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Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps. [ABSTRACT FROM AUTHOR]

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the challenges blind individuals face when comprehending mathematical expressions within a CAS environment. We propose "Math for Everybody" (Math4e, version 1.0), a software module to reduce barriers for blind users in education. Math4e is a Sonification Module for CAS that generates a series of auditory tones, prosodic cues, and variations in audio parameters such as volume and speed. These resources are designed to eliminate ambiguity and facilitate the interpretation and understanding of mathematical expressions for blind users. To assess the effectiveness of Math4e, we conducted standardized tests employing the methodologies outlined in the Software Engineering Body of Knowledge (SWEBOK), International Software Testing Qualifications Board (ISTBQ), and ISO/IEC/IEEE 29119. The evaluation encompassed two scenarios: one involving simulated blind users and another with real blind users associated with the "Asociación de Invidentes Milton Vedado" foundation in Ecuador. Through the SAM methodology and verbal surveys (given the condition of the evaluated user), results are obtained, such as 90.56% for pleasure, 90.78% for arousal, and 91.56% for dominance, which demonstrates significant acceptance of the systems by the users. The outcomes underscored the users' commendable ability to identify mathematical expressions accurately. [ABSTRACT FROM AUTHOR]

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system's frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events. [ABSTRACT FROM AUTHOR]

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%. [ABSTRACT FROM AUTHOR]

The European Union is committed to modernising and improving air traffic management systems to promote environmentally friendly air transport. However, the safety-critical nature of ATM systems requires rigorous user testing, which is hampered by the scarcity and high cost of air traffic controllers. In this article, we address this problem with a novel approach that involves non-experts in the evaluation of expert software in an A/B test setup. Using a transformation model that incorporates auxiliary information from a newly developed psychological questionnaire, we predict the performance of air traffic controllers with high accuracy based on the performance of students. The transformation model uses multiple linear regression and auxiliary information corrections. This study demonstrates the feasibility of using non-experts to test expert software, overcoming testing challenges and supporting user-centred design principles. [ABSTRACT FROM AUTHOR]

A covering array is a combinatorial object that is used to test hardware and software components. The covering array number is the minimum number of rows needed to construct a specific covering array. The search for better upper bounds for covering array numbers is a very active area of research. Although there are many methods for defining new upper bounds for covering array numbers, recently the use of covering perfect hash families has significantly improved a large number of covering array numbers for alphabets that are prime powers. Currently, excellent upper bounds have been reported for alphabets 2, 3, 4, and 5; therefore, the focus of this article is on defining new upper bounds on the size of covering arrays for the alphabet seven using perfect hash families. For this purpose, a greedy column extension algorithm was constructed to increase the number of columns in a covering perfect hash family while keeping the number of rows constant. Our greedy algorithm begins with a random covering perfect hash family containing only eight columns and alternates between adding and removing columns. Adding columns increases the size of the covering perfect hash family while removing columns reduces the number of missing combinations in covering perfect hash families with different column counts. The construction process continues with the covering perfect hash family with the smallest number of columns being refined (i.e., missing combinations reduced). Thus, columns are dynamically added and removed to refine the covering perfect hash families being built. To illustrate the advantages of our greedy approach, 152 new covering perfect hash families of order seven with strengths 3, 4, 5, and 6 were constructed, enabling us to improve 12,556 upper bounds of covering array numbers; 903 of these improvements are for strength three, 8910 for strength four, 1957 for strength five, and 786 for strength six. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity of vehicle software, it is becoming increasingly difficult to comprehensively test all requirements. This inevitably means that alternative test methods, e.g., simulation-based methods, must be used more frequently. However, the challenge involves identifying appropriate requirements that can be technically tested in a simulation environment initially. The present work is aimed at evaluation and optimization of the effectiveness of software-in-the-loop (SiL) simulations in the testing process of vehicle software. The focus is on supporting the testing process by shifting specific test cases from hardware-in-the-loop (HiL) test benches to SiL-based simulations. For this purpose, a systematic approach was developed to analyze and categorize requirements, enabling precise and efficient allocation of test cases. Furthermore, a detailed review and recommendation for improving the ProSTEP iViP standard for virtual electronic control units (vECU) was carried out. The developed matrix associates the defined requirement clusters with different classifications of vECUs, facilitating the identification of suitable test environment types for conducting specific test cases. By assigning test cases to appropriate vECU levels, the testing processes can be targeted and cost-optimized. Finally, the theoretical results were evaluated in an SiL simulation environment. It was observed that a significant part of the requirements could effectively be tested using a vECU. These findings confirmed the potential of SiL simulation environments to not only support, but also enhance, the testing process for vehicle software by providing a cost-effective and flexible complement to traditional HiL test benches. [ABSTRACT FROM AUTHOR]

