Innovations in Systems and Software Engineering

This paper presents a lightweight analysis approach to access control policies based on description logic ( $${\mathcal{DL}}$$ ). After recalling the model-driven security (MDS) approach for the specification and lightweight analysis of role-based security policies using the SecureUML modeling language, we describe how such policies may be specified in $${\mathcal{DL}}$$ and analyzed using $${\mathcal{DL}}$$ tools. We conclude with a comparison between the MDS and $${\mathcal{DL}}$$ -based approaches to the analysis of role-based access control security policies.

A catalogue holds information about a set of objects, typically classified using terms taken from a given thesaurus, and described with the help of a set of attributes. Matching a pair of catalogues means to find a relationship between the terms of their thesauri and a relationship between their attributes. This paper first introduces a matching approach, based on the notion of similarity, that applies to both thesauri and attribute matching. It then describes matchings based on mutual information and introduces variations that explore certain heuristics. Finally, it discusses experimental results that evaluate the precision of the matchings and that measure the influence of the heuristics.

Discovering an optimal route to the most feasible parking lot has been a matter of apprehension for any driver. Selecting the most optimal route to the most favorable lot aggravates further during peak hours of the day and at congested places. This leads to a considerable wastage of resources specifically time and fuel. This work proposes a Bayesian hierarchical technique for obtaining this optimal route. The route selection is based on conflicting objectives, and hence, the problem belongs to the domain of multi-objective optimization. A probabilistic data-driven method has been used to overcome the inherent problem of weight selection in the popular weighted sum technique. The weights of these conflicting objectives have been refined using a Bayesian hierarchical model based on multinomial and Dirichlet prior. Genetic algorithm has been used to obtain optimal solutions. Simulated data have been used to obtain routes which are in close agreement with real-life situations. Statistical analyses have shown the superiority of the weights obtained using the proposed algorithm based on Bayesian technique over the existing frequentist technique.

The success of the ongoing fourth industrial revolution largely depends on our ways to cope with the novel design challenges arising from a combination of an enormous increase in process and product complexity, as well as the expected autonomy and self-organization of complex and diverse industrial hardware–software installments, often called systems-of-systems. In this paper, we employ the service-oriented architectural paradigm, as materialized in the Eclipse Arrowhead framework, to represent modern systems engineering principles and their open structural principles and, thus, relevance to flexible and adaptive systems. As for adequately capturing the structural aspect, we propose using model-based engineering techniques and, in particular, a SysML-based specialization of systems modeling. The approach is illustrated by a real-life use-case in industrial automation.

Refactoring is the art of improving the internal structure of a program without altering its external behavior, and it is an important task when it comes to software maintainability. While existing studies have focused on the detection of refactoring operations by mining software repositories, little was done to understand how developers document their refactoring activities. Therefore, there is recent trend trying to detect developers documentation of refactoring, by manually analyzing their internal and external software documentation. However, these techniques are limited by their manual process, which hinders their scalability. Hence, in this study, we tackle the detection of refactoring documentation as binary classification problem. We focus on the automatic detection of refactoring activities in commit messages by relying on text-mining, natural language preprocessing, and supervised machine learning techniques. We design our tool to overcome the limitation of the manual process, previously proposed by existing studies, through exploring the transformation of commit messages into features that are used to train various models. For our evaluation, we use and compare five different binary classification algorithms, and we test the effectiveness of these models using an existing dataset of manually curated messages that are known to be documenting refactoring activities in the source code. The experiments are carried out with different data sizes and number of bits. As per our results, the combination of Chi-Squared with Bayes point machine and Fisher score with Bayes point machine could be the most efficient when it comes to automatically identifying refactoring text patterns in commit messages, with an accuracy of 0.96, and an F-score of 0.96.

We propose an approach which combines component SysML models and interface automata in order to assemble components and to verify formally their interoperability. So we propose to verify formally the assembly of components specified with the expressive and semi-formal modeling language, SysML. We specify component-based system architecture with SysML Block Definition Diagram, and the composition links between components with Internal Block Diagrams. Component’s protocols are specified with sequence diagrams, they are necessary to exploit interface automata formalism. Interface automata is a common Input Output (I/O) automata-based formalism intended to specify the signature and the protocol level of the component interfaces. We propose formal specifications for SysML semi-formal models in order to exploit interface automata approach. We also improve the interface automata approach by considering system architecture, specified with SysML, in the verification of components composition.

A model-based system development cycle involves two semantically distinct aspects: the requirements specification and the implementation model. Due to the conceptual and semantic differences between these two major system lifecycle stages, the transition from requirements to implementation is inherently a noncoherent process. Consequently, the system requirements are not faithfully transformed into the working system. This paper introduces an effective solution via an Integrated Modeling Paradigm (IMP) that combines the requirements and implementation domain models into a unified system model that continuously represents the system as it evolves. The IMP was implemented in an Object–Process Methodology (OPM) development environment. This implementation reinforces OPM with the capability to bridge the significant conceptual gap that lies right at the heart of the development process. A user survey has shown that this OPM-based solution is easy to use and can indeed help bridge the information gap, yielding a better match between the required and implemented systems than the currently accepted practice.

Software developers, testers and customers routinely submit issue reports to software issue trackers to record the problems they face in using a software. The issues are then directed to appropriate experts for analysis and fixing. However, submitters often misclassify an improvement request as a bug and vice versa. This costs valuable developer time. Hence automated classification of the submitted reports would be of great practical utility. In this paper, we analyze how machine learning techniques may be used to perform this task. We apply different classification algorithms, namely naive Bayes, linear discriminant analysis, k-nearest neighbors, support vector machine (SVM) with various kernels, decision tree and random forest separately to classify the reports from three open-source projects. We evaluate their performance in terms of F-measure, average accuracy and weighted average F-measure. Our experiments show that random forests perform best, while SVM with certain kernels also achieve high performance.

In the recent years, the manufacturing sector has greatly benefited from the implementation of artificial intelligence. It has become a potential significant area for researchers as well as businesses all over the globe. Needless to say, various terminology, such as Intelligent Manufacturing, Smart Manufacturing, and Innovative Manufacturing, terms are being used by many academicians and researchers which could be depicted as comparable concepts. Despite all the facts stated, very little consideration has been given to such concepts from an evolutionary point of view. To address this knowledge gap, the current study conducts a descriptive and analytical review of the concepts mentioned above in order to examine the intrinsic distinctions among these concepts and to elucidate the connection between them. At the same time, to deal with the complexity, increase information awareness, improving process achievement, as well as obtain competitiveness in the global economy, the contemporary manufacturing sector is investing in infrastructure technology such as the Internet of Things (IoT), data analytics, cloud services, as well as information security. These advancements are quickly allowing a new age of smart factory, i.e., a malware network that intimately getting manufacturing companies with digital businesses in internet. Achieving the maximum potential of malware networks is heavily reliant on the creation of new approaches for information technical breakthroughs on the Internet of Manufacturing Things (IoMT). This paper presents a concept for intelligent technology in manufacturing techniques based on the Industrial Internet of Things (IIoT) design, as well as its future potential areas of research.