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Software testing is an efficient way to guarantee the reliability and accuracy of parallel programs. Communication plays a substantial role in parallel program testing. The huge scale of communication within parallel programs poses a great challenge to coverage testing. How to reasonably select the communication to be tested and effectively generate test data to meet the requirements of coverage is a pressing problem. In this paper, a critical communication testing method based on branch transformation is proposed, which can reasonably select part of program communication and generate test data. To be specific, firstly, this paper analyzes the features which easily lead to defects in communication, and then proposes the evaluation method of critical communication based on the above features. Next, we select the critical communication and convert the communication coverage to the coverage of the branch where the communication statement resides. Finally, a method based on genetic algorithm is used to solve the communication coverage problem in different scenarios. The proposed method is applied to the testing of several representative parallel programs. Experimental results show that compared with other methods, this proposed method can effectively reduce the quantity of communication, significantly improve the coverage rate of communication coverage testing and increase the efficiency of test data generation. [ABSTRACT FROM AUTHOR]

We report the effectiveness of using the tablet-game Kalulu Phonics immediately after intervention in kindergarten and on national evaluations the year after. In a previous intervention testing the software with 1st graders, fluency and comprehension were boosted, but only when used in concert with reading instruction at the start of the year. Here, we asked whether a similar intervention would be more efficient if it started a year earlier, in kindergarten. Forty classes (1092=children) were randomized into playing Kalulu Phonics or the matched Kalulu Numbers control game for the first half of the year, with reversed assignments in the second half. Ten non-randomized business-as-usual classes also participated. In a cross-over effect, children who used the phonics version improved in letter naming, grapheme-phoneme correspondence (GPC) matching and fluency. Students with the number version improved in number knowledge. In a longitudinal follow-up, all intervention participants maintained an advantage in phoneme awareness and GPC matching at the start of 1st grade, but this advantage failed to translate into school literacy gains by mid-year. No longitudinal benefits were found for numbers. Our results support using tablet-based aids in spurring early reading skills but question the possibility that a short-term intervention may address the challenges of long-term educational goals. [ABSTRACT FROM AUTHOR]

Keldysh nonequilibrium diagram technique is used for a detailed description of electron transport through a system of tightly bound spatially separated localized states. Particular attention is paid to the predictive ability of the effective theory, for which the number of free parameters in the second-quantized Hamiltonian is reduced. The employed formalism in a variant of tight-binding model rigorously treats multi-electron tunneling effects and the discreteness of the energy spectra in low-dimensional structures. The model is also extended with Coulomb interaction of moderate strength. Proposed algorithm for calculating electric currents and occupation numbers of localized states in presence of weak Coulomb repulsion is implemented in software and tested on a model system of two consecutive three-level quantum dots. Electron transfer proceeds both sequentially between the dots and in parallel within each quantum dot. The calculated IV-curve and current stability diagram are intuitively explained using coordination energy diagrams. The model qualitatively reproduces typical effects in interference-based nanoelectronics: resonant tunneling, negative differential resistance, population inversion in a multi-level system. The impact of moderate Coulomb correlations on current stability diagram is examined. This demonstrates the capability of extended tight-binding model to incorporate weak Coulomb interaction into the description of electronic transport via discrete quantum states. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Gamification is an established practice in Software Engineering to increase effectiveness and engagement in many practices. This manuscript provides a characterization of the application of gamification to the Software Testing area. Such practice in fact reportedly suffers from low engagement by both personnel in industrial contexts and learners in educational contexts. Our goal is to identify the application areas and utilized gamified techniques and mechanics, the provided benefits and drawbacks, as well as the open challenges in the field. To this purpose, we conducted a Multivocal Literature Review to identify white and grey literature sources addressing gamified software testing. We analyzed 73 contributions and summarized the most common gamified mechanics, concepts, tools, and domains where they are mostly applied. We conclude that gamification in software testing is mostly applied to the test creation phase with simple white-box unit or mutation testing tools and is mostly used to foster good behaviors by promoting the testers' accomplishment. Key research areas and main challenges in the field are: careful design of tailored gamified mechanics for specific testing techniques; the need for technological improvements to enable crowdsourcing, cooperation, and concurrency; the necessity for empirical and largescale evaluation of the benefits delivered by gamification mechanics. [ABSTRACT FROM AUTHOR]