Featured Application: The technique uses Machine Learning (ML) models to support decision-making on software testing scope and resource allocation to augment the outcomes with the available resources. As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models' in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach's applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort. [ABSTRACT FROM AUTHOR]

The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L'Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows. [ABSTRACT FROM AUTHOR]

The reliability of AI-based object detection models has gained interest with their increasing use in safety-critical systems and the development of new regulations on artificial intelligence. To meet the need for robustness evaluation, several authors have proposed methods for testing these models. However, applying these methods in industrial settings can be difficult, and several challenges have been identified in practice in the design and execution of tests. There is, therefore, a need for clear guidelines for practitioners. In this paper, we propose a method and guidelines for assessing the robustness of AI-based 2D object detection systems, based on the Goal Question Metric approach. The method defines the overall robustness testing process and a set of recommended metrics to be used at each stage of the process. We developed and evaluated the method through action research cycles, based on two industrial cases and feedback from practitioners. Thus, the resulting method addresses issues encountered in practice. A qualitative evaluation of the method by practitioners was also conducted to provide insights that can guide future research on the subject. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper. [ABSTRACT FROM AUTHOR]

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas. [ABSTRACT FROM AUTHOR]

New rapid, reliable, and cost-effective alternative systems are needed for the rapid diagnosis of Streptococcus pyogenes. The aim of this study was to fabricate a microfluidic test device to detect Streptococcus pyogenes by combining the Loop-mediated isothermal amplification method via a 3D printer. Microfluidic test devices were designed in CATIA V5 Release 16 software, and data were directly transferred to a 3D printer and produced using the FDM method with biocompatible PLA filament. The S. pyogenes ATCC 19615 and different ATCC strains was used. Following identification by classical culture methods, a 0.5 McFarland suspension was prepared from the colonies, and DNA isolation was performed from this liquid by a boiling method. S. pyogenes specific speB gene was used to desing LAMP primer sets in PrimerExplorer V5 software and tested on a microfluidic device. LAMP reactions were performed on microfluidic device and on a microcentrifuge tube separately. Both results were analyzed using the culture method as the standard method to diagnostic values. Melting curve analysis of the amplicons of the LAMP reactions performed on a LightCycler 480 system to detect amplification. Among the 50 positive and 100 negative samples, only four samples were found to be false negative by LAMP reaction in a microcentrifuge tube, while eight samples were found to be false negative by LAMP reaction on a microfluidic device. Six samples were found to be false positive by the LAMP reaction in the microcentrifuge tube, while ten samples were found to be false positive by the LAMP reaction on a microfluidic chip. The sensitivity, specificity, positive predictive value, and negative predictive value of the LAMP reactions performed in the microcentrifuge tube and on the microfluidic device were 92–84%, 94–90%, 88.46–80.77%, and 95.92–91.84%, respectively. The limit of detection (LOD) was found to be the same as 1.5 × 102 CFU/mL and the limit of quantification (LOQ) values of the LAMP reactions were performed on the microcentrifuge tube and on the microfluidic device were 2.46 × 102–7.4 × 102 CFU/mL, respectively. Cohen's kappa (κ) values of the LAMP reactions were performed on the microcentrifuge tube and on the microfluidic device were 0.620–0.705, respectively. In conclusion, our data showed that the LAMP method can be combined with microfluidic test device to detect S. pyogenes, this microfluidic device can be manufactured using 3D printers and results are close to gold standard methods. These devices can be combined with LAMP reactions to detect different pathogens where resources are limited and rapid results are required. [ABSTRACT FROM AUTHOR]

Automated software testing is a crucial yet resource-intensive aspect of software development. This burden on resources affects widespread adoption, with expertise and cost being the primary challenges preventing adoption. This paper focuses on automated testing driven by manually created test cases, acknowledging its advantages while critically analysing its implications across various development stages that are affecting its adoption. Additionally, it analyses the differences in perception between those in nontechnical and technical roles, where nontechnical roles (e.g., management) predominantly strive to reduce costs and delivery time, whereas technical roles are often driven by quality and completeness. This study investigates the difference in attitudes toward automated testing (AtAT), specifically focusing on why it is not adopted. This article presents a survey conducted among software industry professionals that spans various roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principal Component Analysis. In total, 81 participants received a series of 22 questions, and their responses were compared against job role types and experience levels. In summary, six key findings are presented that cover expertise, time, cost, tools and techniques, utilisation, organisation, and capacity. [ABSTRACT FROM AUTHOR]

