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The three‐body recombination reaction, or ternary association, is a termolecular reaction leading to a molecule after a three‐body encounter that plays a vital role in many relevant scenarios in chemical physics. Here, we introduce the Python 3‐Body Recombination program, which is dedicated to the computation of atomic three‐body recombination rate coefficients. The software is based on a classical trajectory approach in hyperspherical coordinates after mapping the three‐body problem as a single particle in a higher‐dimensional space. This theoretical approach is fully general and applicable to any ion‐atom‐atom or atom‐atom‐atom three‐body process. The predictive power of the methodology has been tested in several different experimental scenarios, reaching a good description of every system. The code structure is presented alongside examples and tests to ensure the software's capacity. In addition, the performance of the software after parallelization is shown. [ABSTRACT FROM AUTHOR]

Summary: Graphic processors offer an accessible solution for high‐performance computing, addressing challenges across various fields. The Compute Unified Device Architecture (CUDA) programming model has emerged to enhance the performance of general‐purpose applications on graphic processors. However, developing CUDA programs is far from straightforward, and developers' lack of experience in parallel programming has led to numerous issues. This article presents a structural testing model and criteria to improve the quality of CUDA programs. These criteria facilitate the selection of test cases and aid in identifying faults. The ValiCUDA tool was developed to implement and validate this testing model and criteria. This tool instruments and analyzes programs, generating the necessary elements for each testing criterion. It also facilitates program execution and evaluation of criterion coverage. A statistical validation experiment assessed these criteria' effectiveness, cost, and strength metrics. The results demonstrate that the criteria can identify nontrivial faults in CUDA programs and assist testers in their testing endeavors for such applications. [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]

Objective: This meta-analysis aims to analyze the relationship between serum vitamin D (VD) levels and Graves' disease (GD). Methods: We conducted a search for publications on VD and GD in the English language. Our search encompassed databases such as PubMed, Embase, Web of Science, and the Cochrane Library, covering publications available through August 2023. A meta-analysis was performed using Cochrane RevMan 5.4 software. The standardized mean difference (SMD) and 95% confidence interval (CI) were used for outcome calculation. We used R software to test for publication bias. Results: Twelve studies were selected, comprising 937 (22.4%) cases with GD and 3254 (77.6%) controls. The overall meta-analysis revealed that patients with GD are significantly more likely to have low VD levels (SMD = − 0.66; 95% CI: −1.05, − 0.27; p = 0.001) than those in the control group. Egger's test results indicated no publication bias (p = 0.0791). These studies exhibited a high degree of heterogeneity (chi-square = 205.86, p < 0.00001; I2 = 95%). Subgroup analysis was conducted based on assay method, geographic location, and mean age of the case group to explore the heterogeneity sources. Assay methods and geographic locations were identified as potential heterogeneity sources. Based on the mean age, there were no statistically significant differences found in the subgroup analysis of the included studies. Conclusion: There is promising evidence that low serum VD levels may increase the risk of GD. Further rigorous and long-term trials are needed to explore the role of VD in the onset and treatment of GD. [ABSTRACT FROM AUTHOR]

