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Integration of Automatic Code Generation in Model-Based Algorithm Development; Prerequisites, Workflow and the Human Factor

机译:基于模型的算法开发中自动代码生成的集成;先决条件,工作流和人文因素

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Automatic code generation is an established technology in automotive and aerospace industries that is also adopted in commercial vehicle embedded software development. Code generation brings the productivity gains of model-based algorithm design to the next stage of the process - production software development. The technology has matured to the point where it satisfies most technical and usability requirements and the success of code generation depends on judicious deployment, efficient work practices and acceptance by all users involved. It has been demonstrated that model-based software development and code generation in particular can shorten development cycles while staying true to the requirements and maintaining software quality through multiple algorithm iterations. These gains, though, only come as a result of careful combination of tools and methods. This paper discusses solutions to common organizational and technical challenges of model-based software development. These include workflows to bridge disconnects between algorithm and software development, supplier and OEM, companies and their foreign subsidiaries. In addition to organizational planning, technical infrastructure is needed to provide proper data management, change tracking, merging of parallel versions, integration with legacy software, reuse of test vectors, linking to requirements and enforcing modeling style guidelines. Examples of successful workflows, tool choices and best practices are described. The importance of the human factor is also acknowledged.
机译:自动代码是汽车和航空航天行业的既定技术,也采用了商用车嵌入式软件开发。代码生成将基于模型的算法设计的生产率提升到过程 - 生产软件开发的下一阶段。该技术已经成熟,它满足大多数技术和可用性要求,代码生成的成功取决于可明智的部署,有效的工作实践和所涉及的所有用户的接受。已经证明,基于模型的软件开发和代码生成,特别可以缩短开发周期,同时避免通过多种算法迭代维持软件质量。但是,这些收益只有仔细组合工具和方法。本文讨论了基于模型的软件开发的共同组织和技术挑战的解决方案。这些包括用于在算法和软件开发,供应商和OEM,公司及其外国子公司之间断开连接的工作流程。除了组织规划外,还需要技术基础设施来提供适当的数据管理,更改跟踪,并行版本的合并,与传统软件集成,重用测试向量,链接到要求和强制建模样式指南。描述了成功的工作流,工具选择和最佳实践的示例。人为因素的重要性也得到了承认。

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