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cv usk
@cv_usk
AI / Software Research Notes AI Agent, LLMOps, MLOps, Software Architecture ๆŠ•็จฟใฏๅ€‹ไบบใฎๆ„่ฆ‹ใงใ™ใ€‚
Joined May 2026
257 Following    203 Followers
"How is DDD actually used in open source, and how does it relate to maintenance quality?" โ€” famously discussed, yet almost no large-scale evidence existed. This study tackles it by mining 865 repositories ๐Ÿ” Title: Domain-Driven Design in Practice: A Mining Study of Maintenance and Evolution in Open-Source Repositories URL: โ“ What does it study? ๐Ÿ’ก Starting from 1,260 DDD-tagged GitHub repos filtered down to 865 (Java/C#/TypeScript), it analyzes eight tactical building blocks: Entities, Value Objects, Aggregates, Repositories, Domain Services, Domain Events, Application Services, Factories. โ“ How are they detected? ๐Ÿ’ก A three-layer pipeline: DDD-specific annotations, naming conventions (e.g. OrderRepository), and package/directory structure โ€” proceeding only when precision โ‰ฅ0.75 is met. โ“ What's the biggest technical challenge? ๐Ÿ’ก Bounded Context boundary violations. It operationalizes the "model-code gap" as cross-context dependencies, with violation rate = cross-BC deps / total inter-class deps. Boundary inference is human-validated requiring Cohen's kappa โ‰ฅ0.80. โ“ How is rigor ensured? ๐Ÿ’ก Kruskal-Wallis + Dunn post-hoc for cross-language tests, Spearman's rho for correlations, controlling age/size/language/type/team-size as confounders, with a pre-registered "degradation plan" that narrows scope rather than invalidating results if detection misses precision thresholds. The methodological rigor stands out โ€” a solid empirical foundation for discussing DDD maintenance and evolution. #DDD# #SoftwareEngineering#
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