Context: Software regression testing refers to rerunning test cases after the system under test is modified, ascertaining that the changes have not (re-)introduced failures. Not all researchers' approaches consider applicability and scalability concerns, and not many have produced an impact in practice. Objective: One goal is to investigate industrial relevance and applicability of proposed approaches. Another is providing a live review, open to continuous updates by the community. Method: A systematic review of regression testing studies that are clearly motivated by or validated against industrial relevance and applicability is conducted. It is complemented by follow-up surveys with authors of the selected papers and 23 practitioners. Results: A set of 79 primary studies published between 2016-2022 is collected and classified according to approaches and metrics. Aspects relative to their relevance and impact are discussed, also based on their authors' feedback. All the data are made available from the live repository that accompanies the study. Conclusions: While widely motivated by industrial relevance and applicability, not many approaches are evaluated in industrial or large-scale open-source systems, and even fewer approaches have been adopted in practice. Some challenges hindering the implementation of relevant approaches are synthesized, also based on the practitioners' feedback. [ABSTRACT FROM AUTHOR]

An essential attribute of software quality is software reliability. To achieve higher reliability, the testing phase with detected and corrected flaws is incorporated in the software development. The fault correction process (FCP) includes the fault detection process (FDP) to develop the software reliability growth model (SRGM). This is difficult to integrate because due to several reasons, including the effects of staffing levels and the interdependence of faults. It limits the applicability of the analytical model. Because of the adoption of data-driven methodologies such as Artificial Intelligence (AI) technology, no precise FCP and FDP assumptions are necessary. In this article, we proposed a hybrid long short-term memory (LSTM) with BrainStorm Optimization and Late Acceptance Hill Climbing (BSO-LAHC) algorithm of a stepwise prediction model for software fault detection and correction. The fault detection and correction procedure has great influence by considering the testing effort. While compared to the existing methods, the proposed hybrid with the BSO-LAHC algorithm demonstrated superior results by using Firefox and bug tracking system Bugzilla datasets. The proposed model's effectiveness is confirmed via empirical study. Based on the Bugzilla and firefox datasets, the proposed mean square error performance is 1.92 and 21.44 respectively. Additionally, the proposed method is less expensive and takes less time to execute. In Bugzilla version 5.0.4, releases 2 and 3 had a determination coefficient of 99.2% and 98.9%, respectively. The FCP is 27% more effective than previous approaches, and the FDP is 32% more effective. [ABSTRACT FROM AUTHOR]

One of the significant features of software quality is software reliability. In the testing phase, faults are identified and corrected by integrating them into software development, thus obtaining better reliability. Here, by utilizing the Elliptical Distributions-centric Emperor Penguins Colony Algorithm (ED-EPCA)-based Test Case Prioritization (TCP), an effectual Fault Detection (FD) technique is proposed using Fishers Yates Shuffled Shepherd Optimization Algorithm (FY-SSOA)-based Test Case Selection (TCS). Initially, for the incoming source code, the Test Case (TC) is created. Then, the significant factors needed for TCS and prioritization are identified. Next, by utilizing the Log Scaling-centered Generalized Discriminant Analysis (LS-GDA) model, the estimated factors are abated further to enhance the TCS along with prioritization for the Fault Detection Process (FDP). Then, using the FY-SSOA, the optimized TCs are selected. Subsequently, with the help of ED-EPCA, the TCs being selected are ranked as well as prioritized. Finally, to validate the proposed system's effectiveness, the model's performance is evaluated in the working platform of Java and analogized with the traditional methodologies. The results indicate that the test case prioritization-based fault detection method is robust with a 99.23% fault detection rate and a small amount of memory usage, which is only 8245475 kb by generating a large number of test cases. [ABSTRACT FROM AUTHOR]