Objective: Today it is impossible to imagine human existence without information and communication technology. This study aimed to examine the relationship between the use of virtual spaces and citizens' political participation in Bandar Abbas, Iran. Methods: The survey research method was used to collect information on the use of virtual spaces and political participation. The study focuses specifically on citizens of Bandar Abbas who have reached the legal age of political participation (over 18 years). The sample size of 383 individuals was selected using cluster sampling technique from four districts of Bandar Abbas. Statistical analysis was performed using SPSS software and hypothesis testing was performed using Pearson correlation coefficient, frequency tables and graphs. Spearman and Pearson correlation methods were used to measure the relationship between variables, while the chi-square test was used to examine the association between gender and political participation. To determine the extent of the use of virtual spaces and political participation, a comprehensive questionnaire with 33 questions was completed. Results: The implications of this study highlighted the need for policymakers and political actors to adapt to the changing landscape of political participation in the digital age. It is critical to ensure that virtual spaces are accessible, inclusive, and provide opportunities for diverse voices to be heard. Conclusions: Addressing political dissatisfaction and concerns expressed in virtual spaces can help strengthen democratic processes and promote a more engaged and participatory citizenry. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDN) enable flexible and effective communication systems that are managed by centralized software controllers. However, such a controller can undermine the underlying communication network of an SDN-based system and thus must be carefully tested. When an SDN-based system fails, in order to address such a failure, engineers need to precisely understand the conditions under which it occurs. In this article, we introduce a machine learning-guided fuzzing method, named FuzzSDN, aiming at both (1) generating effective test data leading to failures in SDN-based systems and (2) learning accurate failure-inducing models that characterize conditions under which such system fails. To our knowledge, no existing work simultaneously addresses these two objectives for SDNs. We evaluate FuzzSDN by applying it to systems controlled by two open-source SDN controllers. Furthermore, we compare FuzzSDN with two state-of-the-art methods for fuzzing SDNs and two baselines for learning failure-inducing models. Our results show that (1) compared to the state-of-the-art methods, FuzzSDN generates at least 12 times more failures, within the same time budget, with a controller that is fairly robust to fuzzing and (2) our failure-inducing models have, on average, a precision of 98% and a recall of 86%, significantly outperforming the baselines. [ABSTRACT FROM AUTHOR]

Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequences, such as financial losses. This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is that a pruned sentence should have similar crucial semantics compared with the original sentence. Specifically, STP (1) proposes a core semantics-preserving pruning strategy by basic sentence structures and dependency relations on the level of syntactic tree representation, (2) generates source sentence pairs based on the metamorphic relation, and (3) reports suspicious issues whose translations break the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bing Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP accurately finds 5,073 unique erroneous translations in Google Translate and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively. The reported erroneous translations vary in types and more than 90% of them are not found by state-of-the-art techniques. There are 9,393 erroneous translations unique to STP, which is 711.9% more than state-of-the-art techniques. Moreover, STP is quite effective in detecting translation errors for the original sentences with a recall reaching 74.0%, improving state-of-the-art techniques by 55.1% on average. [ABSTRACT FROM AUTHOR]

Fuzzing is a popular software testing method that discovers bugs by massively feeding target applications with automatically generated inputs. Many state-of-the-art fuzzers use branch coverage as a feedback metric to guide the fuzzing process. The fuzzer retains inputs for further mutation only if branch coverage is increased. However, branch coverage only provides a shallow sampling of program behaviors and hence may discard interesting inputs to mutate. This work aims to take advantage of the large body of research in defining finer-grained code coverage metrics (such as control-flow, data-flow, or mutation coverage) and to evaluate how fuzzing performance is impacted when using these metrics to select interesting inputs for mutation. We propose to make branch coverage-based fuzzers support most fine-grained coverage metrics out of the box (i.e., without changing fuzzer internals). We achieve this by making the test objectives defined by these metrics (such as conditions to activate or mutants to kill) explicit as new branches in the target program. Fuzzing such a modified target is then equivalent to fuzzing the original target, but the fuzzer will also retain inputs covering the additional metric objectives for mutation. In addition, all the fuzzer mechanisms to penetrate hard-to-cover branches will help in covering the additional metric objectives. We use this approach to evaluate the impact of supporting two fine-grained coverage metrics (multiple condition coverage and weak mutation) over the performance of two state-of-the-art fuzzers (AFL++ and QSYM) with the standard LAVA-M and MAGMA benchmarks. This evaluation suggests that our mechanism for runtime fuzzer guidance, where the fuzzed code is instrumented with additional branches, is effective and could be leveraged to encode guidance from human users or static analyzers. Our results also show that the impact of fine-grained metrics over fuzzing performance is hard to predict before fuzzing and most of the time either neutral or negative. As a consequence, we do not recommend using them to guide fuzzers, except maybe in some possibly favorable circumstances yet to be investigated, like for limited parts of the code or to complement classical fuzzing campaigns. [ABSTRACT FROM AUTHOR]

