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Effort‐Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best‐performing CorBF method in CBDP to pre‐process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state‐of‐the‐art EADP model (CBS+) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper‐based feature subset selection methods with forwards search, that is, AdaBoost with Forwards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS+ performs the best on the datasets. (3) The best‐performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper‐based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest, and XGBoost as the base classifiers embedded in CBS+ can achieve the best performance. In summary, we recommend the software testing team should employ XGBF with XGBoost as the embedded classifier in CBS+ to enhance the EADP performance. [ABSTRACT FROM AUTHOR]

Software testing is a very important part of the software development life cycle to develop reliable and bug-free software but it consumes a lot of resources like development time, cost, and effort. Researchers have developed many techniques to get prior knowledge of fault-prone modules so that testing time and cost can be reduced. In this research article, a hybrid approach of distance-based pruned classification and regression tree (CART) and k- nearest neighbors is proposed to improve the performance of software fault prediction. The proposed technique is tested on eleven medium to large scale software fault prediction datasets and performance is compared with decision tree classifier, SVM and its three variations, random forest, KNN, and classification and regression tree. Four performance metrics are used for comparison purposes that are accuracy, precision, recall, and f1-score. Results show that our proposed approach gives better performance for accuracy, precision, and f1-score performance metrics. The second experiment shows a significant amount of running time improvement over the standard k-nearest neighbor algorithm. [ABSTRACT FROM AUTHOR]

The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate and compare the fracture resistance of temporary restorations made of polymethylmethacrylate (PMMA), graphene-modified PMMA (GRA), acetal resin (AR) and polysulfone (PS) obtained by a subtractive technique (milling) using a computer-aided design and manufacturing (CAD/CAM) system of a three-unit fixed dental prosthesis (FDP). Methods: Four groups of ten samples were fabricated for each material. Each specimen was characterized by a compression test on a universal testing machine, all specimens were loaded to fracture and the value in Newtons (N) was recorded by software connected to the testing machine. The fracture mode was evaluated on all samples using a stereomicroscope. Results: There were statistically significant differences (p value < 0.005) between PMMA and the other three materials (PMMA: 1302.71 N; GRA: 1990.02 N; RA: 1796.20 N; PS: 2234.97). PMMA presented a significantly lower value than the other materials, and PS showed the highest value. GRA and RA presented a similar range of values but they were still higher than those of PMMA. Conclusions: GRA, RA and PS are presented as valid options within the range of interim milled restorative materials and as alternatives to PMMA. [ABSTRACT FROM AUTHOR]

At present, the existing scaffold materials cannot well meet the high strength and toughness requirements for the repairing of typical stressed tissues. And the commercial software for 3D printing scaffold modeling and mechanical performance simulation is complex to operate and has poor interoperability with 3D printers. In order to solve these problems, a silk fibroin (SF) based bio-ink was prepared for tough scaffold, and a software was developed for compression performance simulation of 3D printing scaffold based on SF and finite element analysis strategy. The printability of the bio-ink and the mechanical properties of the corresponding hydrogels and 3D printing scaffolds were characterized. The cell compatibility of the related scaffolds was evaluated by using fibroblasts and living/dead staining kit. Based on the SF based bio-ink, scaffolds with different heights and porosities were designed and printed. By using the developed software and the universal testing machine, the compression performance of related scaffolds was simulated and tested separately, and further compared with each other. Results show that the SF based bio-ink has good printability. And the 3D printing scaffold prepared by this ink presents high strength and toughness (modulus of elasticity: (1.86 ± 0.28)MPa, modulus of compression: (1.95 ± 0.11)MPa, elongation at break: (114.03 ± 14.40)%, compression strain ≥ 70%). In addition, the scaffold has good cell compatibility (L929 cell viability≥92.4%), which is expected to be used for the repair and regeneration of stressed tissues. The software is easy to operate, and can be used not only for the modeling of 3D printing scaffolds, but also for accurately predicting their compressive mechanical properties. In addition, the model data can also be directly imported into the 3D printer to guide the efficient preparation of the corresponding 3D printing scaffolds. This research is expected to provide important guidance for the rapid design and fabrication of tissue specific 3D printing scaffolds with bionic structure and mechanical properties. [ABSTRACT FROM AUTHOR]

