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Design patterns are an effective technique for software development. However, lacking suitable documents often reduces traceability, and advantages remain unclear in source lines of code (SLOC). Design patterns are proven, reliable solutions to several frequent problems in object‐oriented software design. Detecting design patterns in a software system contributes to maintainability and reengineering and facilitates understanding codes. Furthermore, different design pattern implementations impede the detection of code pattern instances. A design pattern is a set of roles played by the code classes. Roles are the main details and determinants in patterns. A pattern can be detected in its defined role as it is an instance of a role. It is also of great importance in system documentation. The proposed method exploits blockchain‐based approaches for pattern mining and isolating design patterns in order to identify intercode relationships. The results demonstrate that the blockchain is effective and efficient in extracting the connections of features from source codes and adapting to design patterns with precision 97.2%. [ABSTRACT FROM AUTHOR]

Accidental vessel collisions pose a threat to the safety of train operations on navigable bridges. The CRH2 high-speed train, operating on a four-span continuous girder bridge, is selected as a research prototype to study the effects of vessel impact loads on the responses of the train-track-bridge (TTB) system and train running safety. The TTB model subjected to vessel collision is developed using the finite element software ANSYS and the multi-body system software SIMPACK. The influences of load intensity and train speed on the critical indicators of train running safety are discussed. Safety thresholds based on train running speed are proposed through comprehensive parametric analysis. The results indicate that the impact load has a significant effect on the dynamic responses of both bridge and track structures. The interaction between piles and soil (PSI) has a significant impact on the dynamic responses of the TTB system. The track structure undergoes significant overall deformation under the vessel impact load. The train running safety indices ascend with the increase in impact load intensity and train speed. As the impact load intensity increases from 0 to 24.25 MN, the speed limit for ensuring train running safety decreases from 350 km/h to 250 km/h. [ABSTRACT FROM AUTHOR]

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. We also consider how such a prover can be used to solve a reasoning task without much mental labor. The development is extended with a formalized proof system for writing machine-checked sequent calculus proofs. We consider how this can be used to teach computer science students about logic, automated reasoning and proof assistants. [ABSTRACT FROM AUTHOR]

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality. [ABSTRACT FROM AUTHOR]

Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems.EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher).Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research. [ABSTRACT FROM AUTHOR]

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar. [ABSTRACT FROM AUTHOR]

Software fault prediction (SFP) is a crucial aspect of software engineering, aiding in the early identification of potential defects. This proactive approach significantly contributes to enhancing software quality and reliability. However, a common challenge in SFP is class imbalance (CI). Ensemble learning (EL) is a powerful strategy for refining SFP models in object‐oriented systems with imbalanced data and improving sensitivity to minority classes. This study aimed to improve the effectiveness of ensemble classes in SFP within object‐oriented systems, tackling the challenges associated with imbalanced data. It focuses on enhancing the performance of three ensemble classifiers, BalancedBagging, RUSBoost, and EasyEnsemble, explicitly designed for imbalanced datasets. In Enhanced_BalancedBagging (E_BB) and ROSBoost, random undersampling (RUS) is substituted with random oversampling (ROS). Meanwhile, Enhanced_EasyEnsemble (E_EE) replaces RUS with ROS and AdaBoost with XGBoost. The experimental results demonstrate the superior performance of E_BB, ROSBoost, and E_EE over their base models, achieving the highest F‐measure, balanced accuracy, and AUC. Statistical tests, such as the Wilcoxon signed‐rank test, provide robust support for the enhanced models, highlighting their practical significance through substantial improvements in F‐measure and AUC, as indicated by low negative rank sums and large effect sizes. [ABSTRACT FROM AUTHOR]

