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We deal with no-signaling correlations that include Bell-type quantum nonlocality. We consider a logical implementation using a trusted central server with encrypted connections to clients. We show that in this way it is possible to implement two-party no-signaling correlations in an asynchronous manner. While from the point of view of physics our approach can be considered as the computer emulation of the results of measurements on entangled particles, from the software engineering point of view it introduces a primitive in communication protocols that can be capable of coordinating agents without revealing the details of their actions. We present an actual implementation in the form of a Web-based application programming interface (RESTful Web API). We demonstrate the use of the API via the simple implementation of the Clauser–Horne–Shimony–Holt game. [ABSTRACT FROM AUTHOR]

When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

The development of smart and innovative software applications in various disciplines has inspired our lives by providing various cutting-edge technologies spanning from online to smart and efficient systems. The proliferation of innovative internet-enabled tools has transformed the nation into a globalized world where individuals can participate on various platforms, collaborate in activities, communicate on issues, and exchange information safely and consistently. Coordination and cooperation are essential in software development. It gathers all software developers in one space, encouraging them to discuss goals and work rationally to accomplish the project goal. In recent years, continuous software development and deployment have become increasingly common in software engineering. Continuous software engineering (CSE) is a method that involves a variety of strategies to increase the regularity of novel and modified software versions. CSE enables a continuous learning and improvement process through rapid software update iteration by combining continuous integration and delivery. Continuous integration is a method that has arisen in order to remove gaps between development and deployment. Software engineers must handle uncertainty and alter stakeholders' requirements, which is possible through continuous software developmental strategies that manage the overall software cycle and produce high-quality software applications. The proposed study is a systematic review related to continuous software development and deployment and focuses on achieving four aims: (1) To explore the impacts of continuous development on software, (2) to pinpoint various tools used to carry out this process, (3) to highlight the challenges faced in adopting continuous approaches for development and (4) to analyze the phases of continuous software engineering. [ABSTRACT FROM AUTHOR]

Interoperability stands as a critical hurdle in developing and overseeing distributed and collaborative systems. Thus, it becomes imperative to gain a deep comprehension of the primary obstacles hindering interoperability and the essential criteria that systems must satisfy to achieve it. In light of this objective, in the initial phase of this research, we conducted a survey questionnaire involving stakeholders and practitioners engaged in distributed and collaborative systems. This effort resulted in the identification of eight essential interoperability requirements, along with their corresponding challenges. Then, the second part of our study encompassed a critical review of the literature to assess the effectiveness of prevailing conceptual approaches and associated technologies in addressing the identified requirements. This analysis led to the identification of a set of components that promise to deliver the desired interoperability by addressing the requirements identified earlier. These elements subsequently form the foundation for the third part of our study, a reference architecture for interoperability-fostering frameworks that is proposed in this paper. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them, but also by identifying the key elements of a framework facilitating interoperability in Systems of Systems. [ABSTRACT FROM AUTHOR]

In the past, the focus of developing software applications was mainly on collecting, analyzing, and implementing user and business requirements. Nowadays, with the unlimited variety of software applications that serve the same purpose, it has become essential to go beyond user requirements to incorporate their emotions and values to ensure the use of those applications. However, the paucity of addressing the incorporation of human values into software engineering practices, in the literature and in the industry, makes it challenging to understand how to do it. Hence, in this study, we attempted to understand the level of adopting human values in software engineering activities, perceived usefulness, opportunities, and challenges in practice. In addition, we empirically investigated the relationship between the maturity level of the Requirements Engineering (RE) practices and the adoption of human values. To achieve those goals, we designed a survey that was distributed to software industry practitioners; 51 complete responses were received. The results showed that there is a positive relationship between the maturity level of RE and the adoption of human values. Also, most participants agreed that incorporating human values into the software design cycle is important; however, the lack of proven effective techniques and practices to capture and analyze the values are two of the main obstacles to adopting human values in practice. [ABSTRACT FROM AUTHOR]

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives. [ABSTRACT FROM AUTHOR]