This research raises questions about the possibilities and options of using the BIM methodology associated with software for the wood design and construction of structure modeling along an asset's cycle life. Likewise, several academic and research initiatives are reviewed. In this sense, this paper aims to establish an appropriate link between two agendas that the architecture, engineering, and construction (AEC) industry, academia, and governments normally handle separately. By conducting several literature reviews (book, journals, and congresses) and extensive software tests (BIM software: Revit v2023, Archicad v27, Tekla, and wood plug-ins: AGACAD, Archiframe, Timber Framing 2015, WoodStud Frame, etc.), the state-of-the-art was assessed in both fields, and several cases linking BIM and wood are shown in detail and discussed. Various theoretical samples are modelled and shown, and the advantages and disadvantages of each technique and stage are explained. On the other hand, although wood construction has been most common for hundreds of years, this is not the case of BIM software developments associated with this materiality. Furthermore, since the appearance of materials such as steel and reinforced concrete, all software developments have focused on these materials, leaving aside the possibility of developing applications for use in wood projects. According to that previously discussed, it can be concluded that BIM for wood has been used more frequently in academia, that both fields have several common processes, and, in many cases, that only a few BIM-wood tools have been used, thus disregarding the high potential and high level of benefits that result with the application of these methodologies for the complete building life cycle (design, construction, and operation). [ABSTRACT FROM AUTHOR]

Instability in coal pillars and filling bodies is a common occurrence during the mining process of continuous mining and continuous backfilling (CMCB). In view of this, combining numerical simulation, similarity simulation, and on-site testing approaches, backfill mining models were established in Flac3d5.01 software, similarity model test bench, and "two-stage", "three-stage", and "four-stage" mining sequences were conducted; the stress characteristics of coal pillar-filling body and the displacement evolution law of surrounding rock have been compared under three typical mining sequences. The results show that compared to two-stage mining sequence, three-stage and four-stage mining sequences provide sufficient time for the solidification of the filling body. The coal pillar exhibits better stability in the early stage of mining, but the stress concentration phenomenon is more significant in the later stage of mining. The stress concentration coefficient is the highest when the width of the coal pillar is 10 m. The integrity of the overburden is intact in different mining sequences, with only a small amount of separation and longitudinal cracks. Increasing the number of mining stages significantly reduces the roof subsidence, with the maximum roof subsidence in the three- and four-stage mining sequences being only 62.0% and 33.9% of that in the two-stage mining sequence. "Two stages", "three stages", and "four stages" of mining sequences are implemented in response to the requirements of weak and thick coal seam mining in Haoyuan Coal Mine and gangue disposal in Chahasu Coal Mine. Good engineering applications are achieved, enabling the realisation of safe, green, and efficient coal mining. [ABSTRACT FROM AUTHOR]

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate. [ABSTRACT FROM AUTHOR]

Over the last few years, a dynamic increase in the installed capacity of distributed energy sources has been observed, with the largest share being photovoltaic sources. The power grid is a system of connected vessels, and changing the structure of electricity production has a specific impact on the operation of this network, which makes it necessary to study the impact of the sources on the power system. The current and projected increase in the number of connected installations will make the issues of interaction and cooperation of distributed sources with the network extremely important. The article presents an analysis of the impact of a photovoltaic farm on selected parameters of the quality of electricity supply. This analysis was made on the basis of simulation results in a computer program and measurement tests carried out on a real photovoltaic farm with a capacity of 1.8 MW connected to the medium voltage power grid. The impact of the farm-generated power on the values of fundamental indicators of the quality of electricity supply, such as voltage deviations, voltage asymmetry factors, and voltage distortions factors, is presented. These relationships were determined based on the correlation and regression analysis of individual electrical quantities. [ABSTRACT FROM AUTHOR]

