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.

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.

Requirement engineering (RE) is emerging as an increasingly important discipline for supporting Web application development, as these are designed to satisfy diverse stakeholder needs, additional functional, information, multimedia and usability requirements as compared to traditional software applications. Moreover, when considering innovative e-commerce applications, value-based RE is an extremely relevant methodology which exploits the concept of economic value during the RE activity. In contrast, most of the methodologies proposed for the development of Web applications, primarily focus on the system design, and paying less attention to the RE, and specifically to value-based RE. Focusing this aspect, the paper presents integration of value-based RE models to WebML models using our recently proposed VIP Business Modeling Framework [1]. We also analyze the framework's potential in linking other modeling approaches, and argue about its significant integration potential with various E-R/OO-based, process aware Web modeling approaches.

During the rapid growth of World Wide Web in the recent past, ‘usability’ and ‘navigation’ have emerged as problems of concern to the research community. Large web systems are being made so complex that often the users have to make excessive amount of ‘navigational efforts’ to complete their tasks. We identify certain aspects which aggravate the ‘navigational burden’ of a user. Vital amongst them are, complex navigational structure of web, unfamiliarity of users with the website, and disagreement between the research community and practitioners. This paper is based on the empirical analysis of an experimental data obtained from the prospective students who were invited to interact with the websites of few world class educational institutions from four different countries. The data allows us to determine that despite active research in the area of usability engineering, the users still experience heavy ‘navigational burden’ while performing same task using similar systems. The users do not observe any improvement in their ‘familiarity’ with these web systems even when they interact with more than one such systems which serve similar functions. This leads us to propose a solution with a different approach that is; to resort to ‘Generic User Interface’ as a ‘standard’ for organizations serving similar functions such as universities, hospitals etc. We then present some convincing arguments to support our proposal and answer some of the opponent’s voices, followed by a brief discussion on the future work to be done in this direction.

During the rapid growth of World Wide Web in the recent past, ‘usability’ and ‘navigation’ have emerged as problems of concern to the research community. Large web systems are being made so complex that often the users have to make excessive amount of ‘navigational efforts’ to complete their tasks. This inflicts a heavy ‘navigational burden’ upon the users. Current collection of usability guidelines and principles has on the order of thousand rules to follow but none of them directly and specifically addresses the problem of ‘navigational burden’. Thus there exists no tool/validator that can measure the ‘navigational burden’ a user experiences while performing some task on a website. Some usability and accessibility guidelines do exist which are validated by current tools just by analyzing the HTML pages, CSS (cascading style sheets) and other contents that can be retrieved from a website. However, the implementation of checks for these guidelines often suffers from the problem that no model is available, i.e. no abstract description of certain properties of the web page (or its parts). This way, the validators either fail to find certain usability problems in the pages or it outputs too many general warning messages. For instance, it is straightforward to check given HTML code for the use of a limited number of different font faces, but it is not possible to do this reliably for measuring the ‘navigational burden’ a user experiences, unless a model or a mechanism provides information regarding the aspects that aggravate the ‘navigational burden’. In this paper we identify certain aspects which aggravate the ‘navigational burden’ of users. We then present a mechanism how to measure ‘navigational burden’, followed by a discussion to integrate this mechanism into currently available automated usability validators.