Chatter is a self-induced vibration that results in poor surface quality in the high-speed camshaft grinding process. Accurate and timely chatter prediction is very important for chatter suppression. Consequently, an online chatter prediction method and chatter software system for intelligent manufacturing are urgently required. This paper proposes a novel strategy to predict the chatter for the high-speed grinding process of the camshaft. In this method, the vibration signals acquired by the sensor are decomposed by variational mode decomposition. The backpropagation neural networks model is used to predict grinding chatter. High-speed grinding experiments with different conditions were performed to evaluate the effectiveness of the proposed method. Finally, the vibration state prediction system is developed promptly for chatter prediction. The results show that the proposed method is suitable for chatter prediction in the high-speed grinding process of camshaft, and the correct rate of chatter prediction is 96.4%. [ABSTRACT FROM AUTHOR]

For highly reliable software systems, it is expensive, time consuming, or even infeasible to perform reliability testing via a conventional software reliability demonstration testing (SRDT) plan. Moreover, in the traditional SRDT approach, the various characteristics of the software system or test sets are not considered when making the testing schemes. Some studies have focused on the effect of software testability on SRDT, but only limited situations were discussed, and many theoretical and practical problems have been left unresolved. In this paper, an extended study on the quantitative relation between test effectiveness (TE) and test effort for SRDT is proposed. Theoretical derivation is put forward by performing statistical analysis for the test suite according to TE. The combinations of all the cases of zero-failure and revealed nonzero failure, as well as discrete-type software and continuous-type software, are studied with the corresponding failure probability models constructed. That is, zero-failure and nonzero failure, as well as discrete-type software and continuous-type software, respectively, constitute the symmetry and asymmetry of SRDT. Finally, we illustrated all the models and performed applications on the Siemens program suite. The experimental results show that within the same limitation of requirements and confidence levels, this approach can effectively reduce the number of test cases and the test duration, i.e., accelerate the test process and improve the efficiency of the SRDT. [ABSTRACT FROM AUTHOR]

The threat of side‐channel attacks poses a significant risk to the security of cryptographic algorithms. To counter this threat, we have designed an AES system capable of defending against such attacks, supporting AES‐128, AES‐192, and AES‐256 encryption standards. In our system, the CPU oversees the AES hardware via the AHB bus and employs true random number generation to provide secure random inputs for computations. The hardware implementation of the AES S‐box utilizes complex domain inversion techniques, while intermediate data is shielded using full‐time masking. Furthermore, the system incorporates double‐path error detection mechanisms to thwart fault propagation. Our results demonstrate that the system effectively conceals key power information, providing robust resistance against CPA attacks, and is capable of detecting injected faults, thereby mitigating fault‐based attacks. [ABSTRACT FROM AUTHOR]

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any "extensions library" requirement from Agents.jl, which is paramount in many other tools. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement. [ABSTRACT FROM AUTHOR]

Due to VDES's higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources. [ABSTRACT FROM AUTHOR]