Tests, when not correctly implemented, can pass on incorrect system implementations rather than fail. In this case, they are named silent horrors or false‐negative tests. They make releasing low‐quality (buggy) versions of the software system more probable. Furthermore, faithfully implementing test specifications is crucial when they play the role of documentation, like when documenting components or services or driving legacy systems' re‐engineering. This paper presents TestWizard, a novel approach and tool for automatically assessing individual tests' quality from the point of view of their coherence to specifications. TestWizard automatically assesses the quality of each individual test case w.r.t. its specification, providing detailed reports on why a single test is a false negative, hence helping testers fix them. Thus, TestWizard can help to automate the test code review process, which is still mainly manual today. The analysis of 1012 test implementations, developed by 123 students in three experiments, shows that TestWizard is (1) by far more accurate than code review performed by multiple students, (2) slightly better than code review performed by three senior experts, and (3) always able to detect a significant percentage of false‐negative test methods (up to 21.22%). [ABSTRACT FROM AUTHOR]

Quantum computing is expected to exponentially outperform classic computing on a broad set of problems, including encryption, machine learning, and simulations. It has an impact yet to explore on all software lifecycle's processes and techniques. Testing quantum software raises a significant number of challenges due to the unique properties of quantum physics—such as superposition and entanglementand the stochastic behavior of quantum systems. It is, therefore, an open research issue. In this work, we offer a systematic mapping study of quantum software testing engineering, presenting a comprehensive view of the current state of the art. The main identified trends in testing techniques are (1) the statistic approaches based on repeated measurements and (2) the use of Hoare‐like logics to reason about software correctness. Another relevant line of research is reversible circuit testing, which is partially applicable to quantum software unitary testing. Finally, we have observed a flourishing of secondary studies and frameworks supporting testing processes from 2018 onwards. [ABSTRACT FROM AUTHOR]

Microservice‐based applications consist of multiple services that can evolve independently. When a service must be updated, it is first tested with in‐house regression test suites. However, the test suites that are executed are usually designed without the exact knowledge about how the services will be accessed and used in the field; therefore, they may easily miss relevant test scenarios, failing to prevent the deployment of faulty services. To address this problem, we introduce ExVivoMicroTest, an approach that analyzes the execution of deployed services at run‐time in the field, in order to generate test cases for future versions of the same services. ExVivoMicroTest implements lightweight monitoring and tracing capabilities, to inexpensively record executions that can be later turned into regression test cases that capture how services are used in the field. To prevent accumulating an excessive number of test cases, ExVivoMicroTest uses a test coverage model that can discriminate the recorded executions between the ones that are worth to be turned into test cases and the ones that should be discarded. The resulting test cases use a mocked environment that fully isolates the service under test from the rest of the system to faithfully reply interactions. We assessed ExVivoMicroTest with the PiggyMetrics and Train Ticket open source microservice applications and studied how different configurations of the monitoring and tracing logic impact on the capability to generate test cases. [ABSTRACT FROM AUTHOR]

The letter to the editor in the Journal of General Medicine discusses the implementation of practice management software tests. The authors argue that doctors should conduct the tests, while IT technicians should be responsible for the installation of the practice. They emphasize the importance of the consent of the general practitioner's office and digital competence for participation in the tests. The authors suggest that the tests should be conducted in selected practices to consider different practice installations and hardware. They also emphasize the need for hardware and practice installation to be reviewed by PVS technicians in case of frequent program crashes. [Extracted from the article]

Background: Impaired taste perception has great impact on quality of life; kharra is more pouplar in vidarbh region in young and middle age group population. Tobacco is the integral component of of kharra leads to impaired taste and dependency. The change mainly takes place in sensory organs which manifest in oral cavity. Alteration in taste perception is important as it has impact on diet and general health. Material and methods: 100 subjects [50kharra eaters and 50 non-eaters] were included in the study for taste perception; it was tested by using various molar solutions for five basics modalities of tastes perception like sweet, sour, salty, bitter and Umami. The taste perception was identified and compared between kharra eaters and non-eaters. The data was analysed by using paired t test by software Epi Info-7. Results: A statistically significant increase in taste perception was found for sweet, sour, salty, bitter and Umami in kharra eaters as compare to non-eaters where P>0.01. Conclusion: The results of the study revealed noticeable decrease in taste perception for sweet, sour, salty, bitter and Umami in kharra eaters when compared with kharra non-eaters. [ABSTRACT FROM AUTHOR]