Chaotic maps have been widely studied in the field of cryptography for their complex dynamics. However, chaos-based cryptosystems have not been widely used in practice. One important reason is that the following requirements of practical engineering applications are not taken into account: computational complexity and difficulty of hardware implementation. In this paper, based on the demand for information security applications, we modify the local structure of the three-dimensional Intertwining Logistic chaotic map to improve the efficiency of software calculation and reduce the cost of hardware implementation while maintaining the complex dynamic behavior of the original map. To achieve the goal by reducing the number of floating point operations, we design a mechanism that can be decomposed into two processes. One process is that the input parameters value of the original system is fixed to 2 k by Scale index analysis. The other process is that the transcendental function of the original system is replaced by a nonlinear polynomial. We named the new map as "Simple intertwining logistic". The basic chaotic dynamic behavior of the new system for controlling parameter is qualitatively analyzed by bifurcation diagram and Lyapunov exponent; the non-periodicity of the sequence generated by the new system is quantitatively evaluated by using Scale index technique based on continuous wavelet change. Fuzzy entropy (FuzzyEn) is used to evaluate the randomness of the new system in different finite precision digital systems. The analysis and evaluation results show that the optimized map could achieve the designed target. Then, a novel scheme for generating pseudo-random numbers is proposed based on new map. To ensure its usability in cryptographic applications, a series of analysis are carried out. They mainly include key space analysis, recurrence plots analysis, correlation analysis, information entropy, statistical complexity measure, and performance speed. The statistical properties of the proposed pseudo random number generator (PRNG) are tested with NIST SP800-22 and DIEHARD. The obtained results of analyzing and statistical software testing shows that, the proposed PRNG passed all these tests and have good randomness. In particular, the speed of generating random numbers is extremely rapid compared with existing chaotic PRNGs. Compared to the original chaotic map (using the same scheme of random number generation), the speed is increased by 1.5 times. Thus, the proposed PRNG can be used in the information security. [ABSTRACT FROM AUTHOR]

COVID-19 has become a pandemic which has resulted in measures being taken for the health and safety of people. The spreading of this disease is particularly evident in indoor spaces, which tend to get overcrowded with people. One such place is the airport where a plethora of passengers gather in common places, such as coffee shops and duty-free shops as well as toilets and gates. Guiding the passengers to less overcrowded places within the airport may be a solution to reduce disease spread. In this paper, we suggest a passenger routing algorithm whereby the passengers are guided to less crowded places by using a weighting factor, which is minimised to accomplish the desired goal. We modeled a number of shops in an airport using the AnyLogic software and we tested the algorithm showing that the exposure time is less with routing and that people are appropriately spread out across the common spaces, thus preventing overcrowding. Finally, we added a real airport in Kavala, Greece to show the efficiency of our approach. [ABSTRACT FROM AUTHOR]

This paper starts with an overview of the main principles used for pressure measurements, focusing on their usage in industrial applications' domains. Then, the importance of calibration procedures, namely, static and dynamic calibration of pressure sensors, is analyzed. Regarding calibration, it is important to note that there are several applications where the pressure signals to be measured can have large variations in short periods of time. In industrial applications, particularly in continuous production processes, generally, dynamic pressure measurements are less common; however, they are still required in several cases, such as control loops that are very sensitive to pressure variations, even if the frequencies of those variations are in the range of a few tens of hertz, or even lower. The last part of the paper presents the hardware and software of a flexible and low-cost static and dynamic pressure calibrator that also presents the capability to generate arbitrary waveform pressure signals for calibration and testing purposes. The proposed calibrator also includes the following advantages: remote pressure sensing capabilities that can be used to minimize calibration errors, such as those associated with capillary effects and pressure leakages; portability; and low cost. The paper ends with some experimental results obtained with the proposed calibrator. [ABSTRACT FROM AUTHOR]

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software. [ABSTRACT FROM AUTHOR]

In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation. [ABSTRACT FROM AUTHOR]

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation. [ABSTRACT FROM AUTHOR]

This paper describes a hardware/software strategy for the effective and efficient management of several distributed Memory Built-In Self-Test (MBIST) units orchestrated by a single CPU to enable the parallel testing of several memory banks. Experimental testing of the implementation on an Infineon chip shows up to a 25% test time reduction compared to traditional strategies, especially in cases for which there are a large number of failures affecting several banks. Additionally, it permits balanced failure collection from different banks in cases for which there are limitations to the storage of failure-related information. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet technology has brought about the flourishing growth of social media live streaming. This study employs social presence theory as the primary framework to investigate the impact of Facebook Live's features of sociality, immediacy, and entertainment on users' sense of presence. These features were then considered within the dimensions of awareness, emotion, and cognition. The influence of social presence on user engagement behaviors was divided into browsing behavior, interactive behavior, and creative behavior. Using snowball sampling, an online survey was administered to Facebook Live users, and 416 valid responses were collected. The research team used software to analyze the data, primarily encompassing descriptive statistics, reliability and validity analyses, structural equation modeling, and mediation effects testing. The research findings are as follows. First, the media characteristics of Facebook Live significantly influence the sense of presence. Specifically, sociality, immediacy, and entertainment on Facebook Live have a notable impact on users' awareness, emotion, and cognition. Second, different dimensions of social presence have distinct effects on various user engagement behaviors. Notably, the dimensions of awareness, emotion, and cognition of social presence positively affect users' browsing and interactive behaviors, while emotion influences users' creative behavior. The third finding was that awareness, emotion, and cognition act as intermediates between Facebook Live's media characteristics and user engagement behaviors. Implications are discussed. [ABSTRACT FROM AUTHOR]

