Mathias Fritzsche

MDE Adoption in Industry: Challenges and Success Criteria

Model-Driven Engineering has been promoted for some time as the solution for the main problem software industry is facing, i.e. complexity of software development, by raising the abstraction level and introducing more automation in the process. The promises are many; among them improved software quality by increased traceability between artifacts, early defect detection, reducing manual and error-prone work and including knowledge in generators. However, in our opinion MDE is still in the early adoption phase and to be successfully adopted by industry, it must prove its superiority over other development paradigms and be supported by a rich ecosystem of stable, compatible and standardized tools. It should also not introduce more complexity than it removes. The subject of this paper is the challenges in MDE adoption from our experience of using MDE in real and research projects, where MDE has potential for success and what the key success criteria are.

Where does model-driven engineering help? Experiences from three industrial cases

There have been few experience reports from industry on how Model-Driven Engineering (MDE) is applied and what the benefits are. This paper summarizes the experiences of three large industrial participants in a European research project with the objective of developing techniques and tools for applying MDE on the development of large and complex software systems. The participants had varying degrees of previous experience with MDE. They found MDE to be particularly useful for providing abstractions of complex systems at multiple levels or from different viewpoints, for the development of domain-specific models that facilitate communication with non-technical experts, for the purposes of simulation and testing, and for the consumption of models for analysis, such as performance-related decision support and system design improvements. From the industrial perspective, a methodology is considered to be useful and cost-efficient if it is possible to reuse solutions in multiple projects or products. However, developing reusable solutions required extra effort and sometimes had a negative impact on the performance of tools. While the companies identified several benefits of MDE, merging different tools with one another in a seamless development environment required several transformations, which increased the required implementation effort and complexity. Additionally, user-friendliness of tools and the provision of features for managing models of complex systems were identified as crucial for a wider industrial adoption of MDE.

Putting Performance Engineering into Model-Driven Engineering: Model-Driven Performance Engineering

Late identification of performance problems can lead to significant additional development costs. Hence, it is necessary to address performance in several development phases by performing a performance engineering process. We show that Model-Driven Engineering (MDE) specifics can be utilised for performance engineering. Therefore, we propose a process combining MDE and performance engineering called Model-Driven Performance Engineering (MDPE). Additionally we present our first experiences in application of MDPE concepts.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Towards Utilizing Model-Driven Engineering of Composite Applications for Business Performance Analysis

Composite Applications on top of SAPs implementation of SOA (Enterprise SOA) enable the extension of already existing business logic. In this paper we show, based on a case study, how Model-Driven Engineering concepts are applied in the development of such Composite Applications. Our Case Study extends a back-end business process which is required for the specific needs of a demo company selling wine. We use this to describe how the business centric models specifying the modified business behaviour of our case study can be utilized for business performance analysis where most of the actions are performed by humans. In particular, we apply a refined version of Model-Driven Performance Engineering that we proposed in our previous work and motivate which business domain specifics have to be taken into account for business performance analysis. We additionally motivate the need for performance related decision support for domain experts, who generally lack performance related skills. Such a support should offer visual guidance about what should be changed in the design and resource mapping to get improved results with respect to modification constraints and performance objectives, or objectives for time.

Applying Megamodelling to Model Driven Performance Engineering

Model Driven Engineering (MDE) has to deal with an increasing number of interrelated modelling artifacts. The Model Driven Performance Engineering (MDPE) process is one concrete illustration of such a situation. This process applies MDE within the context of performance engineering in order to support domain experts, who generally lack the necessary performance expertise. In this paper, we demonstrate the use of megamodelling to manage the numerous artifacts involved in MDPE. Megamodelling enables the explicit modelling of the metadata on MDE artifacts, including possible relationships between those artifacts. Appropriate tool support enables different stakeholders to exploit this additional information. Applying the megamodelling to MDPE pointed out the need for an extension of the existing approach. Thus, the result of the paper is twofold: first, an extension of megamodelling is proposed, second the benefits of the approach are shown on the MDPE use case. We claim that the extension is not solely useful for the latter case, but has a more generic applicability.

Systematic Usage of Embedded Modelling Languages in Automated Model Transformation Chains

Annotation of programs using embedded Domain-Specific Languages (embedded DSLs), such as the program annotation facility for the Java programming language, is a well-known practice in computer science. In this paper we argue for and propose a specialized approach for the usage of embedded Domain-Specific Modelling Languages (embedded DSMLs) in Model-Driven Engineering (MDE) processes that in particular supports automated many-step model transformation chains. It can happen that information defined at some point, using an embedded DSML, is not required in the next immediate transformation step, but in a later one. We propose a new approach of model annotation enabling flexible many-step transformation chains. The approach utilizes a combination of embedded DSMLs, trace models and a megamodel. We demonstrate our approach based on an example MDE process and an industrial case study.

Towards Performance Related Decision Support for Model Driven Engineering of Enterprise SOA Applications

Model driven performance engineering (MDPE) enables early performance feedback in a MDE process, in order to avoid late identification of performance problems which could cause significant additional development costs. In our past work we argued that a synchronization mechanism between development and performance analysis models is required to adequately integrate analysis results into the development process enabling performance related decision support. In this paper we present a solution for this requirement. We present a new multi-view based approach and its implementation enabling systematic performance related decision support. We currently apply our research on the model driven engineering of process orchestrations on top of SAPs enterprise service oriented architecture (Enterprise SOA).

Model transformation chains and model management for end-to-end performance decision support

The prototypical Model-Driven Performance Engineering (MDPE) Workbench from SAP Research permits multi-paradigm decision support for performance related questions in terms of what-if simulations, sensitivity analyses and optimizations. This support is beneficial if business analysts are designing new processes, modifying existing ones or optimizing processes. The functionality is provided as an extension of existing Process Modelling Tools, such as the tools employed by process environments like the jCOM! or the SAP NetWeaver Business Process Management (BPM) Suites as well as classical enterprise software like SAP Business Suite or Open ERP. By evaluating our workbench for real world cases we experienced that business processes may span different environments, each employing different Process Modelling Tools. The presence of heterogeneous tools influences the end-to-end performance of the overall process. Thus, the MDPE Workbench essentially needs to take the complete process into account. In this paper, a model transformation chain and a model management architecture is explained to enable such functionality. This architecture combines results from our previous publications, outlines these results in more detail and explains them in the context of end-to-end processes. Furthermore, the work is evaluated with an industrial business process which spans three different Process Modelling Tools.

ALI: An Extensible Architecture Description Language for Industrial Applications

While architecture description languages (ADLs) have gained wide acceptance in the research community as a means of describing system designs, the uptake in industry has been slower than might have been expected. A contributory cause may be the perceived lack of flexibility and, as yet, the limited tool support. This paper describes ALI, a new ADL that aims to address these deficiencies by providing a rich, extensible and flexible syntax for describing component interface types and the use of patterns and meta-information. These enhanced capabilities are intended to encourage more widespread industrial usage.