Volunteer activities are now no stranger to sports in Malaysia. Various programs have been organized to attract people to participate as volunteers. Organized sports programs require the devotion of many volunteers to facilitate and ensure the program is smooth. In addition, sport risk management is important for all parties to ensure the sports tournament runs smoothly. This study aims to analyze the volunteer's involvement in the national volunteer program and to evaluate the main factors of volunteer involvement as well as suggest risk management for the national sports program. 375 volunteers for the Southeast Asian Games 2017 in Kuala Lumpur were selected as the main respondents in this research. The questionnaire method modified from the 'Volunteer Function Inventory' (VFI) was then analyzed using SPSS software and correlation tests were used in this study. The results found that youth aged 21 to 30 years are more likely to engage in volunteer, while those who have a career are more likely involved in volunteer activities. The understanding factor is the main factor of volunteer's involvement in the national volunteer program with the highest mean of 4.32. The correlation test shows the relationship between understanding factor and risk management which is r =0.52. In addition, risk management recommendations in the sports programs ensure that international sports tournament avoided accidents that could affect the ongoing tournament. Therefore, the SEA Games is a prestigious sports tournament, volunteers and sports risk management need to be emphasized so that the sports tournament can be implemented successfully. [ABSTRACT FROM AUTHOR]

The heating, ventilation, and air conditioning (HVAC) system is one of the most complex parts in a building design, which requires a high level of specialty to interpret and analyze during building energy modeling. From an energy simulation perspective, significant efforts are required in extracting HVAC information from the two-dimensional (2D) mechanical drawings or three-dimensional (3D) design models and manually inputting the data into the energy models. This tedious, error-prone, and time-consuming process has hindered high productivity in energy analysis. Automatically transforming HVAC data already contained in building information modeling (BIM) into building energy modeling (BEM) can significantly accelerate this process and improve efficiency during design iterations. To automate this process, the authors proposed a new algorithmic method by leveraging the state-of-the-art data-driven reverse engineering algorithm development (D-READ) method and the invariant signatures of HVAC objects. Following the proposed method, an algorithm was developed using a 2-story residential building model and a 2-story office building model. It successfully transformed HVAC data from BIM to EnergyPlus input file following the Industry Foundation Classes (IFC) standard. It was tested on two testing models with different HVAC systems [i.e., a 4-story office building model with a boiler radiator system and a 2-story clinic building with a variable air volume (VAV) system], which achieved 97.5% and 98.7% transformation accuracy compared with evaluation models manually created in commercially available software, respectively. Results also showed a satisfactory precision (<9.6% error) in total energy consumption by the algorithmically generated model when compared with the evaluation model. This work provides a new IFC-based approach to address the research gap of HVAC interoperability between BIM and BEM and supports better accessibility compared with a proprietary workflow. It builds a solid step for realizing seamless and fully-automated HVAC information transformation between BIM and BEM, for complete BIM-BEM interoperability. [ABSTRACT FROM AUTHOR]

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. [ABSTRACT FROM AUTHOR]

Automated testing in DevOps represents a key factor for providing fast release of new software features assuring quality delivery. In this paper, we introduce DOXAT, an automated framework for continuous development and testing of access control mechanisms based on the XACML standard. It leverages mutation analysis for the selection and assessment of the test strategies and provides automated facilities for test oracle definition, test execution, and results analysis, in order to speedup and automate the Plan, Code, Build, and Test phases of DevOps process. We show the usage of the framework during the planning and testing phases of the software development cycle of a PDP example. [ABSTRACT FROM AUTHOR]

To enter the production stage, in DevOps practices candidate software releases have to pass quality gates, where they are assessed to meet established target values for key indicators of interest. We believe software reliability should be an important such indicator, as it greatly contributes to the end‐user satisfaction. We propose DevOpRET, an approach for reliability testing as part of the acceptance testing stage in DevOps. DevOpRET relies on operational‐profile–based testing, a common reliability assessment technique. DevOpRET leverages usage and failure data monitored in operations to continuously refine its estimate. We evaluate accuracy and efficiency of DevOpRET through controlled experiments with a real‐world open source platform and with a microservice architectures benchmark. The results show that DevOpRET provides accurate and efficient estimates of the true reliability over subsequent DevOps cycles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel concept of using defect management technology to measure, predict, and optimize the management of the entire software project cycle. It first utilizes the software defect measurement GQIM model and prediction Gompertz model to track and quantify the defects of the entire project cycle. Then, the specific optimization strategy is proposed to manage the core processes of the project management, including project scope management, project time management, project quality management, project human resource management, project communication management, and project risk management. This study solves the quantification problems that are common in the current software project management process, such as complex testing procedures, high defect repair costs, and high communication costs. Furthermore, it contributes to promote the overall level of project management in the software industry. Taking the SDLM's electronic banking software project management as an example, the various management quality indicators before and after the optimization of the project management process strongly indicate that using software defect management technology to optimize the software project management process is feasible and effective. [ABSTRACT FROM AUTHOR]