Software projects grow larger every year, which, in turn, makes the testing process harder. One of the most useful methods for testing large projects is unit‐test generation. However, some tests can repeatedly cover the same parts of the code, making it difficult to maintain a growing test codebase. In software testing, test suite minimization plays a crucial role in reducing the cost of testing and improving the efficiency of the testing process. In this paper, we provide an extensible minimization engine that detects redundant tests using one of the supported minimization algorithms without changing the coverage metrics. We also performed a comprehensive analysis of existing approaches and techniques, developed an engine structure, and implemented multiple algorithms of different kinds. Finally, we evaluated our tool on various open‐source projects to demonstrate its effectiveness and efficiency. [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]

The integration of the Pencel Pressuremeter (PPMT) model with the Automated Pressuremeter (APMT) software represents a significant advancement in in-situ soil testing. This combination simplifies the process of data reduction and analysis, resulting in substantial time savings. The APMT software efficiently records digital pressure and volume data, performs necessary calibrations, and offers quick access to essential strength and stiffness properties for engineering analysis. Through extensive testing conducted on various soil types, the integration of a linear potentiometer and a digital pressure transducer into the control unit has significantly improved the accuracy of digital volume measurements and pressure readings. Notably, the Stabilization Time (ST) for volume increments, as estimated by the APMT software, ranges from 20 to 70 seconds depending on the specific soil conditions. The APMT system not only enhances data quality but also minimizes the potential for human recording errors. It drastically reduces the time required for data collection and analysis when compared to manual methods, establishing itself as an efficient and precise tool for evaluating soil properties. [ABSTRACT FROM AUTHOR]

Abstract-Method overriding is a valuable mechanism that happens when an instance method is defined in a subclass and has the same signature and return type as an instance method in the superclass. However, in Java, if those methods are static, then instead method hiding happens, which is a programming weakness and may produce unexpected results. Static analysis is an approach in software testing that examines code to identify various programming weaknesses throughout the software development process without running it. This paper addresses the detection of method-hiding problem in Java programs. We implemented a new automated checker under the SpotBugs static analysis tool that can detect the mentioned problem. According to our results, the checker precisely detected the related issues in both custom test cases and real-world programs. [ABSTRACT FROM AUTHOR]

As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alternatively the priority of validation suites after identifying the difficulties in the regression testing process of any product. The test case prioritisation technique is presented as a solution to this problem. It increases the fault detection rate. Earlier research studies have been implemented many techniques, but the rate of fault detection is not up to the mark. To overcome this drawback, we proposed HOTCP (Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints) model, which includes two steps: first is test case generation and the next one is test case prioritisation. The test case is generated from the released software. Consequently, test case prioritisation will be done by the optimisation strategy, in which the multi-objective function will be defined based on the constraints like statement coverage, branch coverage, contribution index and fault exposing potential. For this optimisation process, a new algorithm is proposed termed as CCCOA (Customised Coot and Chimp Optimisation Algorithm). The COOT optimisation and Chimp optimisation algorithms are combined in this algorithm. The system produces prioritised test cases, and the performance of the proposed method is validated in comparison to the traditional methods in terms of several metrics. [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]

Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew's correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions. [ABSTRACT FROM AUTHOR]

Considering the testing process of the software system as a stochastic process is a primary approach to the software reliability modeling technique. Besides some popular distributions, the Poisson distribution has been considered the best based on its advantage when modeling the times at which arrivals enter a system. In the non-homogeneous Poisson process group of models, the S-shaped function is a value curve with many good results. This paper proposes a new imperfect debugging software reliability model based on (1) an Imperfect debugging assumption (the testing process could cause new faults); and (2) the Fault detection rate can be controlled more effectively by the appearance of a growth-rate-controller. The real data from industrial projects verify the application of this model based on good popular criteria values. [ABSTRACT FROM AUTHOR]

