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Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained Functionality
When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation. The data used in training is where the functionality is ultimately defined, and that is where you will find your issues and bugs.
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Refactoring to a Deeper Model
Paul Rayner uses a case study to demonstrate how refactoring your code can lead to a deeper understanding of your domain model. Through common code refactorings, combined with the implementation of patterns, the codebase became more cohesive and easier to reason about, reducing the time to perform some common tasks from weeks or months to just hours.
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Domain Analysis by Color Modeling
Xu Hao uses the color modeling technique to analyze a domain, identifying the events to be traced and their corresponding moment-intervals, then the domain entities and the role they play.
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Modeling in the Agile Age: What to Keep Next to Code to Scale Agile Teams
Now that Agile methods have become mainstream in software development, working code is considered the most important team artifact. There is still a need for modeling. Kenji Hiranabe explores the spaces where modeling fits and plays an important role in this Agile age. With focus on development scaling to multiple teams where a shared understanding of the system’s “Big Picture” becomes essential.
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There is a Cowboy in my Domain! - Implementing Domain Driven Design Review and Interview
Implementing Domain Driven Design, has brought clarity to an important but little understood area of software design. As a measure of Software Design literature, Vaughn's work is educational and fun. With real world code samples and sage advice, IDDD guides the reader through the sometimes murky waters of DDD and helps them gain the insight required to start a DDD journey of their own.
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The Seven Information Smells of Domain Modelling
Domain modelling is a powerful technique that many IT professionals have in their toolkit. Unfortunately a couple of issues with domain modelling have caused it to fall out of favour over the past few years, especially in Agile circles. Two real problems with the approach are that it takes too long, and that it is prone to “analysis paralysis”. This is an approach that addresses these issues.
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Success Factors for Systematic Reuse
Systematic reuse requires the interplay of people, process, and technology decisions executed within the context of real world constraints. Are there success factors that will make a difference to reuse? This article offers five success factors that will help capture domain variations, ease integration, delve deeper into design context, work effectively as a team, and manage domain complexity.
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Strategic Domain Driven Design with Context Mapping
Many approaches to object oriented modeling tend not to scale well when the applications grow in size and complexity. Context Mapping technique can be used to manage the complexity in large software development projects. In this article, author Alberto Brandolini discusses the many sides of bounded contexts and how to use them to build a context map to support key decisions in a software project.
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Domain Driven Design and Development In Practice
In this article, Srini Penchikala discusses Domain Driven Design and Development from a practical stand-point. The article looks at architectural and design guidelines and best practices that can be used in a DDD project. It also talks about the impact of various design concerns like Persistence, Caching, Transaction Management, Security, Code Generation etc in domain model implementation effort.