Micrometre level ranging accuracy between satellites on-orbit relies on the high-precision calibration of the antenna phase center (APC), which is accomplished through properly designed calibration maneuvers batch estimation algorithms currently. However, the unmodeled perturbations of the space dynamic and sensor-induced uncertainty complicated the situation in reality; ranging accuracy especially deteriorated outside the antenna main-lobe when maneuvers performed. This paper proposes an on-orbit APC calibration method that uses a reinforcement learning (RL) process, aiming to provide the high accuracy ranging datum for onboard instruments with micrometre level. The RL process used here is an improved Temporal Difference advantage actor critic algorithm (TDAAC), which mainly focuses on two neural networks (NN) for critic and actor function. The output of the TDAAC algorithm will autonomously balance the APC calibration maneuvers amplitude and APC-observed sensitivity with an object of maximal APC estimation accuracy. The RL-based APC calibration method proposed here is fully tested in software and on-ground experiments, with an APC calibration accuracy of less than 2 mrad, and the on-orbit maneuver data from 11–12 April 2022, which achieved 1–1.5 mrad calibration accuracy after RL training. The proposed RL-based APC algorithm may extend to prove mass calibration scenes with actions feedback to attitude determination and control system (ADCS), showing flexibility of spacecraft payload applications in the future. [ABSTRACT FROM AUTHOR]

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry. [ABSTRACT FROM AUTHOR]

For automatic generation of test scenarios from UML (Unified Modeling Language) activity diagrams (ADs) are very important for improving test efficiency. However, state-of-the-art approaches mainly focus on simple approaches, without specifically considering the case of concurrent activity, which may result in the path explosion problem during the generation of test scenarios. In this paper, we put forward DFS-KeyLevel, a two-layer test scenario generation approach for UML Activity Diagram. First, the ADs of the software under test are modeled and preprocessed, and each concurrent module in each AD is simplified to a composite node. Then, primary test scenarios are generated from the concurrent activity modules using our proposed KeyLevel method. Next, the high-layer test scenarios are generated from the simplified AD with our improved Depth-First Search (DFS) algorithm. Finally, the primary and high-layer test scenarios are combined to generate the final test scenarios for the AD. The experimental results show that this DFS-KeyLevel is superior to the previous approaches. The DFS-KeyLevel can generate more test scenarios under constraints. Compared with DFS-LevelPermutes, the number of test scenarios generated by our DFS-KeyLevel is 1.13 times higher. Compared with Depth-First Search and Breadth-First Search (DFS-BFS) and Improved-DFS (IDFS), the DFS-KeyLevel produced 2.37 times test scenarios. The average coverage rates of staggered activities and total activity logical path coverage (TALPC) of the DFS-KeyLevel are 83.67% and 84% respectively, which is significantly higher than the above three approaches. In addition, when our method is applied to a real embedded system, it significantly reduces test scenarios generated to avoid path explosion while ensuring enough test scenarios. [ABSTRACT FROM AUTHOR]

A Train Control and Monitoring System (TCMS) is a vital part of monitoring sensors in a train. The data output of sensors is sent wirelessly to the data server for monitoring. However, as the wireless channel used to send the data is a shared public network, the transmitted data are prone to hackers and attacks. This paper proposes the Securebox architecture to manage secure data transfer from the onboard Vehicle Control Unit (VCU) to the data server in TCMS. The architecture is comprised of four main functions: network management, buffer management, data management, and security management. The architecture has been successfully developed in an HSM (Hardware Security Modul) and verified using alpha and beta software testing to form a secure TCMS. From the real-time testing phase in an electric-diesel train, the average performance of the AES-based HSM showed 55% faster time processing with unnoticed 0.1% added memory usage compared to the 3DES. The secure TCMS also withstands MITM attack and provides end-to-end data security compared to the (Mobile Station) MS to Base Station (BS) only in GSM-R. [ABSTRACT FROM AUTHOR]

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel "Flicker Test Software" is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively. [ABSTRACT FROM AUTHOR]

The seismic qualification and seismic performance assessment of suspended ceiling systems have been traditionally conducted through large-scale shake table testing. This experimentally-based approach not only involves significant financial and time resources but is also not flexible to evaluate several configurations of suspended ceiling systems. As an alternate approach, this paper discusses the development of a general finite-element (FE) model of suspended ceiling systems that accurately captures the propagation of damage observed during full-scale shake table testing using the commercial finite-element analysis software, Abaqus/Explicit. The results of the large-scale shake table testing on suspended ceiling systems conducted at the University at Buffalo as part of the NEES nonstructural project were used to validate the FE model. The major components of suspended ceiling systems such as grid runners, ceiling tiles, and hanger wires were modeled and assembled with significant detail in the FE model. The properties of the connections between the grid runners, which are vital for capturing the seismic behavior of suspended ceiling systems were derived numerically. A user-defined subroutine was incorporated in the Abaqus/Explicit software framework to capture the multidirectional pinched hysteretic behavior of the connections between grid runners. An illustrative example of the application of the proposed FE model to develop robust numerically generated fragility curves for suspended ceiling systems is presented. [ABSTRACT FROM AUTHOR]