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 "Road vehicles—Cybersecurity engineering", is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle's cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry's challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework's availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus. [ABSTRACT FROM AUTHOR]

Various software testing techniques have been shown to be successful in producing high-quality test suites for software where the code is not accessible (black-box approach). Nevertheless, no method has been found to guide combining some of these in a general way. In this study, a test suite generation method for black-box software called CMBMeTest was created to respond to these challenges. It employs several coupled software testing techniques, namely, model-based testing (MBT), constraint programming (CP), and metamorphic testing (MT). CMBMeTest provides step-by-step instructions for using the information available (such as program specifications, inputs and outputs) to create an initial test suite that covers the model obtained, using a combination of MBT and CP (referred to as MBT+CP). Furthermore, using the metamorphic relations (MRs) of MT, a better test suite was produced from that initial test suite. The method allows particular stages to be iterated to improve the results by building new models and new MRs. A comprehensive case study was conducted, employing CMBMeTest to produce encouraging results. Mutation testing was used to evaluate the test suite, and the first round produced a high mutation score. A more detailed model was used to repeat the process, with similar outcomes. [ABSTRACT FROM AUTHOR]

Ensemble fuzzing in parallel with heterogeneous fuzzers has been proposed to leverage the advantages of diverse fuzzers and improve testing efficiency. However, in current ensemble fuzzing methods, the collaboration among different fuzzers is achieved solely by synchronizing the seeds discovered by each fuzzer. This results in a high likelihood of different fuzzers choosing the same seeds and creating a large number of equivalent testcases, thus reducing overall fuzzing efficiency. Meanwhile, the existing task division method proposed by AFLTeam is highly coupled with the fuzzer specially designed for it, making it challenging to apply to ensemble fuzzing directly. So, in this paper, we proposed a callgraph-based task division method suitable for ensemble fuzzing. Firstly, we divided the target program's callgraph into subgraphs (subtasks) balancing expected workloads. Then, we divided the global seed corpus into subcorpora, each corresponding to a subtask, making fuzzers easily accept the subtasks. Finally, we designed synchronization mechanisms for coverage bitmaps and seeds to realize the collaborative fuzzing among different fuzzers and a cyclic subtask scheduling strategy to fully leverage the benefits of ensemble fuzzing. We implemented a prototype called TAEF. The evaluation results show that in the best-case scenario, our method has up to 24% more branch coverage than previous work. [ABSTRACT FROM AUTHOR]

Static analysis is a software testing technique that analyzes the code without executing it. It is widely used to detect vulnerabilities, errors, and other issues during software development. Many tools are available for static analysis of Java code, including SpotBugs. Methods that perform a security check must be declared private or final; otherwise, they can be compromised when a malicious subclass overrides the methods and omits the checks. In Java, security checks can be performed using the SecurityManager class. This paper addresses the aforementioned problem by building a new automated checker that raises an issue when this rule is violated. The checker is built under the SpotBugs static analysis tool. We evaluated our approach on both custom test cases and real-world software, and the results revealed that the checker successfully detected related bugs in both with optimal metrics values. [ABSTRACT FROM AUTHOR]

Software testing and debugging are standard practices of software quality assurance since they enable the identification and correction of failures. Benchmarks have been used in that context as a group of programs to support the comparison of different techniques according to pre-established parameters. However, the reasons that inspire researchers to propose novel benchmarks are not fully understood. This article reports the investigation, identification, classification, and externalization of the state of the art about the proposition of benchmarks on software testing and debugging domains. The study was carried out using systematic mapping procedures according to the guidelines widely followed by software engineering literature. The search identified 1674 studies, from which, 25 were selected for analysis. A list of benchmarks is provided and descriptively mapped according to their characteristics, motivations, and scope of use for their creation. The lack of data to support the comparison between available and novel software testing and debugging techniques is the main motivation for the proposition of benchmarks. Advancements in the standardization and prescription of benchmark structure and composition are still required. Establishing such a standard could foster benchmark reuse, thereby saving time and effort in the engineering of benchmarks for software testing and debugging. [ABSTRACT FROM AUTHOR]

BTR code (originally—"Beam Transmission and Re-ionization", 1995) is used for Neutral Beam Injection (NBI) design; it is also applied to the injector system of ITER. In 2008, the BTR model was extended to include the beam interaction with plasmas and direct beam losses in tokamak. For many years, BTR has been widely used for various NBI designs for efficient heating and current drive in nuclear fusion devices for plasma scenario control and diagnostics. BTR analysis is especially important for 'beam-driven' fusion devices, such as fusion neutron source (FNS) tokamaks, since their operation depends on a high NBI input in non-inductive current drive and fusion yield. BTR calculates detailed power deposition maps and particle losses with an account of ionized beam fractions and background electromagnetic fields; these results are used for the overall NBI performance analysis. BTR code is open for public usage; it is fully interactive and supplied with an intuitive graphical user interface (GUI). The input configuration is flexibly adapted to any specific NBI geometry. High running speed and full control over the running options allow the user to perform multiple parametric runs on the fly. The paper describes the detailed physics of BTR, numerical methods, graphical user interface, and examples of BTR application. The code is still in evolution; basic support is available to all BTR users. [ABSTRACT FROM AUTHOR]

We propose CanaryExp, an exploitability evaluation solution for vulnerabilities among binary programs protected by StackGuard. CanaryExp devises three novel techniques, namely canary leakage proof of concept generation, canary leaking analysis time exploitation, and dynamic canary-relocation-based exploitability evaluation. The canary leakage proof of concept input generation mechanism first traces the target program's execution, transforming the execution state into some canary leaking state, from which some canary leaking input is derived. This input can be deemed as proof that some vulnerability that can lead to canary leakage exists. The canary leaking analysis time exploit generation then performs incremental analysis based on the canary leaking input, crafting analysis time exploit that can complete vulnerability exploitation in the analysis time environment. Based on the analysis time exploit, the dynamic canary-relocation-based exploitability evaluation component collects the necessary metadata, on which an exploitation session is automatically constructed that can not only leak the runtime canary and relocate it in the input stream but also evaluate the exploitability of the desired vulnerability. Using a benchmark containing six test programs, eight challenges from some network challenging events and four real-world applications, we demonstrate that CanaryExp can generate canary leaking samples more effectively than existing test case generation methods and automatically evaluate the exploitability for vulnerabilities among programs where the StackGuard protection mechanism is deployed. [ABSTRACT FROM AUTHOR]

This paper investigates a single-machine scheduling problem of a software test with shared common setup operations. Each job has a corresponding set of setup operations, and the job cannot be executed unless its setups are completed. If two jobs have the same supporting setups, the common setups are performed only once. No preemption of any processing is allowed. This problem is known to be computationally intractable. In this study, we propose sequence-based and position-based integer programming models and a branch-and-bound algorithm for finding optimal solutions. We also propose an ant colony optimization algorithm for finding approximate solutions, which will be used as the initial upper bound of the branch-and-bound algorithm. The computational experiments are designed and conducted to numerically appraise all of the proposed methods. [ABSTRACT FROM AUTHOR]

Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners' programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners' programming abilities and proficiency by exploring students' test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students' learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at. [ABSTRACT FROM AUTHOR]

This paper delves into the complex task of evaluating a website user interface (UI) and user experience (UX), a process complicated by gaps in research. To bridge this, we introduced an innovative human–computer interaction (HCI) framework that synergizes expert cognitive walkthroughs and user surveys for a comprehensive view. We transformed user responses into three key domains—control, engagement, and goal. Our work also generalized an extract of four context-level data metrics for a robust evaluation. The numerical evidence, such as a C1 score of 4.1, surpassing the expert usability benchmark, indicated our framework's effectiveness. Our research not only addresses an essential gap by integrating assessments from both users and experts, but also offers actionable insights for UI/UX design. The findings extend beyond this specific context, providing a potent evaluation tool for website usability across various websites. Lastly, the research underscores the importance of prioritizing users' needs and expert recommendations in design principles, significantly contributing to the broader domain of website usability and user experience. [ABSTRACT FROM AUTHOR]

