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HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade. [ABSTRACT FROM AUTHOR]

March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. However, studies show that March algorithms with complexities lower than 18N have poor coverages of faults that have emerged with the advent of the nanometer process technologies and are more relevant to nowadays memories. New March AZ1 and March AZ2 algorithms, with 13N and 14N complexities, respectively, were introduced to provide optimum coverage of those faults and to produce a shorter test than an 18N-complexity test algorithm with a lesser area overhead, thus reducing chip manufacturing costs. This paper presents the implementation and validation of MBIST controllers that applied the March AZ1 and March AZ2 algorithms in a Field-Programmable Gate Array (FPGA) device. They were implemented in the Intel Max 10 DE10-Lite FPGA Development Board. A test generator was built in FPGA, as an alternative to the external tester, to provide test vectors required in initiating the test on the memory model using the implemented MBIST. The FPGA experimental tests demonstrated that they function correctly as the expected test sequences were observed. In addition, their fault detection abilities were also validated through tests on a fault-injected memory model, which shows that the implemented March AZ1 and March AZ2 provide 80.6% and 83.3% coverage of the intended faults, respectively, which outperform any other existing 14N-complexity March algorithms. [ABSTRACT FROM AUTHOR]

Security is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools. [ABSTRACT FROM AUTHOR]

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive, as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road's structural features (lanes, turns, road speed, etc.). In this article, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in two dimensions. This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation. [ABSTRACT FROM AUTHOR]

Background. Software testing is a critical activity for ensuring the quality and reliability of software systems. To evaluate the effectiveness of different test suites, researchers have developed a variety of metrics. Problem. However, comparing these metrics is challenging due to the lack of a standardized evaluation framework including comprehensive factors. As a result, researchers often focus on single factors (e.g., size), which finally leads to different or even contradictory conclusions. After comparing dozens of pieces of work in detail, we have found two main problems most troubling to our community: (1) researchers tend to oversimplify the description of the ground truth they use, and (2) data involving real defects is not suitable for analysis using traditional statistical indicators. Objective. We aim at scrutinizing the whole process of comparing test suites for our community. Method. To hit this aim, we propose a framework ASSENT (evAluating teSt Suite EffectiveNess meTrics) to guide the follow-up research for evaluating a test suite effectiveness metric. ASSENT consists of three fundamental components: ground truth, benchmark test suites, and agreement indicator. Its functioning is as follows: first, users clarify the ground truth for determining the real order in effectiveness among test suites. Second, users generate a set of benchmark test suites and derive their ground truth order in effectiveness. Third, users use the metric to derive the order in effectiveness for the same test suites. Finally, users calculate the agreement indicator between the two orders derived by two metrics. Result. With ASSENT, we are able to compare the accuracy of different test suite effectiveness metrics. We apply ASSENT to evaluate representative test suite effectiveness metrics, including mutation score and code coverage metrics. Our results show that, based on the real faults, mutation score, and subsuming mutation score are the best metrics to quantify test suite effectiveness. Meanwhile, by using mutants instead of real faults, test effectiveness will be overestimated by more than 20% in values. Conclusion. We recommend that the standardized evaluation framework ASSENT should be used for evaluating and comparing test effectiveness metrics in the future work. [ABSTRACT FROM AUTHOR]

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

Regarding the caliber of newly-graduated professionals, the software industry expresses discontent. Therefore, software industries spend on training these individuals to improve software development process-related skills. According to various research studies, one reason for the lack of skilled graduates is a gap between industry and academia. One of the vital areas of software development where that gap exacerbates is software testing. The study aims to find skills (testing skills, Soft skills, and technical skills) that the software industry demands from novice software testers and assess their relevant importance. To find the skills that the software industry requires from fresh graduates, we have analyzed 160 job advertisements. The results revealed that the software industry focuses on testing skills and demands technical and soft skills, as 98% of advertisements require testing-related skills. Regarding soft skills, 92% of advertisements demanded soft skills. The novice tester must have strong technical skills as 78% of job advertisements require different technical skills. Moreover, the industry at present is also considering personality traits and educational attainment when hiring novice software testers. This study intends to identify the skill set for the novice software tester required by the Pakistan software industry. Results of the study indicate that software companies want to hire fresh graduates who are proficient in testing skills along with technical and soft skills. Therefore, there is a need to incorporate identified skills in software testing courses to better prepare computing graduates for the role of a novice software tester. [ABSTRACT FROM AUTHOR]

The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains. [ABSTRACT FROM AUTHOR]