Manual testing is a machine testing method in which testing cases are carried out manually rather than utilizing an automatic process. Both test cases were handled manually by the tester from the perspective of the end consumer. It guarantees that the program performs, regardless of whether or not it is specified in the prerequisite text. In order to complete approximately 100% of the application, test cases are planned and implemented. Manually produced test case reports are common. Since it can spot both visible and hidden program bugs, manual testing is one of the most effective testing techniques. A defect is described by the software as the difference between expected and actual performance. The bugs were corrected and turned over to the tester by the creator. Manual training is expected before automatic testing for any newly created software. This research requires a lot of time and resources, but it ensures the entire program is bug-free. Manual research entails knowledge of manual testing methods but does not require the use of an automated evaluation method. Manual checking is necessary since one of the fundamental software testing concepts is that "100% automation is not feasible." [ABSTRACT FROM AUTHOR]

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X‐ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing‐incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community. [ABSTRACT FROM AUTHOR]

Distributed software applications are one of the most important applications currently used. Rising demand has led to a rapid increase in the number and complexity of distributed software applications. Such applications are also more vulnerable to different types of attacks due to their distributed nature. Detecting and addressing attacks is an open issue concerning distributed software applications. This paper proposes a new mechanism that uses blockchain technology to devise a security testing mechanism to detect attacks on distributed software applications. The proposed mechanism can detect several categories of attacks, such as denial-of-service attacks, malware and others. The process starts by creating a static blockchain (Blockchain Level 1) that stores the software application sequence obtained using software testing techniques. This sequence information exposes weaknesses in the application code. When the application is executed, a dynamic blockchain (Blockchain Level 2) helps create a static blockchain for recording the responses expected from the application. Every response should be validated using the proposed consensus mechanism associated with static and dynamic blockchains. Valid responses indicate the absence of attacks, while invalid responses denote attacks. [ABSTRACT FROM AUTHOR]

Software requirement changes, code changes, software reuse, and testing are important activities in software engineering that involve the traceability links between software requirements and code. Software requirement documents, design documents, code documents, and test case documents are the intermediate products of software development. The lack of interrelationship between these documents can make it extremely difficult to change and maintain the software. Frequent requirements and code changes are inevitable in software development. Software reuse, change impact analysis, and testing also require the relationship between software requirements and code. Using these traceability links can improve the efficiency and quality of related software activities. Existing methods for constructing these links need to be better automated and accurate. To address these problems, we propose to embed software requirements and source code into feature vectors containing their semantic information based on four neural networks (NBOW, RNN, CNN, and self-attention). Accurate traceability links from requirements to code are established by comparing the similarity between these vectors. We develop a prototype tool RCT based on this method. These four networks' performances in constructing links are explored on 18 open-source projects. The experimental results show that the self-attention network performs best, with an average R e c a l l @ 50 value of 0.687 on the 18 projects, which is higher than the other three neural network models and much higher than previous approaches using information retrieval and machine learning. [ABSTRACT FROM AUTHOR]