Test smells, detrimental coding practices that hinder high-quality test code development, pose a significant challenge in software testing and maintenance. Software refactoring, traditionally a powerful approach for addressing code smells and improving software quality without changing functionality, often focuses on production code, leaving test code overlooked. Despite extensive research in test smell refactoring, understanding the efficacy of existing refactoring operations on test code quality remains limited. Investigating real-world developer refactoring practices is crucial to bridge this knowledge gap. In this study, we investigate refactorings performed by developers to address test smells, resulting in a comprehensive catalog of test smells and their corresponding test-specific refactorings. Two test-specific refactorings closely tied to JUnit5 and seven version-agnostic refactorings for various JUnit versions have been identified. While many of these test-specific refactorings are documented in the literature, this analysis unveils new test-specific refactorings aimed at dealing with the "Inappropriate Assertion" test smell. This research provides insights into the challenges faced by developers and prevailing practices for effectively refactoring test code, thereby enhancing software testing and maintenance. [ABSTRACT FROM AUTHOR]

Boundary value analysis (BVA) is a common technique in software testing that uses input values that lie at the boundaries where significant changes in behavior are expected. This approach is widely recognized and used as a natural and effective strategy for testing software. Test coverage is one of the criteria to measure how much the software execution paths are covered by the set of test cases. This paper focuses on evaluating test coverage with respect to BVA by defining a metric called boundary coverage distance (BCD). The BCD metric measures the extent to which a test set covers the boundaries. In addition, based on BCD, we consider the optimal test input generation to minimize BCD under the random testing scheme. We propose three algorithms, each representing a different test input generation strategy, and evaluate their fault detection capabilities through experimental validation. The results indicate that the BCD-based approach has the potential to generate boundary values and improve the effectiveness of software testing. [ABSTRACT FROM AUTHOR]

Fault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness. [ABSTRACT FROM AUTHOR]

Due to the increasing reliance on technology in nearly every industry over the past three decades, it has become necessary to evaluate the performance of a software product prior to its formal release in the market. The properties of a software application, such as its complexity and lines of code, are subject to change over time as a result of factors namely the testing environment, allocation of resources, testing efficiency, and testing team's expertise. The assumption of constant Fault Detection Rate (FDR) may not accurately anticipate the  potential number of bugs correctly. Keeping all these considerations in mind, a framework is developed to incorporate change point in the development of a testing effort-based Software Reliability Growth Model (SRGM) that takes into account the effect of application characteristics under both perfect and imperfect debugging settings. In addition, these outcomes are compared to the model without a change point. The proposed model is validated on two real-life software fault datasets. The results demonstrate that the proposed model performs better than the model without a change point. [ABSTRACT FROM AUTHOR]

Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process. [ABSTRACT FROM AUTHOR]

This research explores the incorporation of green computing variables into the rational unified process (RUP) methodology, specifically focusing on sustainable development goal (SDGs) 12-responsible consumption and production. Supported by three additional papers using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. Our study aims to promote eco-friendly software development practices and tools (artifacts) aligned with green computing principles to support SDGs throughout RUP development phases. We conducted a matrix thorough analysis of existing green computing adaptability within RUP, yielding key findings: a system charter for inception, system requirement specification for elaboration, software development result for construction, and software test report/user acceptance test for transition. As a result, we've compiled comprehensive phase-specific documents, emphasizing the need for educational initiatives to foster green computing adoption among developers. This study advocates for cross-disciplinary collaboration to ensure successful implementation of eco-friendly software development processes. [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]

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]

A comprehensive laboratory testing program, field-testing program, numerical analysis, and life-cycle cost analysis were conducted to evaluate the beneficial effects of incorporating shrinkage-reducing admixture (SRA), polymeric microfibers (PMFs), and optimized aggregate gradation (OAG) into internally cured concrete (ICC) mixtures for rigid pavement applications. Results from the laboratory program indicate that all the ICC mixtures outperformed the standard concrete (SC) mixture. All the ICC mixtures showed a decrease in drying shrinkage compared to the SC mixture. Based on the laboratory program, three ICC mixtures and one SC mixture were selected for the full-scale test and subjected to a heavy vehicle simulator for accelerated fatigue testing. Extensive testing and analysis have shown that ICC mixtures incorporating SRA, PMFs, and OAG can be beneficially used in pavement applications to achieve increased pavement life. [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]

