Our paper entitled “An Automatic Generation of Android Application for WooCommerce” was accepted in 9th International Conference on Computer and Knowledge Engineering (ICCKE 2019).
Dynamic verification and validation (V&V) techniques are used to verify and validate the behavior of software systems early in the development process. In the context of model-driven engineering, such behaviors are usually defined using executable domain-specific modeling languages (xDSML). Many V&V techniques rely on execution traces to represent and analyze the behavior of executable models. Traces, however, tend to be overwhelmingly large, hindering effective and efficient analysis of their content. While there exist several trace metamodels to represent execution traces, most of them suffer from scalability problems. In this paper, we present a generic compact trace representation format called generic compact trace metamodel (CTM) that enables the construction and manipulation of compact execution traces of executable models. CTM is generic in the sense that it supports a wide range of xDSMLs. We evaluate CTM on traces obtained from real-world fUML models. Compared to existing trace metamodels, the results show a significant reduction in memory and disk consumption. Moreover, CTM offers a common structure with the aim to facilitate interoperability between existing trace analysis tools.
A domain-specific metamodeling language (DSM2L) enables language engineers to define a family of similar metamodel-based languages. In recent years, several DSM2Ls have been developed for various domains, e.g., traceability, variability management, process modeling, and metamodeling feature models. However, there is no consensus on an engineering approach for constructing a DSM2L. To address this problem, we consider a DSM2L as a software product line (SPL), in which the software products are the family of languages. Based on this assumption, we propose a roadmap to develop a DSM2L using an SPL engineering framework. To investigate the pros and cons of the roadmap, the MoDEBiTE metamodeling language is engineered in the domain of bidirectional transformations. In order to validate the proposal of engineering a DSM2L, an experiment with six transformation cases is performed on MoDEBiTE. The results of the experiment show the applicability, usefulness, and validity of MoDEBiTE, demonstrating the validity of the proposal.
Model-Driven Engineering is a development paradigm that uses models instead of code as primary development artifacts. In this paper, we focus on executable models, which are used to abstract the behavior of systems for the purpose of verifying and validating (V&V) a system’s properties. Model execution tracing (i.e., obtaining and analyzing traces of model executions) is an important enabler for many V&V techniques including testing, model checking, and system comprehension. This may explain the increase in the number of proposed approaches on tracing model executions in the last years. Despite the increased attention, there is currently no clear understanding of the state of the art in this research field, making it difficult to identify research gaps and opportunities. The goal of this paper is to survey and classify existing work on model execution tracing, and identify promising future research directions. To achieve this, we conducted a systematic mapping study where we examined 64 primary studies out of 645 found publications. We found that the majority of model execution tracing approaches has been developed for the purpose of testing and dynamic analysis. Furthermore, most approaches target specific modeling languages and rely on custom trace representation formats, hindering the synergy among tools and exchange of data. This study also revealed that most existing approaches were not validated empirically, raising doubts as to their effectiveness in practice. Our results suggest that future research should focus on developing a common trace exchange format for traces, designing scalable trace representations, as well as conducting empirical studies to assess the effectiveness of proposed approaches.
This paper presents a solution for the Quality-based Software-Selection and Hardware-Mapping problem using the ACO algorithm. ACO is one of the most successful swarm intelligence algorithms for solving discrete optimization problems. The evaluation results show that the proposed approach generates correct results for all evaluated test cases.
Also, better results in terms of performance and scalability are given in comparison with the ILP and EMFeR approaches.