Software defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting. [ABSTRACT FROM AUTHOR]

Summary: Combinatorial testing (CT) is considered as a practical approach to detect software faults, which has arisen from the interaction between factors affecting the software behavior. However, most of the traditional algorithms on CT generation did not take advantage of the execution results of the earlier test cases, as well as neglect the impact of the nonequilibrium input parameter model (NE‐IPM) effect on redundant test cases, which bring a deleterious effect to the detection accuracy of the software faults. To solve these problems, we propose a novel CT approach with fuzzing strategy called CTAF. Based on the idea that fuzzing is performed during execution, CTAF exploits the execution results of earlier tests to provide guidance for subsequent test generation thereby reducing the redundant test cases without compromising the diversity of test cases. And then, we designed three experiments on real subjects of six open source software systems, and the experimental results show that the proposed CTAF approach can effectively improve the NE‐IPM effect and enhance the detection accuracy of software faults. [ABSTRACT FROM AUTHOR]

ESA Solar Orbiter was launched from Cape Canaveral in collaboration with NASA on the February 10, 2020 to start its journey around the Sun, with the commissioning phase ending in April 2020. After commissioning, ESA set a tighter pointing requirement goal to further improve the performance in Fine Pointing Mode, in preparation of the upcoming start of Science Phase. The first part of this paper outlines the end-to-end process which allowed - in just six months – to update the Fine Pointing Mode Design, re-tune the controller with refined assumptions taken from flight data and robust methods and execute full validation of the new AOCS Software on the Avionics Test bench at Airbus Defence and Space. The second part of the paper describes the upload in flight of the new Software and the analysis of the on-orbit pointing performance, which showed a significant improvement with respect to the previous one, thus contributing to the success of the Mission's scientific goals. • Solar Orbiter Payload Instruments required a better pointing jitter performance (Relative Pointing Error) after launch • Root cause analysis on flight telemetry to identify driver contributors to pointing error • Fine Pointing Mode controller design and tuning updated with refined inputs based on flight data and with robust control methods • Controller uploaded in flight significantly improved the Spacecraft pointing performance [ABSTRACT FROM AUTHOR]

Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice’s 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches. [ABSTRACT FROM AUTHOR]

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

The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system. [ABSTRACT FROM AUTHOR]

Nowadays, automated guided vehicles (AGVs) play a key role in manufacturing systems because of improving system efficiency and lowering the cost of production. To increase the efficiency and stability of AGVs, it is crucial to consider maintenance planning for them. To the best of our knowledge, there are rarely found studies related to maintenance planning and AGVs' design and control. Accordingly, in this paper, a new integrated nonlinear mathematical model is developed for optimizing the AGV design (including AGV fleet sizing and AGV assignment to workshops) and preventive maintenance policy. The proposed model aims to determine the preventive maintenance cycles, the optimal number of employed AGVs in manufacturing, and the optimal assignment of AGVs to manufacturing workshops so that the total cost is minimized. To solve this model, a genetic algorithm (GA) is developed, and its performance is compared with the global solver of LINGO software on 15 test problems, some of which are large dimensions. To tune the GA parameters, a Taguchi method is used. Moreover, a sensitivity analysis is performed to represent the validity of the model and solution approach. The results have demonstrated the effectiveness of GA in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