Programming Online Verification Exam (OE) system has been widely used in algorithm education and practice since it can automatically analyze program results (e.g., correct or incorrect) after executing the submitted programs with corresponding test cases. OE systems can provide both execution results and error information so that novice programmers can get feedback quickly. If the submitted program cannot pass all the test cases, the novice programmers will get wrong-answer feedback, and they have to find and fix the errors in the program. Automated program fault localization techniques, which can locate the errors in programs under test automatically, thus help novice programmers fix the errors quickly. However, the performance of current automated fault localization techniques is limited due to the high-density test cases in novice programs of OE system. In this paper, we analyze the impact of test case density (TCD) and execution variety on fault localization performance and propose a method to reduce TCD to improve fault localization precision for novice programs. To evaluate the performance of our method, we conduct a number of empirical studies on 1199 real fault diagnosis algorithm related novice programs, and the experimental results show that using improved test cases through our method for fault localization in OE system can enhance the precision of fault localization for novice programs. Specifically, after decreasing the test cases' density, the improvement of fault localization accuracy ranges from 0.6% to 17.34% in terms of the Expense metric, and from the Accuracy@N metrics, the number of faulty statements that can be found increases in most cases. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the present study was to investigate the different roles of m-Health in pandemic management using the Partial Least Square (PLS) modeling technique. Owing to the limited existing literature regarding theorizing and the lack of the default model in predicting the role of mHealth in pandemic management, this method was used for exploratory modeling. Material and Methods: The PLS model was performed with smart-PLS software for the following steps: estimating weight ratios, considering weight ratios as input, estimating parameters, model-fitting and testing hypotheses. In addition, Factor scores in regression equations were used to estimate structural parameters. PLS algorithm, Cronbach's alpha, and Composite Reliability were used for the measurement and reliability evaluation model Goodness-of-fit. In addition, the R2 index was used to evaluate the model adequacy. Bootstrapping was used for significant coefficients. The Goodness-of-fit of the model was examined via the Standardized Root Mean Square Residual (SRMR) criterion. Results: It is determined the measurement models goodness-of-fit which the alpha values were as follows: diagnosis construct=0.786, followup=0.772, treatment=0.796, health care providers=0.704 and education=0.839 with more than 0.7 for all measures for Composite Reliability, the structural model measures such as R2 were higher than 0.6 for all areas and the overall model goodness-of-fit was -0.007 for SRMR, the five hypotheses developed in the model were confirmed according standardized coefficients more than 1.96 for all paths. Furthermore, the proposed model concerning the positive and significant role of m-Health in diagnosis, treatment and follow-up, education and health providers during the pandemic era was approved. Conclusion: The results of the present study can be used as a theoretical basis in developing models related to the role of m-Health in pandemic management. Also, health policymakers and practitioners could use the results to manage current and post-coronary conditions and to promote services based on various m-Health apps. [ABSTRACT FROM AUTHOR]

In this study, the geometric process (GP) model is considered in order to calculate the debugging and testing costs of a software product. Under the assumption of the GP model, the debugging and testing costs of the software product are obtained depending on the first and second moment functions of the GP. It is observed that the values of the first and second moment functions of the process must be known in order to calculate the debugging and testing costs. At the same time, the calculation of moment functions also depends on both the distribution of the first interarrival time of the GP and the estimates of the model and distribution parameters. In this study, the proposed debugging and testing costs are calculated for the data set containing 136 failure times of a real-time command and control system. For this dataset, it has been shown in previous studies that the GP with gamma distribution can be proposed as a model. Under gamma distribution assumption, the maximum likelihood estimates of the model parameters are obtained. Using the estimates of the model parameters, the first and second moment functions of the GP are calculated with the help of the numerical methods proposed for these functions. Finally, the debugging and testing costs are obtained for the data set. [ABSTRACT FROM AUTHOR]

The software used today, on the estimation of module temperature of photovoltaic systems, seem very important to be analyzed. These estimates are crucial in future techno-economic and environmentally friendly analyses of the systems to reach better achievements for future generations. This is very important to reach lifetime analyses of long-term feasibility to find out payback time and the levelized cost of energy. The present work is based on this issue, to test the module temperature estimation formulas used by four commonly used software models, and to determine the most suitable software for temperature analyses of five different photovoltaic modules in Middle Anatolia. Outdoor truthful long-term testing is the main realistic approach to reach fundamental contemplations. After an introductory basic knowledge, the main materials and methods are discussed to enlighten the analysis. The main methodology is given and further prospects are enlightened. Four well-known software are analyzed using four years of outdoor testing of five different photovoltaic modules. Measured ambient temperature and solar irradiance are used in the categorization of the software estimation performances. PV*SOL appears to be superior at low irradiance and ambient temperature, whereas Helioscope appears to be superior overall. [ABSTRACT FROM AUTHOR]

Aiming at the problems of test case reduction (TCR) technology based on intelligent algorithms that it is easy to fall into local convergence and premature maturity, this paper proposed a test case set reduction algorithm based on per-survivor processing (PSP). The algorithm used the sequential grid search idea of PSP algorithm to binary code the test case set to form the state space of sequential grid search. In the state transition stage, the algorithm took the code coverage and the effective execution time of test cases as branch metrics. According to the calculation results, the path with the largest branch weight was the surviving path, so as to eliminate redundant states and complete the reduction of test case set. The experimental results show that under the same experimental environment, compared with other algorithms, this algorithm not only has higher redundancy rate (RR), but also ensures higher error detection rate (EDR). This algorithm reduces the complexity of software testing to a certain extent and improves the efficiency of software testing. [ABSTRACT FROM AUTHOR]