Electronic Accounting (E-accounting) is considered a new concept in the field of accounting. E-accounting replaces paper with electronic sources for accounting records and documentation. It retains a lot of time, zeal, paper and funds for any type of organisation. New software for recording accounting transactions is released every day which is giving technological improvements. E-accounting is characterised as the use of Internet technologies and online applications for business accounting. Additionally, it helps organisations maintain their accounting software and financial data in a secure manner while granting authorised users real-time access regardless of location or computer platform. The objective of the paper is to understand the basic concept of E-accounting, the characteristics of E-accounting, its benefits and drawbacks, and its conclusion. The purpose of this study is to shed light on the implications of E-accounting in modern companies. The review of related literature that was used to develop the study’s theoretical framework served as the secondary source of data for this report. According to surveys, many businesses fail not just due to low-quality products, a lack of qualified employees, or managerial problems, but also because of bad accounting procedures. It has been determined that the company needs to switch to current accounting procedures from outdated ones. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67–77% vs. 80–86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions In 2021, a Technical Meeting was hosted by the International Atomic Energy Agency (IAEA) where it was recommended that a standardized method for assessing the accuracy of film dose calculations should be established.To design an audit that evaluates the accuracy of film dosimetry processes. To propose a framework for identifying out‐of‐tolerance results and to perform an international pilot study to test the audit design.Six members of an international Dosimetry Audit Network (DAN) developed an audit for radiochromic film dosimetry. A single host center provided the materials to each participating DAN member to conduct the audits. Materials included: (1) a set of two irradiated audit films (10 Sq: 10 cm × 10 cm, 15 Sq: 15 cm × 15 cm), (2) a reference calibration film set, and (3) a blank sheet of film. The participants were blinded to the dose and tasked with producing dose maps using their standard film dosimetry process. Average Region‐Of‐Interest (ROI: 2 cm × 2 cm) dose was measured from the dose maps and compared to the known dose. In the audit, all participants used their local scanning and software protocols. Film calibration was performed in two distinct ways: (1) using a calibration film set which was provided by the host center and (2) using a calibration film set which was locally irradiated. Several variations of the audit were also performed to examine how scanning and software processing can affect film dosimetry results. In the first variation of the audit (VariantA), a set of film scans was processed using five different software solutions. In the second variation of the audit (VariantB), all films were scanned on the same scanner and processed using two in‐house software solutions.Taking one film scan from each participant, the standard deviations (1σ) (SD) in the dose returned from the host calibration and returned from the local calibration were ±7.2% and ±6.5% respectively, with variations from −12.4% to 12.9% of the known dose. The larger dose variations in the data set were attributed to the corrections applied for variations in scanner brightness during processing and incorrectly assigned calibration doses. When the raw image data set was processed by an expert user of each software solution (VariantA) the SDs were ±2.7% and ±3.7% for in‐house and vendor‐based software, respectively. When the films were scanned on a single scanner and processed with the two in‐house software solutions (VariantB) the results had a SD of ±2.3%.An audit has been designed and tested for radiotherapy film dosimetry at an international level. A framework for diagnosing issues within a film dosimetry process has been proposed that could be used to audit centers that use film as a dosimeter. Incorporating quality assurance throughout the film process is important in obtaining accurate and consistent film dosimetry. A better understanding of vendor‐based software systems is necessary for users to process accurate and consistent film dosimetry. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

Code smells are specific patterns or characteristics in software code that indicate potential design or implementation problems. Identifying code smells has gained significant attention in software engineering. It is essential to address code smells to maintain high-quality software systems. Machine learning (ML) models, such as Long Short-Term Memory (LSTM), have been to detect code smells automatically based on source code features. However, the imbalanced distribution of code smells within software projects poses a challenge to the accuracy of these models. This study explores the role of data balancing methods in optimizing the accuracy of the LSTM model for code smell detection. We investigate different techniques for addressing the class imbalance problem, including random oversampling and synthetic minority oversampling techniques (SMOTE). We evaluate the performance of the LSTM model with and without data balancing methods using accuracy, precision, recall, f-measure, Matthew's correlation coefficient (MCC), and the area under a receiver operating characteristic curve (AUC). Our experimental results, conducted on four code smell datasets (God class, data class, feature envy, and long method) extracted from 74 open-source systems, demonstrate the effectiveness of data balancing methods in improving the accuracy of the LSTM model for code smell detection. The results indicate that the use of data balancing methods had a positive effect on the predictive accuracy of the LSTM model. In addition, we compared our proposed method with state-of-the-art code smell detection approaches. The findings from the comparison indicate that our proposed method performs notably better than existing state-ofthe-art approaches across the majority of datasets. [ABSTRACT FROM AUTHOR]