Entrepreneurship is a global phenomenon because it contributes to economic growth, maintenance of social stability, and reduced unemployment. However, the role of entrepreneurial orientation in Vietnamese universities remains blurred. This study aims to understand the role of university education in shaping the entrepreneurial intention of Vietnamese students through entrepreneurial orientation, perceived desirability, and perceived feasibility. The PLS-SEM technique with SmartPLS 4 software tested the research model and hypotheses. The data set was collected from May 2023 to June 2023 via Google Forms with 411 respondents. The results show that entrepreneurial education has the most decisive impact on perceived desirability, followed by perceived feasibility, and both of these factors have a substantial impact on entrepreneurial orientation and entrepreneurial intention. Based on the results, the research has suggested practical implications that enhance entrepreneurial intention and promote the development of Vietnam's economy. [ABSTRACT FROM AUTHOR]

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes. [ABSTRACT FROM AUTHOR]

Motivation Oxford Nanopore Technologies (ONT) sequencers enable real-time generation of sequence data, which allows for concurrent analysis during a run. Adaptive sampling leverages this real-time capability in extremis , rejecting or accepting reads for sequencing based on assessment of the sequence from the start of each read. This functionality is provided by ONT's software, MinKNOW (Oxford Nanopore Technologies). Designing and developing software to take advantage of adaptive sampling can be costly in terms of sequencing consumables, using precious samples and preparing sequencing libraries. MinKNOW addresses this in part by allowing the replay of previously sequenced runs for testing. However, as we show, the sequencing output only partially changes in response to adaptive sampling instructions. Here we present Icarust, a tool enabling more accurate approximations of sequencing runs. Icarust recreates all the required endpoints of MinKNOW to perform adaptive sampling and writes output compatible with current base-callers and analysis pipelines. Icarust serves nanopore signal simulating a MinION or PromethION flow cell experiment from any reference genome using either R9 or R10 pore models. We show that simulating sequencing runs with Icarust provides a realistic testing and development environment for software exploiting the real-time nature of Nanopore sequencing. Availability and implementation All code is open source and freely available here— https://github.com/LooseLab/Icarust. Icarust is implemented in Rust, with a docker container also available. The data underlying this article will be shared on reasonable request to the corresponding author. [ABSTRACT FROM AUTHOR]

In order to construct an evaluation index system suitable for tourism management classroom teaching, this article evaluates the teaching effectiveness of teachers and improves the teaching quality of tourism management courses. This article is based on developmental evaluation theory, using Analytic Hierarchy Process, Project Response Theory, and CIPP model to construct an indicator system suitable for tourism management classroom teaching. Then, based on the collected data of 5763 students, the reliability and effectiveness of the tool and indicator system were first verified. Then, the variable of teacher teaching style was introduced to construct an OLS regression model for empirical research. The research will summarize teacher and student data collected through the platform and conduct reliability analysis in SPSS 22.0 software, using Cronbach α The credibility of coefficient testing and evaluation tools. Cronbach in Environmental Fundamentals α The cβoefficient value is 0.8350. Cronbach for resource allocation α The coefficient is 0.735, and the Cronbah of the implementation process α Cronb Bach with a coefficient of 0.7 47 for teaching performance α The coefficient is 0.7240, indicating that rat ings has high reliability. Research has found that among the four specific types, the holistic type has the greatest impact on the specific situation, the holistic type has the greatest impact on the environmental foundation and resource allocation, and the legislative type has the greatest impact on the implementation process and teaching performance. [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]

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]

Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP. [ABSTRACT FROM AUTHOR]

Test smells are considered bad practices that can reduce the test code quality, thus harming software testing goals and maintenance activities. Prior studies have investigated the diffusion of test smells and their impact on test code maintainability. However, we cannot directly compare the outcomes of the studies as most of them use customized datasets. In response, we introduced the TSSM (Test Smells and Structural Metrics) dataset, containing test smells detected using the JNose Test tool and structural metrics (test code and production code) calculated with the CK metrics tool of 13,703 open‐source Java systems from GitHub. In addition, we perform an empirical study to investigate the relationship between test smells and structural metrics of test code and the relationship between test smells on a large‐scale dataset. We split the projects into three clusters to analyze the distribution of test smells, the co‐occurrences among test smells, and the correlation of test smells and structural metrics of test code. The ratio of smelly test classes with a specific test smell is similar among the clusters, but we could observe a significant difference in the number of test smells among them. The test smells Sleepy Test, Mystery Guest, and Resource Optimism rarely occur in the three clusters, and the last two are strongly correlated, indicating that those test smells are more severe than others. Our results point out that most test smells have a moderate correlation with high complexity, large size, and coupling of the test code, indicating that they can also negatively affect its quality. To support further studies, we made our dataset publicly available. [ABSTRACT FROM AUTHOR]

PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results. [ABSTRACT FROM AUTHOR]

In this study, we evaluated extreme climate indicators for precipitation and temperature in the Brazilian Legal Amazon (BLA) from 1961 to 2021. Data from 38 National Institute of Meteorology (INMET) weather stations with a record failure rate less than 12% were used, and RClimDex software and the Mann–Kendall test, Pettitt test, standard normal homogeneity test (SNHT), and Buishand test were employed to analyse the data. The results showed increased extreme rainfall events, including the annual total rainfall divided by wet days (SDII). More frequent discontinuities were observed in the 1980s (SNHT) and 1990s (Pettitt and Buishand tests). The extreme temperature climate indicators also significantly increased, particularly in the 1990s. These increases are likely linked to changes in local climate conditions due to agricultural expansion, intensified industrial activities, and regional urbanization. This study underscores the need for proactive measures to curb illegal deforestation and reduce CO2 emissions and further research to better understand and mitigate the adverse impacts of these changes on the BLA environment and local communities. [ABSTRACT FROM AUTHOR]

To improve the handling and directional stability of three-axle heavy vehicles, this paper suggests a control strategy that combines direct yaw moment control (DYC) and active front steering (AFS). The control system's structure is divided into three main layers. Based on an online adjustable index, a fuzzy controller acting as a supervised system decides the cooperation of DYC and AFS in the upper layer. In the intermediate layer, the DYC system controller uses a sliding mode controller to calculate the corrective body moment. The AFS system uses a fuzzy controller to generate the corrective steering angle necessary to achieve the three-axle vehicle motion objective. The algorithm for distributing braking force and the slip ratio control (SRC) system comprises the lower layer. The anti-lock braking system (ABS) in the SRC system is built to produce the necessary braking forces at low slip ratios while preventing the wheels from locking up at high slip ratios. Consideration has been given to a heavy, three-axle, 9-DOF nonlinear vehicle with uncertain dynamics. Trucksim software and simulation tests have validated the model. The proposed control system's satisfactory performance is shown through various maneuvers. [ABSTRACT FROM AUTHOR]

One of the popular development trends in diesel engine piston assembly is higher reliability and life of components. These can be ensured by actual engine testing and comparing the appearance of piston assembly. However, testing multiple iterations involves significant cost and development which can be avoided by using software for piston-assembly performance parameters simulation. This paper contains a detailed study conducted using commercially available piston secondary dynamics simulation software and actual testing on a 4-cylinder diesel engine. In this study, the effect of piston profile and clearance on piston secondary dynamics parameters and piston-liner appearance are studied. In this study, three piston skirt profiles are designed by varying piston axial profile (barrel shape), radial profile (oval shape), and nominal clearances. The simulation results are studied in which piston profile 2 has shown minimum piston tilting, radial displacement, radial velocity, tilting velocity, radial acceleration, tilting acceleration, pressure, and force at TS and ATS. Testing and component merit rating for liner bores and piston skirts were also conducted in which piston profile 2 has shown significantly less liner bore polishing. A good correlation was observed between piston secondary motion parameters obtained from simulation and piston and liner bore observations after testing. [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]

Fuzzing is an important technique in software and security testing that involves continuously generating a large number of test cases against target programs to discover unexpected behaviors such as bugs, crashes, and vulnerabilities. Recently, fuzzing has advanced considerably owing to the emergence of new methods and corresponding tools. However, it still suffers from low coverage, ineffective detection of specific vulnerabilities, and difficulty in deploying complex applications. Therefore, to comprehensively survey the development of fuzzing techniques and analyze their advantages and existing challenges, this paper provides a comprehensive survey of the development of fuzzing techniques, summarizes the main research issues, and provides a categorized overview of the latest research advances and applications. The paper first introduces the background and related work on fuzzing. Research issues are subsequently addressed and summarized, along with the latest research developments. Furthermore, various customized fuzzing techniques in different applications are presented. Finally, the paper discusses future research directions. [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]

Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers' common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors' knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code's execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users. [ABSTRACT FROM AUTHOR]

