Software Engineering Paradigm Research Articles

Advanced discovery mechanisms in model repositories

SummaryAs model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository.

Formal model of multi-agent architecture of a software system based on knowledge interpretation

The use of agents across diverse domains within computer science and artificial intelligence is experiencing a notable surge in response to the imperatives of adaptability, efficiency, and scalability. The subject of this study is the application of formal methods to furnish a framework for knowledge interpretation with a specific focus on the agent-based paradigm in software engineering. This study aims to advance a formal approach to knowledge interpretation by leveraging the agent-based paradigm. The objectives are as follows: 1) to examine the current state of the agent-based paradigm in software engineering; 2) to describe the basic concepts of the knowledge interpretation approach; 3) to study the general structure of the rule extraction task; 4) to develop the reference structure of knowledge interpretation; 5) to develop a multi-agent system architecture; 6) and to discuss the research results. This study employs formal methods, including the use of closed path rules and predicate logic. Specifically, the integration of closed path rules contributes to the extraction and explication of facts from extensive knowledge bases. The obtained results encompass the following: 1) a rule mining approach grounded in closed path rules and tailored for processing extensive datasets; 2) a formalization of relevance that facilitates the scrutiny and automated exclusion of irrelevant fragments from the explanatory framework; and 3) the realization of a multi-agent system predicated on the synergy among five distinct types of agents, dedicated to rule extraction and the interpretation of acquired knowledge. This paper provides an example of the application of the proposed formal tenets, demonstrating their practical context. The conclusion underscores that the agent-based paradigm, with its emphasis on decentralized and autonomous entities, presents an innovative framework for handling the intricacies of knowledge processing. It extends to the retrieval of facts and rules. By distributing functions across multiple agents, the framework offers a dynamic and scalable solution to effectively interpret vast knowledge repositories. This approach is particularly valuable in scenarios where traditional methods may struggle to cope with the volume and complexity of information.

Architectural support for software performance in continuous software engineering: A systematic mapping study

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board. (see [https://www.sciencedirect.com/science/article/pii/S0164121221002168] for an example for where to place the statement and how to format it).

An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage.

Perancangan Sistem Informasi Pelabuhan Labuhan Haji Berbasis Website

Keeping up with technological developments applied to transportation facilities, it is very rare to find sea transportation facilities that can be accessed on a web-based basis, both from departure schedules, information and for booking tickets with one company. This research attempts to design an Android-based mobile application that can solve the problem of ticket ordering and ship departure schedules at the Sinabang Haj port crossing. This application will take advantage of the fast access that the web has that is used to order boat tickets, find out the departure schedules of various ships that will sail around Lintas Labuhan Haji Sinabang. The development method used is the linear sequential model, which is often called the Waterfall Model, which is the most widely used software engineering paradigm. From the research results, it can be concluded that; from the results of making web-based ticket reservations by PT. ASDP Indonesia Ferry (Persero) Labuhan Haji makes it easy to inform ticket reservations and schedules that are ready through the booking system. With the website, the public can access and obtain information and order website-based tickets quickly from their homes, and can choose the desired departure schedule, and The ticket rental application on the Website-based Labuhan Haji Sinabang Information System Design can handle and control and monitor ticket, schedule, ship data, which can avoid delays and in making reports for each transaction activity.

Analysis on Intelligent Agent based Approach for Software Engineering

A broad area of research, known as "Agent-Based Computing", focuses on developing "agent-based" intelligent software using agent-based techniques. However, there is a scarcity of research focusing on providing enough evidence of the superiority of agent-based techniques in creating complex software systems. This article has attempted to provide evidence for why agent-based techniques are superior to traditional methods for creating complex software systems, such as control systems. A case of a distinct agent-based control system (the power transportation management system used by Iber-26 drola) is used to illustrate these broader principles. This line of reasoning allows advocates of complex software engineering paradigms to accurately assert that their method can replicate the essential ideas behind agent-based computing. When broken down to their most basic components, agent-based systems are just computer programmes, and every programme has the same set of computable functionalities. The value of a paradigm lies in the way of thinking and the tools it teaches to programmers. As such, agent-based ideas and approaches are not just an extension of those now accessible within existing paradigms, but also well suited to the development of large, networked systems.