This paper presents the novel design and analysis of a 25 W broadband Class-AB GaN HEMT-based power amplifier working over 0.9–1.7 GHz. A large signal model of the Wolfspeed-make GaN HEMT CG2H40025 has been used in this design. The comprehensive design process has been carried out with the help of the Advanced Design System software by Keysight. Instead of conjugately matching the load and source impedance obtained from load-pull analysis of a nonlinear model of CG2H40025, the value of load and source impedance has been tuned with the help of a novel design approach of an ideal matching design approach. Thus, to achieve an output power of more than 25 W and a flat large signal gain of 15.425 ± 0.375 dB, a novel design approach has been employed to create input and output matching networks, maintaining a lower Q value results in broad bandwidth and for determining the load and source impedances. The circuit has been fabricated on Roger's substrate RO4350B having a dielectric constant of 3.66 and a loss tangent of 0.0031. The unique design approach based on electromagnetic simulation of the circuit at each design step yielded experimental results that agreed very well with the simulated results. The fabricated power amplifier achieves a power-added efficiency of over 54.78% and a drain efficiency of over 55.45% throughout the entire frequency range. It also delivers a maximum output power of 32.18 W at 1.3 GHz. This power amplifier is developed for L-band Foliage Penetrating Radar. [ABSTRACT FROM AUTHOR]

Fluid-flow approximation is an approach to modelling and evaluating the performance of vast computer networks. Due to varying traffic and performance of transmission protocols reacting to traffic overloads, computer networks are in a permanent transient state. The fluid-flow method main advantage is its ability to analyse these transient states. The article reviews and organises several versions of this approach, indicating a few errors. The main reason for these errors is confusion or lack of distinction between the two versions of the Internet Protocol -- when the queue of packets at a node is too long, they may be destroyed or only marked as redundant. The paper compares and evaluates these fluid-flow approximation models with mild and aggressive settings of RED parameters. The authors build a software system with hitherto unprecedented capabilities regarding the size of the networks to be analysed and with innovative way of organising the calculations. The paper shows how large differences imprecise assumptions can introduce in quantitative results. [ABSTRACT FROM AUTHOR]

Embedded computer systems refer to specialized computer systems that integrate operating systems and functional software into computer hardware systems, are application centric, based on computer technology, and have tailorable software and hardware. They have strict requirements for real-time performance, reliability, cost, volume, and power consumption. The performance and security evaluation of embedded systems is an important aspect of the development, development, and integration process of embedded systems, which can provide various quantitative analysis basis for the development and technical transformation of embedded products. The performance and security evaluation of embedded systems is a very complex issue, and evaluators may have different understandings of the problem due to their different starting points and observation perspectives. The embedded computers' performance evaluation is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted geometric Bonferroni mean (GWGBM) operator is built for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number generalized power geometric BM (SVNNGPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNGPGBM operator and power geometric (PG) operator. Finally, an example about embedded computers' performance evaluation and some comparative analysis were given to demonstrate the SVNNGPGBM method. [ABSTRACT FROM AUTHOR]

As society's reliance on software systems escalates over time, so too does the cost of failure of these systems. Meanwhile, the complexity of software systems, as well as of their designs, is also ever‐increasing, influenced by the proliferation of new tools and technologies to address intended societal needs. The traditional response to this complexity in software engineering and software architecture has been to apply rationalistic approaches to software design through methods and tools for capturing design rationale and evaluating various design options against a set of criteria. However, research from other fields demonstrates that intuition may also hold benefits for making complex design decisions. All humans, including software designers, use intuition and rationality in varying combinations. The aim of this article is to provide a comprehensive overview of what is known and unknown from existing research regarding the use and performance consequences of using intuition and rationality in software design decision‐making. To this end, a systematic literature review has been conducted, with an initial sample of 3909 unique publications and a final sample of 26 primary studies. We present an overview of existing research, based on the literature concerning intuition and rationality use in software design decision‐making and propose a research agenda with 14 questions that should encourage researchers to fill identified research gaps. This research agenda emphasizes what should be investigated to be able to develop support for the application of the two cognitive processes in software design decision‐making. [ABSTRACT FROM AUTHOR]