This article presents an overview of the emerging themes in Autonomous Materials Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing interest among the scientists and engineers in the materials and manufacturing domains as well as those in the Artificial Intelligence (AI) and data sciences domains, and it offers immense research potential for the industrial systems engineering (ISE) and manufacturing fields. Although there are a few reviews related to this topic, they had focused exclusively on sequential experimentation techniques, AI/machine learning applications, or materials synthesis processes. In contrast, this review treats AMDM as a cyberphysical system, comprising an intelligent software brain that incorporates various computational models and sequential experimentation strategies, and a hardware body that integrates equipment platforms for materials synthesis with measurement and testing capabilities. This review offers a balanced perspective of the software and the hardware components of an AMDM system, and discusses the current state-of-the-art and the emerging challenges at the nexus of manufacturing/materials sciences and AI/data sciences in this nascent, exciting area. [ABSTRACT FROM AUTHOR]

Software testing and debugging is a crucial part of the software development process since defective software not only incurs customer dissatisfaction but also might incur legal issues. However, the managers of a software development company cannot arbitrarily prolong their software debugging period due to their software testing budget and opportunity in the market. Accordingly, in order to propose an advantageous testing project, the managers should be aware of the influence of the testing project on cost, quality, and time to make the best decision. In this study, a new software reliability growth model (SRGM) with consideration of the testing staff's learning effect is proposed to achieve better prediction. The methods of estimating the model's parameters and the symmetric confidence intervals are also proposed in the study. Moreover, in the past, most of the SRGMs focused on a single software system. However, in practice, some software systems were developed using modular-based system engineering approaches. Therefore, traditional software testing work can be changed to multiple modular testing work in this scenario. Therefore, the manager can use this to dispatch multiple staff groups to perform the individual testing work simultaneously. The study proposes two mathematical programming models to handle the scheduling of modular testing work. Additionally, the design of a computerized decision support system is also proposed in the study for the application in practice. [ABSTRACT FROM AUTHOR]

DevOps is a philosophy and framework that allows software development and operations teams to work in a coordinated manner, with the purpose of developing and releasing software quickly and cheaply. However, the effectiveness and benefits of DevOps depend on several factors, as reported in the literature. In particular, several studies have been published on software test automation, which is a cornerstone for the continuous integration phase in DevOps, which needs to be identified and classified. This study consolidates and classifies the existing literature on automated tests in the DevOps context. For the study, a systematic mapping study was performed to identify and classify papers on automated testing in DevOps based on 8 research questions. In the query of 6 relevant databases, 3,312 were obtained; and then, after the selection process, 299 papers were selected as primary studies. Researchers maintain a continuing and growing interest in software testing in the DevOps context. Most of the research (71.2%) is carried out in the industry and is done on web applications and SOA. The most reported types of tests are unit and integration tests. [ABSTRACT FROM AUTHOR]

This article offers an overview of the research developed in the last decade in the area of software testing. Systematic Literature Mapping was used as a research methodology, and based on three databases, 171 primary studies were selected. The study raises a set of research questions about techniques, strategies and types of tests, aspects such as quality factors analyzed, degree of innovation, and even about the empirical method used in the study. Among the findings, it was found that performance tests are the most analyzed types of tests, mixed strategies were the most cited, that efficiency turned out to be the most important quality factor, and that case studies are the most used technique by researchers. The secondary study carried out allowed us to conclude that research in the field of software testing is still valid in the context of Software Engineering, so it is pertinent to continue research on specific topics through both primary and secondary studies. [ABSTRACT FROM AUTHOR]

Nowadays, power systems' Protection, Automation, and Control (PAC) functionalities are often deployed in different constrained devices (Intelligent Electronic Devices) following a coupled hardware/software design. However, with the increase in distributed energy resources, more customized controllers will be required. These devices have high operational and deployment costs with long development, testing, and complex upgrade cycles. Addressing these challenges requires that a 'revolution' in power system PAC design takes place. Decoupling from hardware-dependent implementations by virtualizing the functionalities facilitates the transition from a traditional power grid into a software-defined smart grid. This article presents a survey of recent literature on software-defined PAC for power systems, covering the concepts, main academic works, industrial proof of concepts, and the latest standardization efforts in this rising area. Finally, we summarize the expected future technical, industrial, and standardization challenges and open research problems. It was observed that software-defined PAC systems have a promising potential that can be leveraged for future PAC and smart grid developments. Moreover, standardizations in virtual IED software development and deployments, configuration tools, performance benchmarking, and compliance testing using a dynamic, agile approach assuring interoperability are critical enablers. [ABSTRACT FROM AUTHOR]

Due to the reason, that many test cases in the software industry can be reused, selection and prioritizing techniques are essential during the regression and validation testing phases. However, time and project-specific limits must be observed. We present a hybrid optimization strategy to solve test case selection and prioritizing challenges in this research. The proposed method is the hybridization of Battle Royale Optimization (BRO) and Remora Optimization (RO) algorithm, which is named as hybrid Battle Royale-based Remora Optimization (HBR2O) algorithm. We focus on issues with pseudo-polynomial time complexity and low memory usage, which may be used to tackle problems like selection-prioritization and selection across collections of test suites or test cases. Dynamic programming optimization approaches require a large amount of memory, yet memory is limited. As a result of the decreased memory usage, the selection method includes a larger number of test cases. During the selection of software test cases, the proposed method's effectiveness is validated using multiple state-of-the-art techniques, resulting in improved performance. [ABSTRACT FROM AUTHOR]