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Rocket flight tests invariably serve a purpose, one of which involves area monitoring or aerial photography. Consequently, the rocket necessitates the installation of a camera that remains consistently oriented toward the Earth's surface throughout its trajectory. Thus, ensuring the rocket's stability and preventing any rotation becomes imperative. To achieve this, the Onboard Attitude Determination Control System (OADCS) was researched and developed, fully controlled by NI myRIO with Labview as the programming language, ensures the rocket's attitude control and maintains a rolling angle of 0 degrees during flight. The MyRIO oversees the retrieval of attitude and position data from the X-Plane flight simulator, offering feedback through actuator control. The development of the OADCS proceeded incrementally through stages utilizing the Software in the Loop Simulation (SILS) and Hardware in the Loop Simulation (HILS) techniques, to ensure the verification of the system's functionality before its application to the rocket for real flight testing. In the OADCS control scheme, Model Predictive Control (MPC) is chosen, and it is compared with a PID controller to serve as a benchmark for processing speed. Because the rocket's flight time is short and its speeds of up to Mach 4. The simulation results indicate that MPC can halt the rocket's rotation 12 times more rapidly than PID control. Additionally, the MPC's ability to maintain a zerodegree rotation can persist throughout the rocket's flight time. Employing SILS and HILS enhances the OADCS rocket development process by incorporating MPC, which holds promise for application in real rockets. [ABSTRACT FROM AUTHOR]

According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills. [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]

We are increasingly surrounded by systems connecting us with the digital world and facilitating our life by supporting our work, leisure, activities at home, health, and so on. These systems are pressed by two forces. On the one side, they operate in environments that are increasingly challenging due to uncertainty and uncontrollability. On the other side, they need to evolve, often in a continuous fashion, to meet changing needs, to offer new functionalities, or also to fix emerging failures. To make the picture even more complex, these systems rarely work in isolation and often need to collaborate with other systems, as well as humans. All such facets call for moving their validation during operation, as offered by approaches called testing in the field. In this article, we observe that even the field-based testing approaches should change over time to follow and adapt to the changes and evolution of collaborating systems or environments or users' behaviors. We provide a taxonomy of this new category of testing that we call self-adaptive testing in the field (SATF), together with a reference architecture for SATF approaches. To achieve this objective, we surveyed the literature and collected feedback and contributions from experts in the domain via a questionnaire and interviews. [ABSTRACT FROM AUTHOR]

The surface reflection pressure sensor installation flatness will directly lead to the change of the explosion shock wave pressure propagation and distribution law, affecting the test results accuracy, and the test data cannot accurately evaluate the ammunition explosion damage power. In this study, the numerical simulation model of the explosion shock wave pressure propagation and distribution law was established by using the explosive mechanics simulation software, and the pressure distribution law was studied when the sensors installation angles were 0°, 4°, 8°, 12°, −4°, −8°, and −12° respectively. Combined with analysis of the pressure peak value and the pressure evolution nephogram at different measuring points, it is clarified that the positive tilt angle of the sensor installation has an enhancing effect on the pressure peak, while the negative tilt angle has a attenuation effect on the pressure peak. Based on the calculation function formula of the surface reflected pressure peak value in the national defense engineering design code, the surface reflected pressure peak value correction function formula is established by introducing the sensor installation angle correction effect. This study results provide a theoretical basis for the design of ammunition explosion shock wave pressure engineering test scheme and the test data validity verification. [ABSTRACT FROM AUTHOR]

Test case growth in software maintenance poses multifaceted challenges, including extended execution times, heightened costs, maintenance complexities, and increased system intricacy. Larger test cases not only introduce redundancy but also elevate the risk of overlooking defects during testing, emphasizing the need to manage test case expansion effectively while maintaining adequate testing coverage. One approach to mitigating this issue is risk-based prioritization, which focuses on testing critical and high-risk areas of the software. By emphasizing these pivotal aspects, this method aims to streamline test cases, optimizing testing efforts. However, it has limitations, as it may not comprehensively address all functional requirements, necessitating an in-depth understanding of the software system's architecture and vulnerabilities. Furthermore, requirements-based prioritization ensures thorough testing of all functional requirements, crucial for meeting industry standards and regulatory compliance. This approach utilizes clear criteria provided by requirements to evaluate whether software functions as intended. While effective in guaranteeing comprehensive coverage, it may fall short in addressing all potential risks and vulnerabilities, potentially undermining its effectiveness in identifying and prioritizing critical areas of the software. Considering these challenges, a nuanced approach that combines risk-based and requirements-based prioritization could offer a balanced solution. By leveraging the strengths of both methods, it becomes possible to address the diverse aspects of software testing, optimizing coverage, and efficiently managing test case growth. Moreover, the proposed model demonstrates promising results in tackling these challenges. With a notable test suite size reduction of 0.61-1.05% compared to traditional methods and a faultless percentage surpassing comparative studies by 0.05-0.75% across seven C-language System Under Test (SUT) programs, the model showcases its efficacy in improving testing efficiency and reliability. These findings underscore the potential of adopting advanced models to navigate the complexities associated with test case management and software testing in general. [ABSTRACT FROM AUTHOR]

