Conference proceeding
A Model-based, Quality Attribute-guided Architecture Re-Design Process at Google
2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp 61-73
May 2023
Abstract
Communicating and justifying design decisions are difficult, especially when the architecture design has to evolve. In this paper, we report our experiences of using formal but lightweight design models to communicate, justify, and analyze the quality trade-offs of an architecture revision plan for Monarch, a large-scale legacy system from Google. We started from a few critical user scenarios and their associated quality attribute scenarios, which makes these models lightweight and concise, expressing high-level abstractions only. We also separated static views from dynamic views so that each diagram can be precise and suitable for analyzing different types of quality attributes respectively. The combination of scenarios, quality attributes, and lightweight modeling was well accepted by the team as an effective way to analyze and communicate the trade-offs. A few days after we presented and shared this process, two new projects within the Monarch team adopted component and sequence diagrams in their design documents, and two other product areas within Google started to learn and to adopt the process as well. Our experience indicates that these architecture modeling and analysis techniques can be integrated into software development process to communicate and assess features, quality attributes, or design decisions continuously and iteratively.
Metrics
Details
- Title
- A Model-based, Quality Attribute-guided Architecture Re-Design Process at Google
- Creators
- Qin Jia - Google (United States)Yuanfang Cai - Drexel UniversityOnur C. Cakmak - Google (United States)IEEE
- Publication Details
- 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp 61-73
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001032815500006
- Scopus ID
- 2-s2.0-85171765336
- Other Identifier
- 991021057566304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering