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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]

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]

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

Software systems typically have an input domain that can be subdivided into subdomains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for variation in how testers apply it and in the benefits they see. Additionally, its adoption has been limited since it requires a specification or model to be analysed. We propose an automated black-box boundary value detection method to support software testers in performing systematic boundary value analysis. This dynamic method can be utilized even without a specification or model. The proposed method is based on a metric referred to as the program derivative, which quantifies the level of boundariness of test inputs. By combining this metric with search algorithms, we can identify and rank pairs of inputs as good boundary candidates, i.e., inputs that are in close proximity to each other but with outputs that are far apart. We have implemented the AutoBVA approach and evaluated it on a curated dataset of example programs. Furthermore, we have applied the approach broadly to a sample of 613 functions from the base library of the Julia programming language. The approach could identify boundary candidates that highlight diverse boundary behaviours in over 70% of investigated systems under test. The results demonstrate that even a simple variant of the program derivative, combined with broad sampling and search over the input space, can identify interesting boundary candidates for a significant portion of the functions under investigation. In conclusion, we also discuss the future extension of the approach to encompass more complex systems under test cases and datatypes. [ABSTRACT FROM AUTHOR]

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

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]

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm's versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called 'test path distance' (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities. [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]

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]

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

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

This article explores the concept of "evaluation capture," where evaluations and evaluators are influenced by the interests of organizations and governments rather than the general public. It discusses the mechanisms of evaluation capture, such as institutionalization and incentives for evaluators. The article emphasizes the importance of recognizing and addressing evaluation capture to ensure the integrity of evaluations. It also discusses the role of key organizations in promoting global evaluation standards and the concept of evaluator resilience. The article suggests that evaluators must resist being absorbed into the system and focus on producing relevant outcomes. It also mentions the challenges faced by evaluators and suggests that resilience and neurofeedback may be helpful in improving evaluation practices. Further research is needed to determine the effectiveness of these approaches. [Extracted from the article]

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

This scientific work introduces an innovative algorithm and its software implementation for addressing the issue of detecting code similarities and identifying instances of code borrowings in software solutions. This development, pre sented in both finished and abstract forms, has the potential to revolutionize the way educators and institutions approach evaluating the creative solutions of student-programmers. The algorithm is designed to automate checks on software solutions submitted by students for various programming challenges, competitions, and Olympiads. The tool provides an effective means of detecting code similarities and flagging potential instances of plagiarism, allowing instructors to ensure that the submissions are original and the result of conscientious efforts. The work presents two examples of creative tasks that require careful evaluation for code similarity, providing concrete illustrations of how the algorithm works in practice. The algorithm itself is described in detail, and parts of the software code implementation in C# programming language are provided, enabling interested parties to explore the tool's technical workings. The results of testing and analysis of the algorithm are presented, with the work highlighting the potential of the tool to automate time-consuming and resource-intensive evaluations of software solutions. Visualizations of the test results provide clear and concise insights into the efficacy of the algorithm, enabling educators to assess the originality of the solutions with ease. Overall, the work is an invaluable contribution to the field of software development, providing educators with an innovative tool for ensuring the originality of software solutions submitted by students. With the growing need for transparency and academic integrity in higher education, this development has significant potential to transform the way institutions approach software solution evaluations, empowering students to pursue creative problem-solving with confidence. All of this can be achived by this algorithm being implemented in the future in bigger application as it`s main or side feature. [ABSTRACT FROM AUTHOR]

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

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]

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

Training in the teaching-learning process is a permanent task of education. Usability evaluations in the world have increased, and therefore the Professional Practice discipline places its emphasis. A methodological proposal is made to contribute to usability evaluations during the training of the Computer Science Engineer in the Research and Development Project subject taught in the second year of Computer Science Engineering at the University of Computer Sciences. Theoretical-methodological references of good practices and international trends in software testing are addressed. Recommendations are offered with a high degree of professional motivation, based on experiences in previous processes. Its objective is to familiarize teachers with the process and help them interpret the Usability characteristic according to ISO IEC 25010:2011. Scientific methods are used to support the stated objective. The bibliographic analysis and surveys carried out set guidelines for the research. [ABSTRACT FROM AUTHOR]

An essential part of software engineering is testing. A series of pre-testing tasks should be completed before performing testing tasks. Implementing test suites is one of the pre-testing phases. In this work, a case study has been used to implement the suggested method for implementing test suites. The strategy is based on an analysis of UML (Unified Modelling Language) enhanced state chart diagrams (SCDs). UML state chart diagram analysis, AD (activity diagram) conversion into a graph, AG (activity graph) simplification, and test suite implementation are all steps in the generating process. [ABSTRACT FROM AUTHOR]