In this note, we evaluated the type I error control of the commonly used t-test found in most statistical software packages for testing the hypothesis on $ H_0: \rho = 0 $ H 0 : ρ = 0 vs. $ H_1: \rho > 0 $ H 1 : ρ > 0 based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality. To address this issue, we derived the large sample variance of the weighted Pearson correlation. Based on this result, we proposed an asymptotic test and a set of studentized permutation tests. A comprehensive set of simulation studies with a range of sample sizes and a variety of underlying distributions were conducted. The studentized permutation test based on Fisher's Z statistic was shown to robustly control the type I error even in the small sample and non-normality settings. The method was demonstrated with an example data of country-level preterm birth rates. [ABSTRACT FROM AUTHOR]

This paper addresses terminological consistency issues of three software testing glossaries used in academia and industry. The evaluation focus mainly deals with a sub-characteristic of information quality such as consistency, which includes syntactic and semantic consistency. To systematically conduct this study, we have established a set of activities or steps. These include defining the evaluation goal and scope, selecting the glossaries, conceiving the terminological categories, classifying the glossary terms into categories, calculating syntactic and semantic similarities, analyzing consistency, and making recommendations. For instance, for the testing domain, eight terminological categories were conceived, in which, for each selected glossary, a corresponding term is included in a category, considering the semantics intended by the authors of these standard documents. To count the occurrence frequency of a term in the glossaries, a tool was built that also takes into account the matching of synonyms. Then, a comparative analysis of syntactic and semantic consistency was carried out for all the terms ending in the word "testing," which enables us to give recommendations. This exploratory study identifies some inconsistencies that might deserve further attention and efforts to promote agreement and harmonization among the authors/editors of these glossaries in order to provide their readers with the most consistent and easiest way to understand and learn software testing concepts. [ABSTRACT FROM AUTHOR]

Mutation testing is an important technique able to evaluate the bug-detection effectiveness of existing software test suites. Mutation testing tools exist for several languages, e.g., Java and JavaScript, but no solutions are available for managing the mutation testing process for entire web applications, in the context of end-to-end (E2E) web testing. In this paper, we propose Mutta, a novel tool able to automate the entire mutation testing process. Mutta mutates the various server source files of the target web application, runs the E2E test suite against the mutated web applications, and finally collects the test outcomes. To evaluate Mutta, we designed a case study using the mutated versions of the target web application with the aim of comparing the effectiveness of two different approaches to E2E web testing: (1) test cases based on classical assertions and (2) test cases relying on differential testing. In detail, Mutta has been executed on two web applications, each equipped with different test suites to compare assertions with differential testing. In this scenario, Mutta generated a large number of mutants (more than 15k overall), took into account the coverage information to consider only the mutants actually executed, deployed the mutated web app, ran the entire E2E test suites (about 87k tests runs overall), and finally, it correctly saved the test suite results. Thus, results of the case study show that Mutta can be successfully employed to automate the entire mutation testing process of E2E web test suites and, therefore, can be used in practice to evaluate the effectiveness of different test suites (e.g., based on different techniques, E2E frameworks, or composed by a different number of test scripts). [ABSTRACT FROM AUTHOR]

In software testing, the quality of the test suite plays a very important role for not only the effectiveness of the testing but also the quality assurance of software. Mutation testing is considered as the usable, automatic and very effective technique in detecting mistakes of the set of test cases such as missing test cases, redundant test cases··· However, when using the mutation testing technique in practice, the generation of a large number of mutants has led to very high computational costs. This raises the question of whether we can reliably and accurately predict this mutation score without running mutants or not. If we can do this, it will save a lot of time and effort but still ensure the effectiveness of mutation testing. In this paper, we propose the approach using machine learning to perform mutation score cross-prediction for software which are new and completely different from the software used to generate test data (mutants) in model training and testing. The experimental results have shown that our proposed approach has achieved the positive results and is highly feasible. Thus, we believe that the approach can be applied to significantly reduce the cost of mutation testing. [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]