Rapid societal development and increased engineering construction have exacerbated the disturbance of the geological environment. The impact of extreme climatic factors has grown, leading to a surge in geological disasters, with landslides emerging as particularly significant. Consequently, fundamental research in geological disaster detection or monitoring necessitates an in-depth study of the physical phenomena accompanying landslides' development, evolution, and occurrence. Exploring the signal characteristics associated with landslides is crucial to indirectly understanding their development and change processes—a scientific question deserving thorough exploration. Despite this research's importance, there is a notable gap in the investigation of the key design and specific implementation of electromagnetic instruments tailored for landslide detection. This gap is particularly pronounced in designing and implementing data acquisition software for electromagnetic instruments. This interdisciplinary research draws on theoretical frameworks from embedded computer science, software engineering, digital signal processing technology, geophysics, and engineering geology. It focuses on developing specialized data acquisition application software for landslide detection or monitoring, contributing to the scientific understanding of landslide development and providing independent intellectual property in the electromagnetic wave signal detection field. [ABSTRACT FROM AUTHOR]

Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems. [ABSTRACT FROM AUTHOR]

This article provides a summary of the current state of the Software & Systems Modeling (SoSyM) journal in 2023. It highlights the publication of new papers and the addition of new editors to the journal. The retirement of two esteemed editors is also mentioned, with gratitude expressed for their contributions. The article includes statistics on the number of papers published, the impact factor of the journal, and the number of submissions and downloads. It announces the recipients of the journal's ten-year most influential paper awards and lists the papers presented at the MODELS conference. The article concludes by expressing appreciation to the reviewers for their contributions. Additionally, a separate document is a message of gratitude and recognition from the software and systems modeling community. The community expresses their appreciation to all the reviewers who have assisted the modeling community and announces the recipients of the Best Reviewers of 2023 award. The document also provides a list of all the reviewers who contributed their expertise to the journal over the past year. The content of the current issue is briefly mentioned, and the community wishes everyone a joyful New Year and encourages readers to explore the journal's archive. [Extracted from the article]

Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future. [ABSTRACT FROM AUTHOR]

Despite potential benefits in Software Engineering, adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received little attention. As a first step towards providing the necessary guidance in modelling, we conduct a systematic literature review to explore the current state of the art. We searched academic literature for guidance on model creation and selected 35 papers for full-text screening through three rounds of selection. We find research on model creation guidance to be fragmented, with inconsistent usage of terminology, and a lack of empirical validation or supporting evidence. We outline the different dimensions commonly used to provide guidance on software and system model creation. Additionally, we provide definitions of the three terms modelling method, style, and guideline as current literature lacks a well-defined distinction between them. These definitions can help distinguishing between important concepts and provide precise modelling guidance. [ABSTRACT FROM AUTHOR]

Business process modeling is a crucial aspect of domains such as Business Process Management and Software Engineering. The availability of various BPM languages in the market makes it challenging for process modelers to select the best-fit BPM language for a specific process modeling task. A decision model is necessary to systematically capture and make scattered knowledge on BPM languages available for reuse by process modelers and academics. This paper presents a decision model for the BPM language selection problem in research projects. The model contains mappings of 72 BPM features to 23 BPM languages. We validated and refined the decision model through 10 expert interviews with domain experts from various organizations. We evaluated the efficiency, validity, and generality of the decision model by conducting four case studies of academic research projects with their original researchers. The results confirmed that the decision model supports process modelers in the selection process by providing more insights into the decision process. Based on the empirical evidence from the case studies and domain expert feedback, we conclude that having the knowledge readily available in the decision model supports academics in making more informed decisions that align with their preferences and prioritized requirements. Furthermore, the captured knowledge provides a comprehensive overview of BPM languages, features, and quality characteristics that other researchers can employ to tackle future research challenges. Our observations indicate that BPMN is a commonly used modeling language for process modeling. Therefore, it is more sensible for academics to explain why they did not select BPMN than to discuss why they chose it for their research project(s). [ABSTRACT FROM AUTHOR]

