Wasif Gilani

An empirical study of the state of the practice and acceptance of model-driven engineering in four industrial cases

Model-Driven Engineering (MDE) has been promoted for many years as a means for handling the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks. However, there is little empirical evidence of the acceptance of MDE in industry which is the subject of this paper. The goal of this empirical study was to investigate the state of the practice of applying MDE and factors considered as important for its adoption. The subjects were developers of four large companies participating in a research project. The collected data came from multiple sources and covered the results of tool evaluations, interviews, and a survey. Among the factors, we found perceived usefulness, ease of use and the maturity of the tools to be important determinants for the adoption of MDE. We also discuss challenges with adopting MDE and present suggestions on how to succeed with the adoption process.

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.

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.

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.

Conformance checking for BPMN-based process models

Measuring how well business process models conform to the execution of the process in reality is an important topic with many applications. While current conformance checking approaches are tailored to formal models such as Petri nets they lack support for domain-specific standards such as BPMN. In this paper we present two approaches for directly measuring the conformance of business process models based on BPMN elements and event logs. We define methods for extracting properties from such models that enable an easy comparison to event logs on a local level (i.e. for individual parts of the process and individual events). Furthermore, we present a method for replaying whole event logs on such models, allowing for a global conformance measure (i.e. on trace level). By utilising the previously extracted properties, we eliminate the need for expensive state-space exploration.

Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs

Process Discovery techniques help a business analyst to understand the actual processes deployed in an organization, i.e. based on a log of events, the actual activity workflow is discovered. In most cases their results conform to general purpose representations like Petri nets or Causal nets which are preferred by academic scholars but difficult to comprehend for business analysts. In this paper we propose an algorithm that follows a top-down approach to directly mine a process model which consists of common BP-domain constructs and represents the main behaviour of the process. The algorithm is designed so it can deal with noise and not-supported behaviour. This is achieved by letting the different supported constructs compete with each other for the most suitable solution from top to bottom using ”soft” constraints and behaviour approximations. The key parts of the algorithm are formally described and evaluation results are presented and discussed.

Event-Driven Process-Centric Performance Prediction via Simulation

Today’s fast, competitive and extremely volatile markets exert a great deal of pressure on businesses to react quicker against the changes, and sometimes even before the changes actually happen. A late action can potentially result in a legal compliance failure or violation of service level agreements (SLA’s). A business analyst needs to be notified before these failures and violations occur. This paper proposes an approach that enables real-time and process-centric decision support in the form of performance prediction as an application of Event-Driven Business Process Management (EDBPM). The ability of simulations to produce future-events, which are of the same type like the live-events generated by the really executed business process, is utilised. Live-events and simulated future-events can therefore be treated by a Complex-Event Processing (CEP) engine in the same way and parameters representing the historic, current, and future performance of the business process can be easily computed.

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).