Based on two progressive aspects of the modeling problems in business process management (BPM), (1) in order to address the increasing complexity of user requirements on workflows underlying various BPM application scenarios, a more verifiable fundamental modeling method must be invented; (2) to address the diversification of software testing processes, more formalized advanced modeling technology must also be applied based on the fundamental modeling method. Aiming to address these modeling problems, this paper first proposes an ADCV (acquisition, decomposition, combination, and verification) method that runs through the core management links of four types of business processes (mining, decomposition, recombination, and verification) and then describes the compositional structure of the ADCV method and the design of corresponding algorithms. Then, the software testing workflow is managed and monitored using the method, and the corresponding analysis tool is implemented based on Petri nets. At the same time, the tool is applied to the case processing of the software testing workflow. Specifically, the workflow models are established successively through ADCV during the process of business iteration. Then, the analysis tool developed with the ADCV method, the model–view–controller (MVC) design pattern, and Java Swing technology are applied to instances of the software testing workflow to realize the modeling and management of the testing processes. Thus, the analysis tool can guarantee the accuracy of the parameter estimations of related software reliability growth models (SRGMs) and ultimately improve the quality of software products. [ABSTRACT FROM AUTHOR]

Test case selection is to minimize the time and effort spent on software testing in real-time practice. During software testing, software firms need techniques to finish the testing in a stipulated time while uncompromising on quality. The motto is to select a subset of test cases rather than take up all available test cases to uncover most bugs. Our proposed model in the research study effort is termed SCARF-RT, which stands for Similarity coefficient (SC), Creating Acronyms, Regression test (RT), and Fuzzy set (FS) with Dataset (DS). Clustering of test cases using ranking and also based on similarity coefficients is to be implemented. This research considered eleven different features for clustering the test cases. Two techniques have been used. Firstly, each cluster will, to a certain extent, encompass a collection of distinct traits. Depending on the coverage of the feature, a cluster of test cases might be chosen. The ranking approach was used to create these groupings. The second methodology finds similarity among test cases based on eleven features. Then, the maxmin composition is used to find fuzzy equivalences upon which clusters are formed. Most similar test cases are clustered. Test cases of every cluster are selected as a test suite. The outcomes of this research show that the selected test cases based on the proposed approaches are better than existing methodologies in selecting test cases with less duration and at the same time not compromising on quality. Both fuzzy rank-based clustering and similarity coefficient-based clustering test case selection approaches have been developed and implemented. With the help of these methods, testers may quickly choose test cases based on the suggested characteristics and complete regression testing more quickly. [ABSTRACT FROM AUTHOR]

Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential. [ABSTRACT FROM AUTHOR]

While network forensics has matured over the decades and even made progress in the last 10 years when deployed in virtual networks, network forensics in fog and edge computing is still not progressed to that level despite the now widespread use of these paradigms. By using an approach similar to software testing, i.e., a mixture of systematic and experience, we analyze obstacles specific to forensics in fog and edge computing such as spatial dispersion and possibly incomplete recordings, and derive how far these obstacles can be overcome by adapting processes and techniques from other branches of network forensics, and how new solutions could look otherwise. In addition, we present a discussion of open problems of network forensics in fog and edge environments and discusses the challenges for an investigator. [ABSTRACT FROM AUTHOR]

Hybrid fuzzing is an automated software testing approach that synchronizes test cases between the fuzzer and the concolic executor to improve performance. The concolic executor solves path constraints to direct the fuzzer to explore the uncovered path. Despite many performance optimizations for hybrid fuzzing, we observe that the concolic executor often repeatedly performs constraint solving on branches with unsolvable constraints and branches covered by multiple test cases. This can cause significant computational redundancies. To be efficient, we propose BSFuzz, which keeps tracking the coverage state and solving state in a lightweight branch state map. BSFuzz synchronizes the current coverage state of all test cases from the fuzzer's queue with the concolic executor in a timely manner to reduce constraint solving for high-frequency branches. It also records the branch-solving state during the concolic execution to reduce repeated solving of unsolvable branches. Guided by the coverage state and historical solving state, BSFuzz can efficiently discover and solve more branches. The experimental results with real-world programs demonstrate that BSFuzz can effectively increase the speed of the concolic executor and improve branch coverage. [ABSTRACT FROM AUTHOR]

The increasing complexity of web applications and systems, driven by ongoing digitalization, has made software security testing a necessary and critical activity in the software development lifecycle. This article compares the performance of open-source tools for conducting static code analysis for security purposes. Eleven different tools were evaluated in this study, scanning 16 vulnerable web applications. The selected vulnerable web applications were chosen for having the best possible documentation regarding their security vulnerabilities for obtaining reliable results. In reality, the static code analysis tools used in this paper can also be applied to other types of applications, such as embedded systems. Based on the results obtained and the conducted analysis, recommendations for the use of these types of solutions were proposed, to achieve the best possible results. The analysis of the tested tools revealed that there is no perfect tool. For example, Semgrep performed better considering applications developed using JavaScript technology but had worse results regarding applications developed using PHP technology. [ABSTRACT FROM AUTHOR]

Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them. [ABSTRACT FROM AUTHOR]

Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance but computationally challenging. Previous research has shown that meta heuristic algorithms can devise class integration test orders with lower test stubbing complexity, resulting in software testing cost reduction. This study focuses on the comparable performance evaluation of ten commonly used meta heuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CS), firefly algorithm (FA), bat algorithm (BA), grey wolf algorithm (GWO), moth flame optimization (MFO), sine cosine algorithm (SCA), salp swarm algorithm (SSA) and Harris hawk optimization (HHO). The objective of this study is to identify the most suited algorithms, narrowing down potential avenues for future researches in the field of search-based class integration test order generation. The standard implementations of these algorithms are employed to generate integration test orders. Additionally, these test orders are evaluated and compared in terms of stubbing complexity, convergence speed, average runtime, and memory consumption. The experimental results suggest that MFO, SSA, GWO and CS are the most suited algorithms. MFO, SSA and GWO exhibit excellent optimization performance in systems where fitness values are heavily impacted by attribute coupling. Meanwhile, MFO, GWO and CS are recommended for systems where the fitness values are strongly influenced by method coupling. BA and FA emerge as the slowest algorithms, while the remaining algorithms exhibit intermediate performance. The performance analysis may be used to select and improve appropriate algorithms for the CITO problem, providing a cornerstone for future scientific research and practical applications. [ABSTRACT FROM AUTHOR]

The pump system is the heart of the concrete pump truck, and its main function is to deliver high-pressure concrete into the pipeline through the reciprocating motion and cooperation of the main cylinder and the distribution valve driven by the swing cylinder. However, there are currently problems, such as unclear pumping operation rules and mismatched commutation timing, that result in poor pumping efficiency and short life of wear parts such as spectacle plates and cutting rings. To address above problems, based on AMEsim software and system test data, this thesis establishes an accurate simulation model of the pump system with more than 90% precision. Based on this model, the simulation reveals the operating rules of the main cylinder and the swing cylinder during the pumping process; draws the commutation timing diagram of the pumping system; clarifies that the commutation timing has four characteristics: sequential, hysteresis, coupling and cyclic; and proposes an optimization scheme for the commutation timing, i.e., reducing the relief pressure of the main cylinder and shortening the swing time of the distribution valve. The proposed optimization has been proven in the market and significantly improved the life of wearing parts by more than 90%. This study provides technical reference for the structure and parameter optimization of concrete pumping systems. [ABSTRACT FROM AUTHOR]

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques. [ABSTRACT FROM AUTHOR]

Microgrids represent a promising energy technology, because of the inclusion in them of clean and smart energy technologies. They also represent research challenges, including controllability, stability, and implementation. This article presents a dSPACE-control-platform-based implementation of a fixed-switching-frequency modulated model predictive control (M 2 PC) strategy, as an inner controller of a two-level, three-phase voltage source inverter (VSI) working in an islanded AC microgrid. The developed controller is hierarchical, as it includes a primary controller to share the load equally with the other power converter with its own local modulated predictive-based controller. All details of the implementation are given for establishing the dSPACE-based implementation of the control on a dSPACE ds1103 control platform, using MATLAB/Simulink for the controller design, I/O implementation and configuration with the embedded dSPACE's real-time interface in Simulink, and then using the ControlDesk software for monitoring and testing of the real plant. The latter consists of the VSI operating with  L C L  filters, and sharing an  R L  load with a paralleled VSI with exactly the same controller. Finally, the obtained experimental waveforms are shown, with our respective conclusions representing this work, which is a very valuable tool for helping microgrid researchers implement dSPACE-based real-time simulations. [ABSTRACT FROM AUTHOR]

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. It is a constant struggle to maximize test coverage through optimal data generation. We conducted explanatory research to maximize test coverage of security requirements as modeled in the structured misuse case description (SMCD). The acceptance test case is designed through the structured misuse case description for mitigation of security threats. Mal activity is designed from SMCD upon which constraints are specified in object constraint language (OCL) in order to minimize human dependency and improve consistency in the optimal test case design. The study compared two state-of-the-art test coverage maximization approaches through optimal test data generation. It was evident through the results that MC/DC generated optimal test data, i.e., n + 1 test conditions in comparison to the decision coverage approach, i.e., 2n test conditions for security threats. Thus, MC/DC resulted in a significantly lower number of test cases yet maximized test coverage of security threats. We, therefore, conclude that MC/DC maximizes test coverage through optimal test data in comparison to decision coverage at the design level for security threat mitigation. [ABSTRACT FROM AUTHOR]