Model-Driven Engineering of Process-Aware Information Systems

Enterprise information systems created with model-driven software engineering methods need to handle not only data but also business processes in an automated way. This paper shows how to engineer process-aware information systems following the model-driven and generative software engineering paradigms. Existing approaches realize either the generation of automated or manual activities but do not employ model-driven engineering of all system aspects through systematic language composition. A generative approach that additionally uses process modeling languages allows developers to evolve generated data-centric information systems into process-aware information systems. To be usable within our generation process, we have developed a textual BPMN version and a corresponding language tooling to check the soundness of the models. We have included these process models into the generation process of an information system together with other domain-specific modeling languages, e.g., for data structures, and generate an extendable, process-aware information system that is open for continuous regeneration and hand-written additions. This approach allows us to lift a generated data-centric information system to a process-aware information system. Agile development enabled through the opportunity to validate assumptions automatically and adapt changes efficiently, enhances the engineering process as well as the generated systems themselves.

Robustness Estimation and Optimisation for Semantic Web Service Composition With Stochastic Service Failures

Service-oriented architecture (SOA) is a widely adopted software engineering paradigm that encourages modular and reusable applications. One popular application of SOA is web service composition, which aims to loosely couple web services to accommodate complex goals not achievable through any individual web service. Many approaches have been proposed to construct composite services with optimized Quality of Service (QoS), assuming that QoS of web services never changes. However, the constructed composite services may not perform well and may not be executable later due to its component services' failure. Therefore, it is important to build composite services that are robust to stochastic service failures. Two challenges of building robust composite services are to efficiently generate service composition with near-optimal quality in a large search space of available services and to accurately measure the robustness of composite services considering all possible failure scenarios. This article proposes a novel two-stage GA-based approach to robust web service composition with an adaptive evolutionary control and an efficient robustness measurement. This approach can generate robust composite service at the design phase, which can cope with stochastic service failures and maintain high quality at the time of execution. We have conducted experiments with benchmark datasets to evaluate the performance of our proposed approach. Our experiments show that our method can produce highly robust composite services, achieving outstanding performance consistently in the event of stochastic service failures, on service repositories with varying sizes.

Perancangan E-Learning Berbasis Web Pada Universitas Katolik Santo Thomas

The e-learning system is absolutely necessary to anticipate the times with the support of information technology where everything is heading to the digital era, both mechanism and content. In its development, the system must be preceded by analyzing the needs of the user (user needs). In accordance with the systems and software engineering paradigm, the needs of these users are of the highest order, and are the basis for the creation and work of developers. This is all to prevent the failure of the implementation of the Wahono system, (2003). E-learning is one way to help realize the quality of good learning education. To find out how much the ability of students to receive lessons during tutoring. In addition, to unite intellectual and spiritual intelligence, it requires a good teaching and learning process system through a learning medium. By utilizing advances in technology, a tutoring information is built that can be accessed through a website.

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

Energy consumption is one of the major issues in today’s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.

A knowledge based System for diagnosis of Varicella (Chickenpox) and Measles diseases in children at kindergarten age based on IOT

In childhood diseases, the varicella (chickenpox) and measles diseases considered an important condition. In this research we have designed and implemented an expert knowledge-based system utilizing IOT for early diagnosis of these diseases for children in kindergarten. The system was developed according to an object-oriented software engineering paradigm using UML tools. Also it developed based specific roles in web domain using IoT environment. Furthermore the system able to send a message contains all details on the infection type and description on the disease to the parents. This system can be used by kindergartens or parents as a domestic application for early diagnosis of the disease through the symptoms that appear on the affected child. The knowledge – based system was implemented using python 3.8. The results observe a significant response for detecting and diagnosis the Varicella (Chickenpox) and Measles diseases.

PrEstoCloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

Readiness model for DevOps implementation in software organizations

AbstractDevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.