Despite the tremendous development of artificial intelligence (AI)‐based mobile apps, they suffer from quality issues. Data‐driven AI software poses challenges for maintenance and quality assurance. Metamorphic testing has been successfully adopted to AI software. However, most previous studies require testers to manually identify metamorphic relations in an ad hoc and arbitrary manner, thereby encountering difficulties in reflecting real‐world usage scenarios. Previous work showed that information available in user reviews is effective for maintenance and testing tasks. Yet, there is a lack of studies leveraging reviews to facilitate AI function maintenance and testing activities. This paper proposes METUR, a novel approach to supporting maintenance and testing for AI functions based on reviews. Firstly, METUR automatically classifies reviews that can be exploited for supporting AI function maintenance and evolution activities. Then, it identifies test contexts from reviews in the usage scenario category. METUR instantiates the metamorphic relation pattern for deriving concrete metamorphic relations based on test contexts. The follow‐up test dataset is constructed for conducting metamorphic testing. Empirical studies on plant identification apps indicate that METUR effectively categorizes reviews that are related to AI functions. METUR is feasible and effective in detecting inconsistent behaviors by using the metamorphic relations constructed based on reviews. [ABSTRACT FROM AUTHOR]

Software testing is an essential phase in the software development life cycle. One of the important types of software testing is unit testing and its execution is time-consuming and costly. Using parallelization to speed up the testing execution is beneficial and productive for programmers. To parallelize test execution, researchers can use GPU machines. In GPU applications, multiple threads execute in parallel within a group known as a warp. Branch divergence affects the performance of a warp negatively when some threads run a branch, and the other threads are idle waiting for the first set of threads to finish their execution. In this paper, we propose a novel algorithm to minimize branch divergence when testing an application on a GPU. We arrange test inputs based on the warp size of a GPU machine. Test inputs that have similar control flow paths are grouped within the same warp executing in parallel. Thus, the branch divergence is minimized per warp. We validate and evaluate our algorithm on six benchmarks (57 programs in total). Our approach accelerates the testing execution by up to 3.8x and improves the warp execution efficiency by up to 15x. [ABSTRACT FROM AUTHOR]

Background: HLA antibody testing is essential for successful solid-organ allocation, patient monitoring post-transplant, and risk assessment for both solid-organ and hematopoietic transplant patients. Luminex solid-phase testing is the most common method for identifying HLA antibody specificities, making it one of the most complex immunoassays as each panel contains over 90 specificities for both HLA class I and HLA class II with most of the analysis being performed manually in the vendor-provided software. Principal component analysis (PCA), used in machine learning, is a feature extraction method often utilized to assess data with many variables. Methods & findings: In our study, solid organ transplant patients who exhibited HLA donor-specific antibodies (DSAs) were used to characterize the utility of PCA-derived analysis when compared to a control group of post-transplant and pre-transplant patients. ROC analysis was utilized to determine a potential threshold for the PCA-derived analysis that would indicate a significant change in a patient's single antigen bead pattern. To evaluate if the algorithm could identify differences in patterns on HLA class I and HLA class II single antigen bead results using the optimized threshold, HLA antibody test results were analyzed using PCA-derived analysis and compared to the clinical results for each patient sample. The PCA-derived algorithm had a sensitivity of 100% (95% CI, 73.54%-100%), a specificity of 75% (95% CI, 56.30%-92.54%), with a PPV of 65% (95% CI, 52.50%-83.90%) and an NPV of 100%, in identifying new reactivity that differed from the patients historic HLA antibody pattern. Additionally, PCA-derived analysis was utilized to assess the potential over-reactivity of single antigen beads for both HLA class I and HLA class II antibody panels. This assessment of antibody results identified several beads in both the HLA class I and HLA class II antibody panel which exhibit over reactivity from 2018 to the present time. Conclusions: PCA-derived analysis would be ideal to help automatically identify patient samples that have an HLA antibody pattern of reactivity consistent with their history and those which exhibit changes in their antibody patterns which could include donor-specific antibodies, de novo HLA antibodies, and assay interference. A similar method could also be applied to evaluate the over-reactivity of beads in the HLA solid phase assays which would be beneficial for lot comparisons and instructive for transplant centers to better understand which beads are more prone to exhibiting over-reactivity and impact patient care. [ABSTRACT FROM AUTHOR]

The object of research in the article is the process of testing and operating software with cost minimization. In the Software Development Life Cycle, depending on the chosen option of the flexible methodology, special attention is focused on testing software versions both in the process of passing iterations and in the process of releasing alpha, beta and production versions. This article is devoted to the problem of developing a method for software testing cost optimization method that estimates the test cost function and the losses cost function from the occurrence of an error. Using the optimization method (for example, the first-order descent method) from the two functions of testing costs and estimating the losses caused during operation, it is possible to calculate the optimal cost of testing and operating the software product. The results obtained show that with the correct assessment of a cost function and a loss function such calculations allow to significantly save money and time for the production of the next version of the software product. These results are explained by the fact that the method of optimizing the cost function finds the optimum point and allows to pre-estimate the budget and risks during the development and operation of the software. The article provides several examples of the calculation and optimization of testing costs within the proposed concept for one iteration in a flexible software development cycle. The results of the study can be used in practice, provided that the functions of estimating costs for testing and compensation for losses caused during the operation of the software are set correctly. Experienced managers and project supervisors determine these functions quite accurately for a certain number of iterations, which makes it possible to apply the method of finding the minimum budget costs for testing and operating a software product. [ABSTRACT FROM AUTHOR]

This work proposes a systematic assessment of measuring type A uncertainty (caused by random errors) used in RF power sensor calibration. To reduce A type uncertainty, several successive measurements are repeated. The uncertainty arises from repeatability errors in connectors caused by changes in their electrical properties during repeated mating. The suitability of the METAS UncLib software was analysed and we concluded that software should be developed to take into account the shape of probability density function (PDF) using a Monte Carlo method (MCM), which was lacking in METAS UncLib. The self-developed software was then tested on an example taken from the literature and the superiority of the MCM over the analytical method (GUM) was confirmed. During the calibration of the RF sensor using a vector network analyzer (VNA), a series of repeated measurements were performed and, after applying our MCM software, it was found that the measurement uncertainties calculated by the MCM method were several times larger than those by the GUM. The reason for this was that the correlation between the measured input quantities was not taken into account. When this was done using a covariance matrix and assuming a normal PDF of the input quantities, the results obtained with the GUM and the MCM converged. Our main objective was to investigate the influence of the PDF shape of the input measurement samples on the measurement uncertainty. Taking more than a dozen measurements is too costly, on the other hand, the small sample size prevents a reliable determination of the PDF shape. Finally, to overcome this inconvenience, we have developed a special method that uses the histograms of standardized input data taken at all measurement frequencies under fixed conditions without disconnecting the connectors, to increasing the total number of results which were needed to create the PDF histograms of input quantities. [ABSTRACT FROM AUTHOR]

In view of the actual situation of multi-wing fracture off-center wells, the mathematical model for the wells was established. Based on the Laplace transform and the pressure drop superposition principle, the semi-analytical solution of the bottom hole pressure in the multi-wing fracture off-center well in the Laplace space, was obtained. The semi-analytical solution was discretized with the non-uniform flow method. Combined with Stehfest numerical inversion, the numerical solution of the real space bottom hole pressure and the production distribution were obtained. The numerical well test model for the reservoir was established with the SAPHIR well test analysis software, and the numerical discrete calculation was carried out. The numerical results were compared with the calculation results of the semi-analytical model, which verifies the correctness of the semi-analytical model. The results show that, the bottom hole pressure variation of the multi-wing fracture off-center well can be divided into 8 main flow stages. Finally, the effects of the dimensionless conductivity, the fracture asymmetry factor and the off-center distance on the bottom hole pressure variation and production distribution characteristics, were discussed. [ABSTRACT FROM AUTHOR]

