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Unified Modelling Language (UML) is essentially a de‐facto standard for software modeling and supported with many modeling tools. In this study, 58 UML tools have been analysed for modelling viewpoints, analysis, transformation & export, collaboration, tool integration, scripting, project management, and knowledge management. The analysis results reveal important findings: (i) 11 UML tools support multiple viewpoints, (ii) 17 tools support large‐viewpoint management, (iii) Umple and Reactive Blocks support formal verification, (iv) 9 tools support the simulation of activity diagrams, (v) while 14 tools check pre‐defined well‐formedness rules, 8 of them support user‐defined rules, (vi) 16 tools support scripting, (vii) 29 tools support code‐generation and 18 of them support round‐trip engineering, (viii) Java is the top popular language, (ix) 38 tools export UML models as image, 32 tools export as HTML, and 32 tools export as XML/XMI, (x) 17 tools enable versioning and 13 of them support multi‐user access, (xi) 15 tools support the plug‐in extensions and 12 tools support the IDE integration, (xii) 6 tools support project management, and (xiii) while most tools provide user‐manuals, interactive guidance is rarely supported. The results will be helpful for practitioners in choosing the right tool(s) and the tool developers in determining the weaknesses/strengths. [ABSTRACT FROM AUTHOR]

Manufacturing variability is an increasingly significant problem. Silicon devices that are designed to be identical will display widely ranging characteristics after manufacture. Power use, supported clock frequencies and lifespan may all vary considerably. This is of particular concern for embedded systems because of their extensive use of complex system‐on‐chip (SoC)‐based architectures. If this variability is not tolerated by the software, then manufacturing yields are reduced and devices are not used efficiently. This study discusses a novel approach to the integration of variability‐mitigation techniques that uses model‐driven engineering to explicitly consider variability as part of the development process. Developers can build systems that are much more resilient to variability effects, allowing systems to have higher yields, lower costs and greater reliability. The approach uses code generation and code transformation to simplify design‐space exploration and reduce time‐to‐market. The approach is illustrated with an example of audio processing on a complex multiprocessor SoC with simulated variability, and it is shown to be increasingly effective as system variability becomes more significant. [ABSTRACT FROM AUTHOR]