Product line architectures play a vital role in the automotive industry in supporting cross-product development involving several hardware and software variation points for different vehicle variants. The effective resolution of multiple variation points for the generation of valid variants is complex, especially when dealing with both software and hardware components implying timing constraints, simultaneously. EAST-ADL is a well-known domain-specific modelling language supporting cross-product development using different levels of abstraction. Furthermore, it offers timing extensions to perform system and component-level timing verification. In this article, we propose an EAST-ADL-compliant and timing-aware variability resolution approach to generate valid product variants effectively. The method relies on existing EAST-ADL product line architecture for system modelling, where several variation points at different levels of abstraction are identified. We propose a variability resolution algorithm where several configuration decisions, starting from the topmost vehicle level down to the design level, are incor-porated for seamless variability resolution. Furthermore, timing decisions based on analysis and design prototypes are provided to generate variant-specific timing constraints. The approach is validated on the car wiper use case provided by our industrial partner, Volvo, an international original equipment manufacturer in the automotive domain. In the use case, three product variants comprising a full system model with associated timing constraints are generated successfully. The results show the feasibility of the proposed approach and indicate its effectiveness in managing timing-aware product variants.

Outcome-Based Education (OBE) is widely recognized for its goal-oriented approach in education. The key elements of outcome-based education are learning outcomes which are measured at student, course, program, and institution levels. The evaluation of learning outcomes is usually performed manually in isolation which leads to several issues like assessment delays and impaired judgement. Furthermore, integration of OBE techniques with existing Learning Management System (LMS) becomes impracticable. To handle such issues, in this article, we propose a framework comprising Metamodel for streamlining evaluation processes of OBE. Moreover, a set of text-to-model transformations is implemented for the automatic gen-eration of high-level models from traditional documents containing information about students, courses, grades etc. Furthermore, the model-to-text transformations are implemented to generate the target model in JAVA for the assessment of learning outcomes. This facilitates OBE evaluation straightforwardly. The efficacy of the framework is validated through a case study conducted within the Department of Software Engineering at University of AJK. The results are encouraging, and OBE assessment is successfully performed for software engineering courses.

EAST-ADL is a domain-specific modelling language for the design and analysis of vehicular embedded systems. Seamless modelling through multiple concrete syntaxes for the same language, known as blended modelling, offers enhanced modelling flexibility to boost collaboration, lower modelling time, and maximise the productivity of multiple diverse stakeholders involved in the development of complex systems, such as those in the automotive domain. Together with our industrial partner, which is one of the leading contributors to the definition of EAST-ADL and one of its main end-users, we provided prototypical blended modelling features for EAST-ADL. In this article, we report on our language engineering work towards the provision of blended modelling for EAST-ADL to support seamless graphical and textual notations. Notably, for selected portions of the EAST-ADL language (i.e., timing and variability packages), we introduce ad-hoc textual concrete syntaxes to represent the language's abstract syntax in alternative textual notations, preserving the language's semantics. Furthermore, we propose a full-fledged runtime synchronisation mechanism, based on the standard EAXML schema format, to achieve seamless change propagation across the two notations. As EAXML serves as a central synchronisation point, the proposed blended modelling approach is workable with most existing EAST-ADL tools. The feasibility of the proposed approach is demonstrated through a car wiper use case from our industrial partner - Volvo. Results indicate that the proposed blended modelling approach is effective and can be applied to other EAST-ADL packages and supporting tools.

Modern vehicles are extremely complex embedded systems that integrate software and hardware from a large set of contributors. Modeling standards like EAST-ADL have shown promising results to reduce complexity and expedite system development. However, such standards are unable to cope with the growing demands of the automotive industry. A typical example of this phenomenon is autonomous vehicle perception (AVP) where deep learning architectures (DLA) are required for computer vision (CV) tasks like real-time object recognition and detection. However, existing modeling standards in the automotive industry are unable to manage such CV tasks at a higher abstraction level. Consequently, system development is currently accomplished through modeling approaches like EAST-ADL while DLA-based CV features for AVP are implemented in isolation at a lower abstraction level. This significantly compromises productivity due to integration challenges. In this article, we introduce MoDLF - A Model-Driven Deep learning Framework to design deep convolutional neural network (DCNN) architectures for AVP tasks. Particularly, Model Driven Architecture (MDA) is leveraged to propose a metamodel along with a conformant graphical modeling workbench to model DCNNs for CV tasks in AVP at a higher abstraction level. Furthermore, Model-To-Text (M2T) transformations are provided to generate executable code for MATLAB® and Python. The framework is validated via two case studies on benchmark datasets for key AVP tasks. The results prove that MoDLF effectively enables model-driven architectural exploration of deep convnets for AVP system development while supporting integration with renowned existing standards like EAST-ADL.

In Model Driven Software Engineering (MDSE), Action Language for Foundational UML (ALF) is a new standard for specifying the structure and behavior of a system textually. To update/transform existing systems with respect to advance business needs and/or by the change in the dependent technology, this standard can play a vital role in reverse engineering a system for technology change. In this paper, using ALF, we propose a reverse engineering approach for transforming object oriented system. Our work is the first attempt to use ALF in reverse engineering. Using a case study (an ATM system) of significant size developed in C[Formula: see text], we validate the feasibility of our approach. In this paper, to support our approach by a computer application, we created a tool CPP2ALF; this tool converts the C[Formula: see text] code to srcML code by using a third party srcML-tool and then generates the ALF code by using the generated srcML code.