Cross-browser testing not only is one of the most common non-functional testing methods in the field of software testing, but also the testing method that requires large amounts of resources, in terms of hardware and time. Basically, based on Selenium Grid, Kubernetes and KEDA auto-scaler, a cross-browser testing platform can be quickly built. However, through our empirical study of this style of platform, we observed three significant problems in terms of its reliability and efficiency: the Health-Check problem, the Session-Queue problem, and the Cooldown problem. This paper suggests solutions to these problems. The experimental result shows a 2.27 times improvement in reliability and a decrease in execution time for 61.5%. Moreover, the overall execution time is also 54.2% less comparing with Selenium's Dynamic Grid. [ABSTRACT FROM AUTHOR]

Medical imaging is a crucial tool for various clinical applications, including examination of medical issues, including early identification, monitoring, diagnosis, and therapy. To analyse medical images using computer vision, it is crucial to comprehend the ideas behind artificial neural networks and deep learning. In this particular article, we present recent principles of deep learning models and the different forms of widely used activation functions. Utilising Deep Learning Approach (DLA) has expanded quickly in the study of medical images, particularly in detecting either being present or not diseases. This study explores artificial neural networks development and provides a comprehensive analysis of DLA and potential uses for medical imaging. The majority of DLA implementations focus on pictures from digital histopathology, computer tomography, mammography, and X-rays. A thorough overview of the literature on the classification, detection, and segmentation of medical pictures using DLA is provided in the paper, which might help researchers decide what changes should be made to medical image analysis. [ABSTRACT FROM AUTHOR]

A crucial field in software development and testing is Software Defect Prediction (SDP) because the quality, dependability, efficiency, and cost of the software are all improved by forecasting software defects at an earlier stage. Many existing models predict defects to facilitate software testing process for testers. A comprehensive review of these models from different perspectives is crucial to help new researchers enter this field and learn about its latest developments. Algorithms, method types, datasets, and tools were the only perspectives discussed in the current literature. A comprehensive study that takes into account a wide spectrum of viewpoints hasn't yet been published. Examining the development and advancement of SDP-related studies is the goal of this literature review. It provides a comprehensive and updated state-of-the-art that satisfies all stated criteria. Out of 591 papers retrieved from 6 reputable databases, 73 papers were eligible for analysis. This review addresses relevant research questions regarding techniques & method types, data details, tools, code syntax, semantics, structural and domain information. Motivation to conduct this comprehensive review is to equip the readers with the necessary information and keep them informed about the software defect prediction domain. [ABSTRACT FROM AUTHOR]

A novel interpolation and difference optimized (IDO) machine learning model to predict the depth of silicon etching is proposed, which is particularly well-suited to addressing small sample problems. Our approach involves dividing both experimental and simulation data obtained from the Technology Computer-Aided Design (TCAD) software into training and testing sets. Both experimental data and TCAD simulation data are used as inputs to machine learning module 1 (ML1), while ML2 takes the actual experimental data as inputs and then learns the difference between the experimental data and the TCAD simulation data, outputting the difference. The outputs generated by ML1 and ML2 serve as input parameters to machine learning module 3 (ML3), and the weights of ML3 are updated through its own learning process to produce the final prediction results. We demonstrate that our IDO model, which contains three basic ML algorithms, achieves higher prediction accuracy compared to the basic ML algorithm alone. Moreover, through ablation studies, we establish that the three components of the IDO prediction model are inseparable. The IDO model exhibits improved generalization performance, making it particularly suitable for small sample datasets in the semiconductor processing domain. [ABSTRACT FROM AUTHOR]

In solving technological problems related to sheet metal deep drawing with the use of computer tools, the key issue is still the correct determination and entering of boundary conditions to FEM-based software. The procedure for preparing input data for modelling such processes includes geometric data (drawing of tools and material), technological parameters along with the contact conditions between the workpiece and the tools (friction model and type of lubricant) and material properties, in which work-hardening curves are of particular importance. In typical material databases of FEM-based software and designed for computer modelling of deep drawing processes, the properties of only a small number of material grades are available, and commercial software producers charge additional fees for each additional quantity. Those properties that are already in the database are usually devoid of basic information, e.g. related to the state of the material (material after recrystallization, annealing, cold working has different properties). In paper, experimental tests were carried out to determine flow curves based on cold tensile curves for flat samples made of EN-AW 1050A aluminium, Cu-ETP copper, CuZn37 brass and S235JRG2 steel. The investigation used a universal testing machine with a 20 kN pressing force, equipped with specialized Test&Motion software for measuring forces and displacements. It was calibrated and satisfies the metrological requirements for class 0.5. A comparative analysis of the curves determined by the analytical method was carried out. The material models obtained in the experimental tests were used in the computer simulation of the deep drawing processes of cylindrical drawpieces in the ABAQUS software. The results were experimentally verified in terms of comparing the changes in the pressing forces as a function of the displacement of the punch. The results obtained in the research can be used in industrial practice for computer-aided design of cold-deep drawing processes for drawpieces of various shapes from the discussed materials. [ABSTRACT FROM AUTHOR]

