Abstract. EMF and GMF are powerful frameworks for implementing tool support for modelling languages in Eclipse. However, with power comes complexity; implementing a graphical editor for a modelling language using EMF and GMF requires developers to hand craft and maintain several low level-interconnected models through a loosely-guided, labour-intensive and error-prone process. In this paper we demonstrate how the application of model transformation techniques can help with taming the complexity of GMF and EMF and deliver significant productivity and quality benefits. In particular we demonstrate EuGENia, a widely-used tool that adopts a single-sourcing approach and advanced model transformation techniques for automatically producing and maintaining the low-level models required by EMF and GMF. We evaluate EuGENia through automated testing and substantial feedback from researchers and practitioners within the Eclipse modelling community.
Abstract. Software Product Line (SPL) Engineering is a development paradigm where core artefacts are developed and subsequently configured into different software products dependent on a particular customer's requirements. In industrial product lines, the scale of the configuration (variability management) can become extremely complex and very difficult to manage. Visualisation is widely used in software engineering and has proven useful to amplify cognition in data intensive applications. Adopting this approach within software product line engineering can help stakeholders in supporting essential work tasks by enhancing their understanding of large and complex product lines. In this paper we present our research into the application of visualisation techniques and cognitive theory to address SPL complexity and to enhance cognition in support of the SPL engineering processes. Specifically we present a 3D visualisation approach to enhance stakeholder cognition and thus support variability management and decision making during feature configuration.
Features implementing the functionality in a software product line (SPL) often interact and depend on each other. It is hard to maintain the consistency between feature dependencies on the model level and the actual implementation over time, resulting in inconsistency during product derivation. We describe our initial results when working with feature dependency implementations and the related inconsistencies in actual code. Our aim is to improve consistency checking during product derivation. We have provided tool support for maintaining consistency between feature dependency implementations on both model and code levels in a product line. The tool chain supports the consistency checking on both the domain engineering and the application levels between actual code and models. We report our experience of managing feature dependency consistency in the context of an existing scientific calculator product line.
Learning how to build software systems using new tools can be a daunting task to anyone new to the job. This is especially true of tools that provide a large number of functionalities and views on the system under development, such as IDES for Model-Driven Development (MDD). Applying Machine Learning (ML) techniques can help in this state of affairs by pointing out to appropriate next actions to rookie or even intermediate developers. AutoFOCUS3 (AF3) is a mature MDD tool we are building in-house and for which we provide regular tutorials to new users. These users come from both the academia (e.g, students/professors) and the industry (e.g. managers/software engineers). Nonetheless, AF3 remains a complex tool and we have found there is a need to speedup the learning curve of the tool for students that attend our tutorials-or alternatively and more importantly for others that simply download the tool and attempt using it without human supervision. In this paper, we describe a machine learning-based recommendation system named MAGNET for aiding beginner and intermediate users of AF3 in learning the tool. We describe how we have gathered data and trained an ML model to suggest new commands, how a recommender system was integrated in the AF3, experiments we have run thus far, and the future directions of our work.
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