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]

Flooding in urban areas is expected to increase its magnitude and frequency in the future. Therefore, there is a strong need to better model sewer–surface flow interactions. Existing numerical methods are commonly based on simplified representations of sewer/surface mass exchange, and mainly validated in steady flow conditions. Current methodologies describing the propagation of transient conditions/waves through interaction nodes are simplified, rely on empirical coefficients and/or lack detailed validation. In this paper, an integrated numerical approach for modelling the propagation of water waves through interaction nodes (e.g., manholes) is presented. In this solution, the shallow water equations are used to simulate the free‐surface propagation inside the sewer network, and an ordinary differential equation is employed for modelling flow regimes through pipes and manholes. The model proposed is validated against the well‐known STAR‐CD modelling software for a number of test cases. Finally, further validation is performed against experimental data describing the evolution of water depth around a manhole in unsteady surcharging conditions. [ABSTRACT FROM AUTHOR]

We present the Python Red Pitaya Lockbox (PyRPL), an open source software package that allows the implementation of automatic digital feedback controllers for quantum optics experiments on commercially available, affordable Field-Programmable Gate Array (FPGA) boards. Our software implements the digital generation of various types of error signals, from an analog input through the application of loop filters of high complexity and real-time gain adjustment for multiple analog output signals, including different algorithms for resonance search, lock acquisition sequences, and in-loop gain optimization. Furthermore, all necessary diagnostic instruments, such as an oscilloscope, a network analyzer, and a spectrum analyzer, are integrated into our software. Apart from providing a quickly scalable, automatic feedback controller, the lock performance that can be achieved by using PyRPL with imperfect equipment, such as piezoelectric transducers and noisy amplifiers, is better than the one achievable with standard analog controllers due to the higher complexity of implementable filters and possibilities of nonlinear operations in the FPGA. This drastically reduces the cost of added complexity when introducing additional feedback loops to an experiment. The open-source character also distinguishes PyRPL from commercial solutions, as it allows users to customize functionalities at various levels, ranging from the easy integration of PyRPL-based feedback controllers into existing setups to the modification of the FPGA functionality. A community of developers provides fast and efficient implementation and testing of software modifications. [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]

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]

Testing Autonomous Driving Systems (ADS) is essential for safe development of self-driving cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested in interaction with the simulated environment. However, their high complexity and the multiple safety requirements lead to costly and ineffective testing. Recent techniques exploit many-objective strategies and ML to efficiently search the huge input space. Despite the indubitable advances, the need for smartening the search keep being pressing. This article presents CART (CAusal-Reasoning-driven Testing), a new technique that formulates testing as a causal reasoning task. Learning causation, unlike correlation, allows assessing the effect of actively changing an input on the output, net of possible confounding variables. CART first infers the causal relations between test inputs and outputs, then looks for promising tests by querying the learnt model. Only tests suggested by the model are run on the simulator. An extensive empirical evaluation, using Pylot as ADS and CARLA as simulator, compares CART with state-of-the-art algorithms used recently on ADS. CART shows a significant gain in exposing more safety violations and does so more efficiently. More broadly, the work opens to a wider exploitation of causal learning beside (or on top of) ML for testing-related tasks. [ABSTRACT FROM AUTHOR]

Researchers and tool developers working on dynamic analysis, software testing, automated program repair, verification, and validation, need large compiled, compilable, and executable code corpora to test their ideas. The publicly available corpora are relatively small, and/or non-compilable, and/or non-executable. Developing a compiled code corpus is a laborious activity demanding significant manual effort and human intervention. To facilitate large-scale program analysis research, we develop SourcererJBF, a Java Build Framework that can automatically build a large Java code corpus without project-specific instructions and human intervention. To generate a compiled code corpus, SourcererJBF creates an offline knowledge base by collecting external dependencies from the project directories and existing build scripts (if available). It constructs indices of those collected external dependencies that enable a fast search for resolving dependencies during the project compilation. As the output of the large-scale compilation, it produces JAigantic, a compilable Java corpus containing compiled projects, their bytecode, dependencies, normalized build script, and build command. We evaluated SourcererJBF's effectiveness, correctness, performance, and scalability in a large collection of Java projects. Our experimental results demonstrate that SourcererJBF is significantly effective and scalable in building large Java code corpus. Besides, it substantiates reasonable performance and correctness similar to projects' existing build systems. [ABSTRACT FROM AUTHOR]