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]

Apart from the widespread acceptance of the digital library as a major research response among university lecturers and students, little is known about the reliability of its RFID software (RFIDS), the challenges posed during software flaws, and the improvement strategies that can be used to maintain the RFID software reliability. The various NHPP software reliability growth models (SRGMs) to investigate the reliabilities of software products of this kind in real-world software development has not always been achieved because those studies were primarily concerned with modeling fault detection processes (FDP) and ignoring the fault correction processes (FCP). Here, we propose a reliability model of RFID software while keeping in mind the interdependence between fault quantities of the dual procedures of fault detection and fault correction. In evaluating the software reliability, fault introduction and testing coverage rates were eventually included to the parameters making our proposed model to be highly effective in estimating and forecasting the dependability of the software when compared with existing NHPP software reliability growth models (SRGMs). [ABSTRACT FROM AUTHOR]

This study investigates the development and evaluation of an Android-based security system for residential areas, focusing on entrance and exit gate control. Recognizing the limitations of existing systems, we propose an "IoT-based Fingerprint Security Village" application. The system utilizes a prototype development model informed by data collected through interviews, questionnaires, and observations. The application allows residents to monitor and access gates using fingerprint scanners, enhancing security and convenience. To ensure quality, the system underwent rigorous testing based on the ISO 25010 software testing standard. Testing encompassed five key aspects, Functional Suitability: The application achieved a perfect score, indicating it flawlessly performs its intended functions. Portability: The application successfully functioned on all 14 tested Android devices. Performance Efficiency: CPU usage remained exceptionally low (average 0.1%, max 5.5%), and memory usage (average 58.2 MB, max 86.5 MB) demonstrated efficient resource utilization. Security: Testing revealed a low-medium security risk, highlighting areas for potential improvement. Usability: The user-friendliness test yielded a very high score (92%), placing it in the "Very Feasible" category. These results confirm that the "IoT-based Fingerprint Security Village" application effectively meets all evaluated aspects of the chosen software testing standard. [ABSTRACT FROM AUTHOR]

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test cases. However, existing automated tools are not mature enough to be widely used by software testing groups. This paper conducts an empirical study on the state-of-the-art automated tools for Java, i.e., EvoSuite, Randoop, JDoop, JTeXpert, T3, and Tardis. We design a test workflow to facilitate the process, which can automatically run tools for test generation, collect data, and evaluate various metrics. Furthermore, we conduct empirical analysis on these six tools and their related techniques from different aspects, i.e., code coverage, mutation score, test suite size, readability, and real fault detection ability. We discuss about the benefits and drawbacks of hybrid techniques based on experimental results. Besides, we introduce our experience in setting up and executing these tools, and summarize their usability and user-friendliness. Finally, we give some insights into automated tools in terms of test suite readability improvement, meaningful assertion generation, test suite reduction for random testing tools, and symbolic execution integration. [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 primary reasons for the rise in bad loans are asymmetric information, poor monitoring of projects, and a lack of detailed profile evaluations of borrowers. Hence, it is necessary to improve the quality of information available to the lenders for monitoring both the project as well as profile details of the borrowers. The present study describes the usability of a blockchain-secured system (B-Bank™) for easily availing loans along with secured loan repayments, wherein the complete inventory, cash flow, and project progress can be monitored on a real-time basis. B-Bank™ enables the optimisation of credit risk capital and empowers economic development. Pilot testing of the software also indicated a high degree of construct, usability, and reliability. Hence, this tool complies optimally at both theoretical as well as practical levels to bring about a positive change for loan borrowers and lenders along with ample opportunity for the government to gain tax through the GST payments. [ABSTRACT FROM AUTHOR]