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

Background: The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. Results: We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data. Conclusions: We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author summary: Experimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data. [ABSTRACT FROM AUTHOR]

In the past 20 years, semiconductor manufacturing technology has advanced rapidly, but the advancement of integrated circuit (IC) testers has been slow. Using obsolete testers to inspect advanced wafers has become a significant challenge for test manufacturers. In this research, we used DITM (digital IC testing model) to discuss the impact of the test guardband (TGB) on quality and yield. Considering the interaction between semiconductor fabrication capability parameters and test capability parameters, we proposed an estimation method [deductive estimation method (DEM)] to analyze the electrical distribution changes of products after chip production and deduce the yield of future products. The deductive estimation method can correctly depict the future test yield (Y t) curve using the chip frequency data published by IRDS (International Roadmap for Devices and Systems) in 2017. Furthermore, test manufacturers can measure whether the current test capabilities can cope with future semiconductor chip manufacturing capabilities by predicting the trends. Next, test manufacturers can maintain high-quality and high-yield chip output by pre-adjusting the hardware testing capabilities of ATE (automated test equipment) or proposing more effective chip testing methods. [ABSTRACT FROM AUTHOR]

Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% (BFPsmall datasets) and 25.7% (BFPmedium datasets) of software defects. [ABSTRACT FROM AUTHOR]

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP‐complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing high‐strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high‐strength CAG problem. The sub‐optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high‐covering strength test suites. The QSSMA method could successfully build high‐strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software modules to build a model to rank within‐project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross‐project modules to train a model without considering the data distribution difference between cross‐project and within‐project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort‐aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K‐nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) tries to find the best control output by optimizing among the predictions. In this context, determining the objective function of optimization is a critical process. This study discusses the velocity control of the mini brushed direct current (BDC) motor used in the actuator with MPC. Four different functions have been defined to design the most appropriate cost function for MPC by considering the parameters that may be effective in the motor dynamics such as velocity, current, power, and switching states. Software in the Loop (SIL), one of the modeling-based testing methods, was used within the scope of testing the algorithms. After the SIL tests, it has been observed that the mini BDC motor can be successfully controlled with simple adjustments to the cost function of the MPC. As a result of the analysis obtained the best results with the objective function consisting of velocity error, estimated current, and the difference between the two estimated velocity values. With this controller, the BDC motor can be controlled without overshoot and with a steady-state error of less than 2% under load. [ABSTRACT FROM AUTHOR]

Software reliability growth models (SRGMs) are used to estimate future failure rates and the number of residual defects in software. SRGM helps reliability software engineers make test termination decisions. Although more than 250 traditional SRGMs have been proposed for reliability assessment, research to develop more reliable models is still ongoing. Recently, new methods based on neural networks have been developed to improve the accuracy of software reliability assessment. The article develops a neural network algorithm for assessing software reliability. To do this, factors affecting software reliability and covering its life cycle were assessed. Based on them, sets for training a three-layer neural network were created. [ABSTRACT FROM AUTHOR]

Body mass is arguably the most important characteristic of an organism, yet it is often not available in biological samples that have been skeletonized, liquidpreserved, or fossilized. The lack of information is especially problematic for fossil species, for which individuals with body mass information are not available anywhere. Multiple methods are available for estimating the body mass of fossil terrestrial vertebrates but those for their marine counterparts are limited. Paleomass is a software tool for estimating the body mass of marine vertebrates from their orthogonal silhouettes through bracketing. It generates a set of two 3D models from these silhouettes, assuming superelliptical body cross-sections with different exponent values. By setting the exponents appropriately, it is possible to bracket the true volume of the animal between those of the two models. The original version phased out together with the language platform it used. A new version is reported here as an open-source package based on the R scripting language. It inherits the underlying principles of the original version but has been completely rewritten with a new architecture. For example, it first produces 3D mesh models of the animal and then measures their volumes and areas with the VCG library, unlike the original version that did not produce a 3D model but instead computed the volume and area segment by segment using parametric equations. The new version also exports 3D models in polygon meshes, allowing later tests by other software. Other improvements include the use of NACA foil sections for hydrofoils such as flippers, and optional interpolation with local regression. The software has a high accuracy, with the mean absolute errors of 1.33% when the silhouettes of the animals are known. [ABSTRACT FROM AUTHOR]