Modeling and simulation have been used to study tsunamis for several decades. We created a review to identify the software and methods used in the last decade of tsunami research. The systematic review was based on the PRISMA methodology. We analyzed 105 articles and identified 27 unique software and 45 unique methods. The reviewed articles can be divided into the following basic categories: exploring historical tsunamis based on tsunami deposits, modeling tsunamis in 3D space, identifying tsunami impacts, exploring relevant variables for tsunamis, creating tsunami impact maps, and comparing simulation results with real data. Based on the outcomes of this review, this study suggests and exemplifies the possibilities of system dynamics as a unifying methodology that can integrate modeling and simulation of most identified phenomena. Hence, it contributes to the development of tsunami modeling as a scientific discipline that can offer new ideas and highlight limitations or a building block for further research in the field of natural disasters. [ABSTRACT FROM AUTHOR]

The development of hard X‐ray nanoprobe techniques has given rise to a number of experimental methods, like nano‐XAS, nano‐XRD, nano‐XRF, ptychography and tomography. Each method has its own unique data processing algorithms. With the increase in data acquisition rate, the large amount of generated data is now a big challenge to these algorithms. In this work, an intuitive, user‐friendly software system is introduced to integrate and manage these algorithms; by taking advantage of the loosely coupled, component‐based design approach of the system, the data processing speed of the imaging algorithm is enhanced through optimization of the parallelism efficiency. This study provides meaningful solutions to tackle complexity challenges faced in synchrotron data processing. [ABSTRACT FROM AUTHOR]

Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing's fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain. [ABSTRACT FROM AUTHOR]

Dependability of software systems is one of the challenging issues for software developers. Main software dependability issues include reliability, security, performability, availability, maintainability, and aging. Software becomes non-dependable due to the overconfidence of developers, lack of knowledge about dependability issues, or ignorance of dependability attributes during software development. Classification and ranking of these non-dependable software modules based on above-mentioned dependability attribute values in early phase are the main aspects of this article. Hence, computation of dependability attribute values becomes a primitive concern here. The values of software dependability attributes depend on various software metrics like: requirement stability, cyclomatic complexity, essential complexity, lines of code, and so on. Neutrosophic inference system (NIS) has been used here to compute the values of dependability attributes accurately, reducing incompleteness, indeterminacy, and impreciseness from metric values by incorporating expert knowledge. An Elman Recurrent Neural Network (ERNN)-based algorithm has been proposed here based on predicted dependability attribute values to classify dependable and non-dependable software modules. Backpropagation algorithm and Genetic Algorithm are used during training of ERNN. Mahalanobis distance (MD) is used to rank software modules based on dependability attributes at early phase of development. This entire process of dependability analysis will help to optimize resource utilization, development cost, and meet the target release time. Different comparison criteria are used to compare the effectiveness of the proposed model with some existing models based on four datasets. Performance analysis demonstrates effectiveness and usefulness for identifying and ranking the non-dependable software modules during early phase of development. [ABSTRACT FROM AUTHOR]

Software systems and product lines often use configurable features to specify a portfolio of product variants from a common core. Typically, their requirements also include constraints on which combinations of features are valid. Especially for larger systems and systems where the specifications are scattered among documents, the analysis of a new product's variability-related requirements is challenging. To address this, we introduce a scalable, tool-supported framework that uses a variability model to automate checks for missing and inconsistent features and constraints. Our approach also extends and scales traditional variability requirements engineering by incorporating combinatorial interaction testing techniques to build valid product variants covering all configurations in the variability model and to automatically discover faulty feature settings in failed builds. Results from evaluation on two configurable systems show that our framework is effective both at early detection of missing, incorrect, and inconsistent variability requirements and at later finding faulty feature configurations. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein's theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI. [ABSTRACT FROM AUTHOR]

