoping Review

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