In software engineering, the planning and budgeting stages of a software project are of great importance to all stakeholders, including project managers as well as clients. The estimated costs and scheduling time needed to develop any software project before and/or during startup form the basis of a project's success. The main objective of soft- ware estimation techniques is to determine the actual effort and/or time required for project development. The use of machine learning methods to address the estimation problem has, in general, proven remarkably successful for many engineering problems. In this study, a fully connected neural network (FCNN) model and a metaheuristic, gray wolf optimizer (GWO), called GWO-FC, is proposed to tackle the software development effort estimation (SEE) problem. The GWO is integrated with FCNN to optimize the FCNN parameters in order to enhance the accuracy of the obtained results by improving the FCNN's ability to explore the parameter search field and avoid falling into local optima. The proposed technique was evaluated utilizing various benchmark SEE datasets. Furthermore, various recent algorithms from the literature were employed to verify the GWO-FC performance. In terms of accuracy, comparative outcomes reveal that the GWO-FC performs better than other methods in most datasets and evaluation criteria. Experimental outcomes reveal the strong potential of the GWO-FC method to achieve reliable estimation results. [ABSTRACT FROM AUTHOR]

Automated software transformations and the process of automated program repair and improvement are an important aspect of modern software engineering practices. In this paper, we describe a system which uses a graph-based deep learning model that can be trained to automatically transform and improve computer programs. By operating on language-agnostic, universal graph-like structures easily extractable from source code files (abstract syntax trees), the deep learning agent learns which transformations should be effectively applied to various structures recognized in the source code in order to improve it. By defining a metric which we want to improve and introducing an optimization task—a reinforcement learning setting, an agent learns to automatically apply a chain of transformations to the program, drastically improving it. While similar program improvement processes exist, they exclusively use exhaustive search algorithms to try all the possible code transformations which is a long process susceptible to local optimum issues. Our solution aims to model and embed structural knowledge about the programs being transformed which greatly helps the agent to choose best possible code transformations to apply. Elements of the approach we present in this paper are further applicable not just to automatic software improvement tasks, but also to other code-related tasks. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

With the recent rise in internet usage, the volume of crucial, private, and confidential data traveling online has increased. Attackers have made attempts to break into the network due to weaknesses in the security mechanisms, gaining access to sensitive data that could disrupt the operation of the systems and jeopardize the confidentiality of the data. Intrusion detection systems, a key component of cybersecurity, were used to defend against these potential threats. Numerous review papers have examined various intrusion detection system (IDS) approaches for networks, many of which focused primarily on the anomaly-based IDS while paying less attention to the signature and hybrid-based approaches. Additionally, a lot of these studies took a non-systematic approach, comparing existing techniques, without taking into account the in-depth analytical synthesis of methodologies and results of the approaches to provide a thorough grasp of state-of-the-art. In order to provide a thorough assessment of the current status of Network IDS, this work provides an in-depth insight into what is attainable in the research of Anomaly, Signature, and Hybrid-Based methods by adhering to the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses and guidelines for Software Engineering Domain. We discovered unexplored study topics and unsolved research issues based on the study's findings. We end by highlighting potential, high impact future research areas in order to improve the IDS model. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for the special section on SEFM 2020 and 2021 in the journal Software & Systems Modeling. The International Conference on Software Engineering and Formal Methods (SEFM) aims to bring together practitioners and researchers to advance the state of the art in formal methods and their application in the software industry. This special section includes selected papers from SEFM 2020 and 2021, which were held virtually during the COVID pandemic. Each article in this section has undergone rigorous peer review and has been revised and extended compared to its conference version. The selected papers cover topics such as embedded software portability and verification, nontermination inference, quantitative modeling and analysis of BDI agents, lazy model checking for recursive state machines, P-stable abstractions of hybrid systems, fairness and guarantees for extended bounded response LTL+P synthesis, counterexample classification, analyzing the impact of human errors on interactive service robotic scenarios, and active model learning of stochastic reactive systems. [Extracted from the article]

This document is a correction notice for an article titled "Detection of hateful twitter users with graph convolutional network model" published in the journal Earth Science Informatics. The correction addresses an error in the affiliation details for one of the authors, Serpil Aslan. The correct affiliation should be "Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey." The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are Anıl Utku, Umit Can, and Serpil Aslan. [Extracted from the article]