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In a model-driven environment aiming at generating implementation code ensuring that extra-functional properties modeled at design level are preserved at execution time, a full round-trip engineering approach is often needed. Target code is meant to be generated from design models through appropriate model transformations, once the code has been generated, quality attributes of the embedded system are evaluated by execution monitoring/analysis tools. Eventually, in order to complete a model-driven round-trip approach, provision of back-annotation of the target code analysis results to modeling level is crucial for evaluating and consequently optimizing the design models for ensuring preservation of analyzed extra-functional aspects. In this work the problem of providing such approach in terms of process and related challenges is described together with a proposed solution. Particular emphasis is put on the description of how both traceability information and code analysis results are formalized in order to enable the desired back-annotating capabilities.

In a model-driven engineering development process that focuses on guaranteeing that extra-functional concerns modeled at design level are preserved at platform execution level, the task of automated code generation must produce artifacts that enable back-annotation activities. In fact when the target platform code has been generated, quality attributes of the system are evaluated by appropriate code execution monitoring/analysis tools and their results back-annotated to the source models to be extensively evaluated. Only at this point the preservation of analysed extra-functional aspects can be either asserted or achieved by re-applying the code generation chain to the source models properly optimized according to the evaluation results. In this work we provide a solution for the problem of automatically generating target platform code from source models focusing on producing code artifacts that facilitate analysis and enable back-annotation activities. Arisen challenges and solutions are described together with completed and planned implementation of the proposed approach.

In order for model-driven engineering to succeed, automated code generation from models through model transformations has to guarantee that extra-functional properties modelled at design level are preserved at code level. A full round-trip engineering approach could be needed in order to evaluate quality attributes of the embedded system by code execution monitoring/analysis tools and then provide backpropagation of the target code analysis results to modelling level. In this way, properties that can only be estimated statically are evaluated against runtime values and this consequently allows to optimize the design models for ensuring preservation of analysed extra-functional aspects. This paper presents an approach to support the whole round-trip process starting from the generation of source code for a target platform, passing through the monitoring of selected system quality attributes at code level, and nishing with the back-propagation of measured values to modelling level. The technique is validated against an industrial casestudy in the telecommunications applicative domain.

As software complexity increases in embedded systems domain, component-based development becomes increasingly attractive. A main challenge in this approach is however to analyze the system's extra-functional properties (such as timing properties, or resource requirements), an important step in a development of embedded systems. Analysis of such properties are computational and time consuming, and often difficult. For this reason reuse of the results of the analysis is as important as the reuse of the component itself, especially in case of modifications of the context in which the component is used. This paper presents concepts and mechanisms that allow to automatically discover whether a property value is still valid when related components evolve: a value context language is proposed to formally define the validity conditions and identify possible threats