Software testing is the most crucial stage in the software development process. Structural testing, functional testing and models that even support hybrid testing are different software testing techniques. Basic path testing, the most significant structural testing approach, is focused on evaluating software source code. The method emphasizes developing test data inputs to produce all feasible and efficient test paths that connect to all nodes and edges of the graph. The objective is to define the number of independent paths that can define the number of test cases needed to maximize test coverage. It ensured the execution of every statement and condition at least once. A nature-inspired Smell Detection Agent (SDA) algorithm is proposed in this paper to select all paths and prioritize the feasible solution. This algorithm is an optimization algorithm suitable for identifying optimal paths with priority. The concept is derived from the natural behaviour of canines that identified optimal path from source to the destination. The SDA algorithm is based on the evaporation of smell molecules in the form of gas and the perception capability of a smelling agent. The number of linearly independent paths through a programme module is measured by creating a Control Flow Graph of the code, which measures cyclomatic complexity. SDA algorithm gives significant increases in performance while considering the cyclomatic complexity. Complexity analysis of SDA trends to be in the O(E+V log V), while the competitor algorithms have an exponential growth of O(n 2 ). Various experiments were also carried out to emphasis the relevance of the proposed method. Ten different benchmarked applications has been taken for experimental analysis and it was observed to have an increased path coverage of 8% when SDA was used over the traditional methods. Also, the time complexity was reduced by 22%, which shows the powerfulness of the proposed SDA algorithm. [ABSTRACT FROM AUTHOR]

The paper presents a new mutually coupled circuit (MCC) based on a modified voltage differencing transconductance amplifier (MVDTA), designed using the modified Gorski‐Popiel technique. The proposed synthetic transformer (ST) circuit uses two MVDTAs as active elements, and all passive elements are grounded—two capacitors and two resistors. Thanks to the newly established coupling method‐connections, it is provided with independent electronic control of primary and secondary self‐inductance, as well as mutual inductance, via bias voltages of MVDTAs. In addition, it is very easy to provide the conditions by which symmetrical couplings are achieved. Monte Carlo analysis is carried out to verify the example application's robustness against the variation of the active and passive elements. In order to verify the results and conclusions reached through theoretical analysis, simulation testing was performed in the PSPICE software environment, both in the time and frequency domains. The functionality of the proposed MCC is shown in the example of a double‐bandwidth filter (double‐tuned band‐pass filter [DTBP]), while in order to accurately illustrate the superiority of the proposed solution, a comparison was made with similar circuits that are known from the professional literature. The proposal is also supported by experimental results (with off‐the‐shelf components IC MAX435) in order to confirm the workability of the proposed solution. [ABSTRACT FROM AUTHOR]

Methods of creating information databases for predicting the behavior of vegetation fires are analyzed. A brief description is given of the developed classification of vegetation fuels (VF), which is based on long-term fundamental pyrological research conducted in various regions of Russia. Examples of information databases have been created. The results of using the developed software for making VF maps based on forest inventory data in GIS and for fire behavior prediction are presented. Successful retrospective software performance tests have been carried out using the data of 125 wildfires in plain conditions and 10 wildfires in mountain conditions. A field performance test of the software is planned on active wildfires in the most fire-prone region of Siberia. [ABSTRACT FROM AUTHOR]

Building simulation and BIM in architect education at the TU Dresden – Series: Building simulation and calculation tools in teaching This series of articles presents the use of computer programs in university teaching of building physics and building technology for architects and civil engineers. This article addresses the integration of building simulation tools in the education of architects at the Faculty of Architecture at the TU Dresden. In contrast to the education of civil engineers, which focuses on verification calculations, the challenge for prospective architects is to incorporate findings from thermal simulation calculations into an optimization of the design. Data exchange formats and Building Information Modelling are particularly important in this context. These factors are investigated at the TUD in two courses, a course accompanying the design in the sixth semester and an advanced seminar in the eighth semester. In both teaching formats, the influence of design decisions on the supply of daylight, the risk of overheating and the supply concept (regenerative energies) was discussed. This involved using the CAD models created by the architecture students from various software applications and testing a transfer into BIM‐compatible exchange data formats (gbXML and IFC). [ABSTRACT FROM AUTHOR]