In software development, improving the efficiency of a testing process is important to ensure reliability during a limited development period. One approach to improving the testing process is identifying modules that are likely to contain faults (fault‐prone modules) and allocate effort toward resolving them. For this purpose, many fault‐prone module detection models have been proposed in previous studies. However, an appropriate fault‐prone module detection model cannot be constructed if outliers, such as modules with a significantly large number of source code lines and branches, but no faults, are included. In this study, we propose a new outlier elimination technique that creates missing values (deleted values) to complete the data artificially and applies a missing value imputation technique using a regression approach. If the imputed value differs significantly from the actual (recorded) value, the proposed technique treats the values as outliers. We name the proposed technique Outlier Elimination Technique using Deletion‐Imputation Iteration (OEdii), and its performance is verified experimentally. In general, our experimental results are more accurate than the previous outlier elimination techniques in the area under the ROC curve. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Planar cellular structure panels are affected by a series of factors, which can produce various types of initial damage within the structure and thus affect the service life as well as the starting point and path of crack propagation. Investigating the effect of different initial damage modes on crack expansion paths and failure modes in additively manufactured honeycomb cellular panel under quasi-static three-point bending load. Experimental tests and finite element simulations are used. The quasi-static three-point bending experiments are conducted by using the CMT5305 universal testing machine, and numerical simulations are carried out by combining finite element analysis software ANSYS Workbench. Two kinds of experimental specimens with prefabricated cracks (PFC) and plastic hinge truncation (PHT) are fabricated by using additive manufacturing technology to simulate the initial damage. The final failure of the specimens includes plastic failure dominated by type-I cracks and brittle failure dominated by type-I and type-II mixed cracks. T-0-60 and C-90-60 specimens have the strongest crack suppression and load-bearing ability. With the increase in load speed, the cracking displacement decreases, and peak load increases. The undamaged (UDD) specimens have the strongest crack suppression ability at loading speeds of 10mm/min and 50mm/min, while the damage mode has little effect on the peak load of the specimens. The damage mode affects the crack initiation point, propagation path and failure mode of the cellular structure, and the model with initial damage will enhance the crack suppression ability at a specific inclination angle. In practical engineering applications, as a load-bearing structure, such as the open beam of a truck, two types of structures, T-0-60, and C-90-60, should be given priority, while avoiding the design of C-0-60 structure. As an energy-absorbing structure, such as the front bumper of a car, the cushioning effect should be enhanced by adjusting the hole direction and the initial damage direction. [ABSTRACT FROM AUTHOR]

The High Temperature Test Facility (HTTF) at Oregon State University is an integral system test facility to simulate postulated reactor transients of prismatic high-temperature gas-cooled reactors(HTGRs). A series of test campaigns was launched, providing abundant test data that could be used to benchmark reactor system analysis codes like the System Analysis Module (SAM). In this study, a SAM model of the facility is developed based on the two-dimensional (2D) ring model approach. All components including the ceramic matrix, graphite heaters, helium coolant channels, core barrel, upcomer, pressure vessel, and reactor cavity cooling system are modeled as concentric cylindrical rings. The model is used to simulate one of the benchmark problems—Pressurized Conduction Cooldown (PCC)—within the scope of the Organisation for Economic Co-operation and Development Nuclear Energy Agency International HTTF Benchmark. The simulations consist of two parts. In the first part, operating and boundary conditions as well as thermophysical properties of materials are specified for the benchmark problem. Results from the first part will be used in code-to-code comparison. In the second part, the SAM model is used to simulate Test PG-27, which is the first PCC test carried out in the HTTF, with only two of the ten heater banks activated. The results in the second part are used for code-to-data comparison. Because the helium coolant flow rate is not measured in this facility, it is estimated using the input power and inlet/outlet coolant temperatures. Additionally, radial heat flow in the ceramic blocks is complicated by hundreds of cylindrical coolant channels and heater rods embedded in them. As such, it is necessary to deduce an effective thermal conductivity for the ceramic to analyze the core thermal behavior. SAM predictions of the helium coolant and ceramic temperatures are compared with test data measured in three equivalent sectors. Overall, the SAM results agree reasonably well with test data within the variation of data among the three sectors, which demonstrates SAM's capability in capturing transient effects in HTGR using the simplified 2D ring model. [ABSTRACT FROM AUTHOR]