Auxiliary functions in software systems, often overlooked due to their perceived simplicity, play a crucial role in overall system reliability. This study focuses on the effectiveness of agile practices, specifically the pair programming and the test-first programming practices. Despite the importance of these functions, there exists a dearth of empirical evidence on the impact of agile practices on their development, raising questions about their potential to enhance correctness without affecting time-to-market. This paper aims to bridge this gap by comparing the application of agile practices with traditional approaches in the context of auxiliary function development. We conducted six experiments involving 122 participants (85 novices and 37 professionals) who used both traditional and agile methods to develop six auxiliary functions across three different domains. Our analysis of 244 implementations suggests the potential benefits of agile practices in auxiliary function development. Pair programming showed a tendency towards improved correctness, while test-first programming did not significantly extend the total development time, particularly among professionals. However, these findings should be interpreted cautiously as they do not conclusively establish that agile practices outperform traditional approaches universally. As indicated by our results, the potential benefits of agile practices may vary depending on factors such as the programmer's experience level and the nature of the functions being developed. Further research is needed to fully understand the contexts in which these practices can be most effectively applied and to address the potential limitations of our study. [ABSTRACT FROM AUTHOR]

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method. [ABSTRACT FROM AUTHOR]

The scientific literature on vertical gardens presents gaps in the systematization of botanical parameters related to modeling and simulation. To address these gaps, the article aimed to investigate and identify parameters and modeling processes necessary for configuring vertical greening systems in computer simulation software. The systematic literature review method was employed to gather all scientific publications from the past ten years. As a result, the article presents the processes for 3D modeling, the most common software, and information tables containing botanical parameters used for configuring vegetation. Additionally, the work provides an overview of studies conducted on this topic in Architecture in recent years, highlighting other gaps and shortcomings in knowledge for possible future research. [ABSTRACT FROM AUTHOR]

This article explores the emerging problem of drug- and alcohol-impaired e-scooter riding. Fifteen government and e-scooter hire firm professionals from four jurisdictions (Australia, Belgium, New Zealand, Norway) described and assessed countermeasures designed to deter drug- and alcohol-impaired riding. Government professionals were from local/municipal councils, state and national departments of transport, and policing organisations. We used Braun and Clarke's (Qual Res Psychol 18(3):328–352, 2020.https://doi.org/10.1080/14780887.2020.1769238) six-step method of thematic analysis and the NVivo qualitative analysis software system to analyse the interviews. All participants said traditional police enforcement is ineffective because laws and regulations are underdeveloped, and police have limited resources for patrols. Thematic analysis grouped countermeasures into four themes: enforcement, education, encouragement, and the road environment. Third-party policing initiatives show promise. For example, local/municipal councils required e-scooter hire firms to ban e-scooters from areas that sell alcohol through GPS-based geofencing. However, geofencing and other regulatory controls do not extend to privately owned e-scooters, a growing sector in the market. Other countermeasures used a combination of education and behavioural change techniques (BCTs) to encourage compliance. For example, innovative nudge methods (a type of BCT) have potential to change dangerous riding behaviours by targeting riding norms. However, participants said some people will continue to ride under the influence of alcohol or drugs either because they are addicted to substances or exhibit high-risk behaviours. Government should therefore adopt laws and regulations that build a more forgiving road environment, based on the safe system approach, to limit injuries when crashes occur. [ABSTRACT FROM AUTHOR]

This article by Dr. Hugo Minney discusses the importance of benefits maps in project and program management. Benefits maps visually represent the relationships between activities, outputs, and outcomes, and guide stakeholders through the journey from project inception to the realization of benefits. The article emphasizes the principles of causality, materiality, and clarity when creating benefits maps, and highlights the importance of stakeholder involvement in the process. It also introduces different types of benefits maps, such as bidirectional strategy maps and portfolio benefits maps, and emphasizes the need for continuous refinement and integration with other project management tools. The given text is a list of references and sources related to benefits management and project management, providing additional resources for further research. [Extracted from the article]