A knowledge-intensive adaptive business process management framework

Business process management has been the driving force of optimization and operational efficiency for companies until now, but the digitalization era we have been experiencing requires businesses to be agile and responsive as well. In order to be a part of this digital transformation, delivering new levels of automation-fueled agility through digitalization of BPM itself is required. However, the automation of BPM cannot be achieved by solely focusing on process space and classical planning techniques. It requires a holistic approach that also captures the social aspects of the business environment, such as corporate strategies, organization policies, negotiations, and cooperation. For this purpose, we combine BPM, knowledge-intensive systems and intelligent agent technologies, and yield one consolidated intelligent business process management framework, namely agileBPM, that governs the entire BPM life-cycle. Accordingly, agileBPM proposes a modeling methodology to semantically capture the business interests, enterprise environment and process space in accordance with the agent-oriented software engineering paradigm. The proposed agent-based process execution environment provides cognitive capabilities (such as goal-driven planning, norm compliance, knowledge-driven actions, and dynamic cooperation) on top of the developed business models to support knowledge workers’ multi-criteria decision making tasks. The context awareness and exception handling capabilities of the proposed approach have been presented with experimental studies. Through comparative evaluations, it is shown that agileBPM is the most comprehensive knowledge-intensive process management solution.

Social Implication of OOP

This summary delves into an examination of the social implications stemming from the adoption and integration of Object-Oriented Programming (OOP) standards inside software program improvement practices. As OOP maintains to serve as a foundational paradigm in modern software engineering, its societal ramifications expand past technical spheres, influencing collaboration, accessibility, and relationships in the broader social context. A noteworthy social implication of OOP lies in its effect on collaborative software improvement methodologies. The modular and encapsulated design standards inherent in OOP facilitate cohesive teamwork and code reusability, fostering collaborative environments amongst builders. This collaborative ethos transcends geographical constraints, permitting global groups to seamlessly collaborate on interconnected tasks. Consequently, OOP contributes to reshaping the panorama of international collaboration in the software development domain. Accessibility inside the realm of software program development represents another vital dimension of OOP's social impact. The encapsulation of code into items enhances code clarity and maintainability, fostering a more on hand programming experience. This more desirable accessibility broadens participation, enabling people with various backgrounds and ability ranges to have interaction in software program development. In turn, this inclusivity democratizes get admission to programming understanding and promotes a more various participation inside the introduction of technological solutions. Moreover, the social implications of OOP enlarge to it have an impact on societal relationships and consumer reports. Object-oriented layout principles, with a focus on modularity and encapsulation, facilitate the advent of user-friendly interfaces and interactive packages. This emphasis on consumer-centric design complements the overall consumer experience, shaping how people have interaction with era in diverse aspects in their lives, ranging from private computing to communication and entertainment. The abstraction supplied by OOP principles in addition contributes to the improvement of state-of-the-art software program systems, impacting societal features across numerous industries including healthcare, finance, and training. By promoting modular and scalable architectures, OOP helps the advent of complicated and adaptable systems that cater to the evolving wishes of society, shaping the social panorama of technological improvements. In conclusion, this abstract elucidates the social implications of OOP, emphasizing its influence on collaborative software improvement, accessibility, societal relationships, and person studies. As OOP keeps to conform, know-how and navigating these social implications turn out to be critical for discerning the reciprocal dating among technological paradigms and societal dynamics.

Correctness enhancement: a pervasive software engineering paradigm

Refinement-based correctness-preserving transformations are considered as the gold standard of program transformations. While we do not dispute the merits of transforming programs in a way that preserves their correctness, we argue that the vast majority of software engineering processes are in fact instances of correctness enhancement rather than correctness preservation. In this paper, we discuss mathematics of correctness enhancement, i.e., the process of making a program more correct than it is with respect to a specification; then we highlight in what way and to what extent correctness enhancement pervades the practice of software engineering.

Correctness enhancement: a pervasive software engineering paradigm

Refinement-based correctness-preserving transformations are considered as the gold standard of program transformations. While we do not dispute the merits of transforming programs in a way that preserves their correctness, we argue that the vast majority of software engineering processes are in fact instances of correctness enhancement rather than correctness preservation. In this paper, we discuss mathematics of correctness enhancement, i.e., the process of making a program more correct than it is with respect to a specification; then we highlight in what way and to what extent correctness enhancement pervades the practice of software engineering.

Educational value of Systematic Mapping Studies: A replication study.