In this study, the notion of composite materials is thoroughly assessed. Actual and simulation in a specific computer software stress testing of hybrid composites are investigated. The paper deals with the mechanics of rigid bodies, their elasticity, strength, and stiffness. In addition to a general overview of the former's behavior and properties, this paper presents the possibility of calculating the bearing capacity of various materials in relevant computer programs. The production and testing process of the composite samples are described. The latter are then subjected to simulated tests in computer software. The main objective of this study is to compare real test results of hybrid composites, namely combined carbon fibers, glass fibers, aramid-carbon fibers, aramid honeycomb, and metal mesh with the simulation findings. [ABSTRACT FROM AUTHOR]

The increasing use of solar energy for electricity production has led to a directly proportional growth in the production of solar cells. Photovoltaic (PV) performance of silicon solar cells can be improved by using more efficient technologies, optimizing processes, and changing behavior in order to reduce operational costs and greenhouse gas emissions. In order to propose solutions for commercial solar cell production with better performance, this article presents an experimental assessment on Supplementary Oxide Layers (SOLs) that are deposited on the surface of a solar cell absorber layer. SOLs are typically used to improve the performance of solar cells by passivating surface defects, reducing recombination losses, and improving the electrical contact between the absorber layer and the metal electrodes. The obtained solar cells are tested under natural sunlight conditions, following a variable dynamic electronic charge profile. The experimental results along with the corresponding I-V and P-V curves, are assessed according to the process parameters, the lighting parameters, and the dynamic load scenario. SOLs have been shown to improve the Power Conversion Efficiency (PCE) of solar cells considerably. The proposed method for increasing the energy efficiency of solar cells can be applied to any type of commercial solar cell and it is easy to implement at the industrial or research level by controlling process parameters. The integration of the whole process, i.e. development of precursor solutions, deposition of thin films, and testing of electrical properties is another contribution of the current study, along with its interdisciplinary character, which involves materials science, electronics, and software programming. [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]

The Software Defect Prediction (SDP) process provides reliable software by identifying defect-prone modules before the testing stage. It efficiently and effectively utilizes quality assurance resources. Most predictive models are trained on historical data which belong to the same project or comparable project. These models show satisfactory performance as they utilize similar settings to the considered projects. But the limitation of these models is that they are effective only if there are adequate historical data to train a predictive model. In reality, however, such historical data are minimal for some projects and absent for new projects. The defect prediction in such projects which lack historical data can be accomplished by training prediction models on different project data. This process is known as Cross-Project Defect Prediction (CPDP). Software defect datasets also suffered from class imbalance issues which further degrades the model's performance. In this research work, the authors have proposed a Multi-Objective Random Forest (MO-RF) algorithm with a data resampling technique to minimize the probability of false alarms, to maximize the probability of detection and to overcome the class imbalance problem. The study also evaluates the performance of other prediction models. The proposed method has shown percentage improvement (in terms of AUC) of 2.78 and 3.46 over MONB and MONBNN, respectively. [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]

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]

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]

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 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]

In order to investigate user's innovative demand for the functionality of bed frame backrests, based on the technology acceptance model (TAM), four external variables were introduced to construct a research model of the willingness to use a new functional bed frame backrest and 11 hypotheses were put forward. The questionnaire survey was conducted on 193 participants aged 20 -- 60 years old, and the reliability and validity of the data were examined. Subsequently, the data were analyzed using the AMOS software in combination with the structural equation modeling for model analysis and hypothesis testing. The results show that the functional effectiveness, perceived comfort, perceived risk, and aesthetic design of the new backrest has a significant positive effect on the attitude toward use, and the attitude toward use positively affects the willingness to use. Improving the functionality and perceived comfort of the new backrests and reducing the perceived risks can increase user willingness. At the same time, the design should consider the age of users and pay attention to the style needs of consumers. [ABSTRACT FROM AUTHOR]