Computational thinking is of great importance in developing students' ability to analyse complex problems in a systematic way, to develop appropriate strategies for solving these problems, and to use technology effectively in applying these solutions. In this context, mathematical modelling activities, which involve solving complex real-life situations through mathematics, stand out as an effective method for developing these skills in students. The purpose of this study is to investigate the effect of mathematical modelling activities on students' computational thinking (CT) self-efficacy. The participants of the study were English preparatory students at a high academic level public school in Istanbul. The modelling activities used in the study were restructured by considering the steps of computational thinking skills. Among these steps, the Python software language was used in the 'algorithmic thinking' step, and the students translated their problem-solving systems into software language through Python. As a data collection tool, in addition to the activities, the CT self-efficacy scale was used as a pre- and post-test. The mathematical modelling activities were applied to the high school preparatory class students, who were the research group, for a period, and the change in the students' CT self-efficacy was evaluated in the process. The results showed that modelling activities led to a significant increase in students' CT self-efficacy. It was found that students felt more competent in sub-dimensions such as problem solving, analytical thinking and algorithmic thinking. These findings suggest that mathematical modelling activities are a powerful tool for developing computational thinking skills. In conclusion, this study suggests that mathematical modelling activities should be more widely incorporated into the secondary school curriculum to develop computational thinking skills and draws attention to the importance of professional support for teachers in this process. [ABSTRACT FROM AUTHOR]

Quantum Software, its engineering and its applications are becoming more and more indispensable components of the current knowledge baggage of researchers and industrial professionals. Given the continuous accelerating developments of Quantum Computing, and the vastness of available knowledge, one needs efficient methods to dedicate the limited available personal time to the correct choice of selective information. This Special Issue on "Quantum Computing and Software Engineering" offers a small, but diverse set of papers covering theoretical, practical software system design and a pragmatic survey of Quantum Software Engineering, being a map pointing to references of relevance, aiming to answer the issue of choice of selective information. [ABSTRACT FROM AUTHOR]

Identifying key classes of a software system can help developers understand the system quickly, reduce the time for system maintenance, and prevent security risks caused by defects in key classes. So far, many approaches have been proposed to identify key classes from software systems. However, some approaches select too many key class candidates, making it inconvenient and difficult for developers to start understanding the system from these classes. For the other approaches, although the number of key class candidates is not large, their effectiveness needs to be further improved. To this end, in this paper, we propose a new model, named SAVM, to detect key classes by combining static analysis and a voting mechanism. First, we extract structural information from the source codes of a software system and construct a class coupling network (CCN) using this information. Then, we present the VRWD method that iteratively identifies important nodes in CCN based on a voting mechanism. Specifically, in each iteration, a node votes for its outgoing neighbors and in the meantime receives votes from its incoming neighbors. Afterward, the node that attains the highest voting score is elected as the important node in this turn. Finally, the corresponding classes of the selected important nodes are the key class candidates. The effectiveness of the proposed model and eight other baselines is evaluated in eight open-source Java projects. The experimental results show that although no method performs the best in all projects, according to the average ranking of the Friedman test, our method overall performs better compared to the baselines. In addition, this paper also proves through experiments that our approach can be applied to large-scale software projects. These indicate that our approach is a valuable technique for developers. [ABSTRACT FROM AUTHOR]

Privacy by design is nowadays recognized as essential in bringing data privacy into software systems. However, developers still face many challenges in reconciling privacy and software requirements and implementing privacy protections in software systems. One emerging trend is the adoption of microservices architectures—they bring in some qualities that can benefit privacy by design. The main goal of this study is to adapt privacy by design to the qualities brought by microservices. The main focus is at the architectural level, where the main structural decisions are made. A systematic literature review is adopted to identify a set of privacy models that underscore significant differences in software systems' protection using microservices. From the literature review, a decision framework is developed. The decision framework provides guidance and supports design decisions in implementing data privacy using microservices. The framework helps select and integrate different privacy models. An illustration of using the framework, which considers the design of an electronic voting system, is provided. This study contributes to closing the gap between regulation and implementation through design, where decisions related to data privacy are integrated with decisions on architecting systems using microservices. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for conducting load flow (LF) calculations in radial distribution networks. The method is based on solving LF calculations as a constrained minimization problem using nonlinear programming CONOPT solver, integrated into general algebraic modeling system (GAMS) environment. The performance of the proposed CONOPT solver is evaluated on IEEE 33-bus and IEEE 69-bus test systems, where four cases were considered. In the first case, LF calculations were performed on standard test systems, and the results obtained using the proposed CONOPT solver were compared with three well-known power systems analysis software: EPSA, NEPLAN, and DigSilent. In the second and third cases, CONOPT is used for LF calculation in the presence of distributed generators (DGs) and shunt capacitors (SCs), respectively. In the fourth case, the proposed CONOPT solver is employed for LF calculation in the presence of electric vehicles (EVs). The simulation results show that the CONOPT solver outputs are accurate and fast in their convergence to global solutions in standard test systems in the presence of DGs, SCs, and EVs. [ABSTRACT FROM AUTHOR]