Contribution: The goal of this study is to validate the results of a prior study on the educational value of systematic mapping studies (SMS) and evaluate the possibility of generalizing those results to similar contexts. Background: since the inception of the Evidence Based Software Engineering (EBSE) paradigm in 2004, few studies have aimed at evaluating their impact in the academic field, and the educational values that SMS can offer to the students. In 2010, three renowned researchers presented an article on this subject at ICSE'10. The study made interesting contributions. We believe that, after 8 years, a replication of that study can be useful to assess the original findings and to guide future educational interventions. Research Questions: What educational value does a SMS provide? Methodology: a replication of a multi-case holistic study. Findings: This replication confirms all but two of the results from the original work. The differences may be due to slight variations in the context. The experience of undertaking an SMS provided the students with both research skills and a broad understanding of a research area. We encourage other researchers to replicate this study, to gain further insights into the educational value of this method.

FM-CF: A framework for classifying feature model building approaches

Software product line engineering has emerged as a prominent software engineering paradigm, as it comprises a set of core assets sharing functionality and quality attributes. Feature modelling is one of the most frequently used techniques for modelling the variability within a software product line. There are several proposals for building Feature Models which rely on semi-automated or fully automated means. Unfortunately, automatic feature model construction has been addressed from different viewpoints, so it is not easy to know which is the best approach for automating the building of variability models. In fact, there is no clarity regarding common elements, and the main differences that characterise such approaches. Additionally, the wide variety of terms used to refer to the process of building a Feature Model (e.g. synthesis, location, re-engineering, and weaving) means that approaches are varied and very heterogeneous, making them complex to understand and classify. This paper introduces FM-CF, which is a Conceptual experience-based Framework for classifying approaches for the automatic building of Feature Models. The framework considers a set of categories mainly focused on characterising some aspects, such as input sources, methods and techniques, results, and types of evaluation. A literature review of (semi-) automated Feature Model construction was performed to identify approaches for building Feature Models by (semi-)automatic means, and the main terms used by those approaches. Then the completeness of the framework was evaluated by mapping the set of dimensions and their items, and the terms extracted from the literature.The conceptual framework provides guidance to researchers for choosing the appropriate aspects with which to build Feature Models, and helps in the understanding and clarification of the proposed approaches.

Is Fragmentation a Threat to the Success of the Internet of Things?

Internet of Things (IoT) aims to bring connectivity to almost every objects, i.e., things, found in the physical space. It extends connectivity to everyday things, however, such increase in the connectivity creates many prominent challenges. Context: Generally, IoT opens the door for new applications for machine-to-machine and human-to-human communications. The current trend of collaborating, distributed teams through the Internet, mobile communications, and autonomous entities, e.g., robots, is the first phase of the IoT to develop and deliver diverse services and applications. However, such collaborations is threatened by the fragmentation that we witness in the industry nowadays as it brings difficulty to integrate the diverse technologies of the various objects found in IoT systems. Diverse technologies induce interoperability issues while designing and developing various services and applications, hence, limiting the possibility of reusing the data, more specifically, the software (including frameworks, firmware, applications programming interfaces, and user interfaces) as well as of facing issues, like security threats and bugs, when developing new services or applications. Different aspects of handling data collection ranging from discovering smart sensors for data collection, integrating and applying reasoning on them must be available to provide interoperability and flexibility to the diverse objects interacting in the system. However, such approaches are bound to be challenged in future IoT scenarios as they bring substantial performance impairments in settings with the very large number of collaborating devices and technologies. Objective: We raise the awareness of the community about the lack of interoperability among technologies developed for IoT and challenges that their integration poses. We also provide guidelines for researchers and practitioners interested in connecting IoT networks and devices to develop services and applications. Method: We apply the methods advocated by the evidence-based software engineering paradigm. This paradigm and its core tool, the systematic literature review (SLR), were introduced to the software-engineering research community early 2004 to help researchers and industry systematically and objectively gather and aggregate evidences about different topics. In this paper, we conduct an SLR of both IoT interoperability issues and the state-of-practice of IoT technologies in the industry, highlighting the integration challenges related to the IoT that have significantly shifted the landscape of Internet-based collaborative services and applications nowadays. Results: Our SLR identifies a number of studies from journals, conferences, and workshops with the highest quality in the field. This SLR reports different trends, including frameworks and technologies, for the IoT for better comprehension of the paradigm and discusses the integration and interoperability challenges across the different layers of this technology while shedding light on the current IoT state-of-practice. It also discusses some future research directions for the community.