Cyber risk profoundly affects all. In the context of cyber threats, malware is trending in various productive sectors. Nowadays, anti-malware is essential to combat cyber threats; however, their efficiency is often questioned, because malware is different for different regions in the world. Choosing an efficient anti-malware software solution is crucial to protect information from different institutions. The method confirmed the reality of evaluating the different known methodologies, showing another scenario of the efficiency of the different testers. The method allowed visualizing an interesting panorama because 50% of malware collected on the Brazilian Internet was detected by anti-malware commercially available in Brazil. [ABSTRACT FROM AUTHOR]

A type of software testing, regression testing is often costly and labour-intensive. As such, multiple corporations have intensified efforts to estimate the amount of effort required. However, frequent alterations in software projects impact the precision of software regression test effort estimation (SRTEE), which increases the difficulty of managing software projects. Therefore, machine learning (ML) has increasingly been used to develop more accurate SRTEEs. The estimation process of a software project comprises inputs, the model, and outputs. This present study examines the quality of estimation inputs and the model required to deliver accurate estimation outputs. An SRTEE that uses the stacking ensemble model (StackSRTEE) was developed to increase the precision of SRTEE. It consisted of the three most common ML methods, namely neural networks, support vector regression, and decision tree regression. The grid search (GS) technique was then used to tune the hyperparameters of the StackSRTEE before it was trained and tested using a dataset from the International Software Benchmarking Standards Group (ISBSG) repository. The size of the functional change; specifically, enhancement; was used as the primary independent variable to improve the inputs of the StackSRTEE model. With the appropriate features; such as the functional change size of an enhancement; (1) the proposed StackSRTEE model yielded higher accuracy than the three individual ML methods on their own, (2) using GS to tune and set the individual ML methods increased the precision of the SRTEE outputs, and (3) the StackSRTEE-based GS tuning yielded estimations that were more precise. [ABSTRACT FROM AUTHOR]

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

Background: Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based auto-segmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT. Methods: The Autolung3D software was tested on the retrospective SPECT/CT data of 43 patients who underwent V/Q scintigraphy with 99mTc-macroaggregated albumin and 99mTc-labeled aerosol. It was compared to planar scintigraphy and SPECT/CT using the manual quantification method in terms of relative lobar V/Q quantification values and interobserver variability. Results: The three methods provided similar V/Q estimates for the left lung lobes and total lungs. However, compared to the manual SPECT/CT method, planar scintigraphy yielded significantly higher estimates for the middle right lobe and significantly lower estimates for the superior and inferior right lobes. The estimates of the manual and automated SPECT/CT methods were similar. However, the post-processing time in the automated method was approximately 5 min compared to 2 h for the manual method. Moreover, the automated method associated with a drastic reduction in interobserver variability: Its maximal relative standard deviation was only 5%, compared to 23% for planar scintigraphy and 19% for the manual SPECT/CT method. Conclusions: This study validated the AutoLung3D software for general clinical use since it rapidly provides accurate lobar quantification in V/Q scans with markedly less interobserver variability than planar scintigraphy or the manual SPECT/CT method. [ABSTRACT FROM AUTHOR]

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

The software testing phase usually consumes a lot of the development of software projects time in order to find defects before release. Different strategies have been approached to optimize this phase of the testing stage. Metaheuristics are important in software testing due to their ability to find optimal or near-optimal solutions in complex situations. This research aims to analyze the current status of the application of metaheuristics that assist in software testing phase activities, specifically the most representative Non-bio-inspired algorithms (NBA) are surveyed, being Hill Climbing the most reported. The main activities of the software testing where NBA were implemented, were test case and test data generation and test case prioritization. It was concluded that NBAs used on their own are only viable in some activities of the software testing phase. As future work, it is proposed to investigate the use of hybrid algorithms and approaches in software testing phase. [ABSTRACT FROM AUTHOR]