Changes are inevitable in software due to technology advancements, and changes in business requirements. Making changes in the software by insertion, deletion or modification of new code may lead to malfunctioning of the old code. Hence, there is a need for a priori analysis to ensure and capture these types of changes to run the software smoothly. Making changes in the software while it is in use is called dynamic evolution. Due to the lack of formal modeling and verification, this dynamic evolution process of software systems has not become prominent. Hence, we used the bigraphical reactive system (BRS) technique to ensure that changes do not break the software functionality (adversely affect the system). BRS provides a powerful framework for modeling, analyzing, and verifying the dynamic evolution of software systems, resulting in ensuring the reliability and correctness of evolving software system. In this paper, we proposed a formal method technique for modeling and verifying the dynamic evolution process (changing user requirements at run time) using the BRS. We used a bigraph to model software architectures and described the evolution rules for supporting the dynamic changes of the software system. Finally, we have used the BigMC model checker tool to validate this model with its properties and provide associated verification procedures. [ABSTRACT FROM AUTHOR]

Software aging refers to the performance degradation and failure crash phenomena in long-running systems. As a proactive remedy, software rejuvenation can be scheduled timely to mitigate aging effects. Inescapably, how to accurately predict the time to aging failure (TTAF) of software is a prerequisite for implementing effective rejuvenation. However, the characterization of software aging is relatively complicated, leading to the selection of aging indicators case by case, which means that only fitting the variation trend of a single indicator for prediction models to formulate a rejuvenation schedule may be limited. To fill this gap, this paper proposes a novel framework called TTAFPred, which directly constructs the direct mapping relationships between the software aging process considering multiple system indicators and TTAF. Specifically, this framework includes three parts, i.e., data preprocessing, software degradation feature extraction, and TTAF prediction modules. First, the raw data is processed into the input form required by the network. Secondly, a temporal relationship extraction stream integrating bidirectional gated recurrent unit (BiGRU) with attention mechanism is used to extract temporal features from raw inputs. Synchronously, a spatial relationships extraction stream is adopted to extract the spatial features for enhancing the representation ability of degraded features by using the multi-scale one-dimensional convolutional neural network (1DCNN) with the residual connection. Then, extracted temporal-spatial features from the two streams are further fused. Finally, two fully-connected layers are constructed to estimate the TTAF. The experiments are performed on two mainstream software systems (OpenStack and Android), and four sets of real run-to-failure data for each software system are collected. The effectiveness of the proposed TTAFPred is verified through extensive experiments with its seven competing models, and the prediction performance can be improved by 9.1%, 8.0%, and 8.0%, respectively, in terms of three evaluation metrics, compared to the best baseline model. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine whether there are blind spots in decision making to protect innovation by examining how formal and informal protection systems affect software innovation. We empirically compared the innovation activities of 218 software companies with 261 information and communication technology (ICT) manufacturing companies in South Korea. Our results confirm that formal protection system alone is insufficient to protect software innovation, but its effect may vary depending on the corporate size. While, combination of patent with complicated designs is effective. More sophisticated policy to complement informal protections for small and medium‐sized enterprises with less than 100 employees is needed. [ABSTRACT FROM AUTHOR]