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

This paper presents a novel method for testing Boolean expressions. It is based on spectral, aka Fourier analysis of Boolean functions which is exploited to generate test inputs. The approach has three important contributions: (i) It generates a relatively small test suite with a high capability of fault detection, (ii) The test suite is prioritized such that expected fault detection time is shorter, (iii) It is entirely mathematical relying on a simple and straightforward formula. The proposed method is formulated and evaluations are performed on both synthetic and real expressions. It is also compared with two common test generation criteria, MC/DC and Minimal MUMCUT. Evaluations show that the test suite generated by the spectral approach is relatively small while expressing the capability of a better and quicker fault detection. The approach presented in this paper provides a useful insight into how spectral/Fourier analysis of Boolean functions can be exploited in software testing. [ABSTRACT FROM AUTHOR]

In software development, the software testing phase is an important process in determining the quality level of the software. Software testing is a process of executing a program aimed at finding errors in module access, units, and involves the execution of the system being tested on a number of test inputs, and determining whether the output produced is correct. In this study, a model-based testing (MBT) called integration-based model (I-BM) framework will be developed. This I-BM framework integrates testing variables from several software testing methods, namely black-box testing, white-box testing, unit testing, system testing, and acceptance testing. The integrated variables are function, interface, structure, performance, requirement, documentation, positives, and negatives. Then, this framework will document software errors to form a dataset, which will be measured for the level of accuracy of expected manual fault output using neural network algorithm and support vector machine. From the experiment results, it shows that the accuracy level of predicting fault output values from the I-BM framework using the neural network algorithm is on average 80%, and it produces a superior SVM architecture model in predicting I-BM framework output errors with an accuracy value of 0.99, precision of 0.99, recall of 0.99, and F 1 -score of 0.99. Compared to other MBT, the IBM framework has the advantage of being a more comprehensive software testing model because it starts from the identification of problems, analysis, design, documentation of software testing, and recommendations for each fault output found. Thus, software errors can be classified systematically in the form of a dataset, and not only focus on software testing for product lines and module mappings. [ABSTRACT FROM AUTHOR]

Cloud computing has been adopted in many developing nations, namely Iraq and Ghana, for its multiple benefits. Nonetheless, there is a dearth of studies on cloud computing adoption in developing nations. This study aims to investigate the factors influencing cloud computing adoption among Iraqi small and medium-sized enterprises (SMEs) and the implication of cloud computing on the SME firm performance. The diffusion of innovation (DOI) theory served as the foundation for the research model. Primary data from 396 SMEs were analysed using the structural equation modelling (SEM) approach through SmartPls 3.0 software for model testing. Resultantly, the use of cloud computing enhances firm performance. The technological factor also positively affects firm performance and cloud computing. The findings provide cloud service providers, managers, and government regulators with important recommendations to encourage the adoption and effective implementation of cloud computing in Iraq. [ABSTRACT FROM AUTHOR]

Background: When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. Aim: We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. Method: We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distancebased surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). Results: We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time. Conclusions: Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results. [ABSTRACT FROM AUTHOR]

Background: Digital advancement of power assisted exercise equipment will advance exercise prescription for people with stroke (PwS). This article reports on the remote usability evaluation of a co-designed graphical user interface (GUI) and denotes an example of how video-conference software can increase reach to participants in the testing of rehabilitation technologies. The aim of this study was to evaluate the usability of two sequential versions of the GUI. Methods: We adopted a mixed methods approach. Ten professional user (PU) (2M/8F) and 10 expert user (EU) participants (2M/8F) were recruited. Data collection included a usability observation, a 'think aloud' walk through, task completion, task duration and user satisfaction as indicated by the Post Study System Usability Questionnaire (PSSUQ). Identification of usability issues informed the design of version 2 which included an additional submenu. Descriptive analysis was conducted upon usability issues and number of occurrences detected on both versions of the GUI. Inferential analysis enabled comparison of task duration and PSSUQ data between the PU and EU groups. Results: Analysis of the 'think aloud' walkthrough data enabled identification of 22 usability issues on version 1 from a total of 100 usability occurrences. Task completion for all tasks was 100%. Eight usability issues were directly addressed in the development of version 2. Two recurrent and 24 new usability issues were detected in version 2 with a total of 86 usability occurrences. Paired two tailed T-tests on task duration data indicated a significant decrease amongst the EU group for task 1.1 on version 2 (P = 0.03). The mean PSSUQ scores for version 1 was 1.44 (EU group) and 1.63 (PU group) compared with 1.40 (EU group) and 1.41 (PU group) for version 2. Conclusions: The usability evaluation enabled identification of usability issues on version 1 of the GUI which were effectively addressed on the iteration of version 2. Testing of version 2 identified usability issues within the new submenu. Application of multiple usability evaluation methods was effective in identifying and addressing usability issues in the GUI to improve the experience of PAE for PwS. The use of video-conference software to conduct synchronous, remote usability testing is an effective alternative to face to face testing methods. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction. [ABSTRACT FROM AUTHOR]

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

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

Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the article introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed. [ABSTRACT FROM AUTHOR]

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