Search

Background and Objectives: AIDS is a public health problem for which screening tests are recommended for prevention. This study aimed to determine the impact of an educational intervention based on the precaution adoption process model on HIV screening. Materials and Methods: A semi-experimental study was conducted on 120 third and fourth-year nursing students in Sabzevar city in 2019 using random sampling. Data collection tools, researcher-made questioner with appropriate validity and reliability, included demographic information, awareness, and constructs of the precaution adoption process model. Data was collected before, two weeks, and two months after the intervention in intervention and control groups. Data was analyzed using SPSS19 software and tests including Chi-square, independent t-test, GEE, and multiple linear regression. Results: In the present study, 21.7%, 35%, 28.3%, and 15% of students were in stages 2, 3, 4, and 5, respectively. After the intervention, 55% of individuals in the intervention group progressed to stage 6 (action). The educational intervention increased awareness by 4%, sensitivity by 6%, perceived benefits by 16%, and self-efficacy. Additionally, the behavior of the intervention group improved more than three times compared to the control group (P=0.028). Conclusion: The study results indicated that the educational intervention to students, despite being implemented at various stages of the model, increased the likelihood of them moving closer to the stage of performing the behavior. Therefore, designing educational interventions to promote the target group from stages of non-engagement or decision-making to HIV testing is recommended. [ABSTRACT FROM AUTHOR]

For software developers as well as software product users, fault removal efficiency is crucial. Developers can estimate the amount of work required by evaluating the proportion of addressed faults. At the same time, the level of trust in utilizing the software product can be determined by customers, based on this information. The proposed software reliability growth model considers fault removal efficiency relevant to contemporary scenarios characterized by automated testing and debugging tools. A comparison between the proposed model and other existing models is conducted by utilizing two data sets from software testing. Additionally, the best release plans are created by considering warranty costs, risk costs, and error removal costs while still meeting reliability requirements. [ABSTRACT FROM AUTHOR]

Modern web services widely provide RESTful APIs for clients to access their functionality programmatically. Fuzzing is an emerging technique for ensuring the reliability of RESTful APIs. However, the existing RESTful API fuzzers repeatedly generate invalid requests due to unawareness of errors in the invalid tested requests and lack of effective strategy to generate legal value for the incorrect parameters. Such limitations severely hinder the fuzzing performance. In this paper, we propose DynER, a new test case generation method guided by dynamic error responses during fuzzing. DynER designs two strategies of parameter value generation for purposefully revising the incorrect parameters of invalid tested requests to generate new test requests. The strategies are, respectively, based on prompting Large Language Model (LLM) to understand the semantics information in error responses and actively accessing API-related resources. We apply DynER to the state-of-the-art fuzzer RESTler and implement DynER-RESTler. DynER-RESTler outperforms foREST on two real-world RESTful services, WordPress and GitLab with a 41.21% and 26.33% higher average pass rate for test requests and a 12.50% and 22.80% higher average number of unique request types successfully tested, respectively. The experimental results demonstrate that DynER significantly improves the effectiveness of test cases and fuzzing performance. Additionally, DynER-RESTler finds three new bugs. [ABSTRACT FROM AUTHOR]

Metamorphic testing is a valuable approach to verifying machine learning programs where traditional oracles are unavailable or difficult to apply. This paper proposes a technique to prioritize metamorphic relations (MRs) in metamorphic testing for machine learning and deep learning systems, aiming to enhance early fault detection. We introduce five metrics based on diversity in source and follow-up test cases to prioritize MRs. The effectiveness of our proposed prioritization methods is evaluated on three machine learning and one deep learning algorithm implementation. We compare our approach against random-based, fault-based, and neuron activation coverage-based MR ordering. The results show that our data diversity-based prioritization performs comparably to fault-based prioritization, reducing fault detection time by up to 62% compared to random MR execution. Our proposed metrics outperformed neuron activation coverage-based prioritization, providing 5–550% higher fault detection effectiveness. Overall, our approach to prioritizing metamorphic relations leads to increased fault detection effectiveness and reduced average fault detection time. This improvement in efficiency can result in significant time and cost savings when applying metamorphic testing to machine learning and deep learning systems. [ABSTRACT FROM AUTHOR]

Background: The use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties. Results: We developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of on a desktop workstation. Our software and test dataset are freely distributed on Github. Conclusions: TopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping. [ABSTRACT FROM AUTHOR]

Few mobile robot developers already test their software on simulated robots in virtual environments or sceneries. However, the majority still shy away from simulation-based test campaigns because it remains challenging to specify and execute suitable testing scenarios, that is, models of the environment and the robots' tasks. Through developer interviews, we identified that managing the enormous variability of testing scenarios is a major barrier to the application of simulation-based testing in robotics. Furthermore, traditional CAD or 3Dmodelling tools such as SolidWorks, 3ds Max, or Blender are not suitable for specifying sceneries that vary significantly and serve different testing objectives. For some testing campaigns, it is required that the scenery replicates the dynamic (e.g., opening doors) and static features of real-world environments, whereas for others, simplified scenery is sufficient. Similarly, the task and mission specifications used for simulation-based testing range from simple point-to-point navigation tasks to more elaborate tasks that require advanced deliberation and decision-making. We propose the concept of composable and executable scenarios and associated tooling to support developers in specifying, reusing, and executing scenarios for the simulation-based testing of robotic systems. Our approach differs from traditional approaches in that it offers a means of creating scenarios that allow the addition of new semantics (e.g., dynamic elements such as doors or varying task specifications) to existing models without altering them. Thus, we can systematically construct richer scenarios that remain manageable. We evaluated our approach in a small simulation-based testing campaign, with scenarios defined around the navigation stack of a mobile robot. The scenarios gradually increased in complexity, composing new features into the scenery of previous scenarios. Our evaluation demonstrated how our approach can facilitate the reuse of models and revealed the presence of errors in the configuration of the publicly available navigation stack of our SUT, which had gone unnoticed despite its frequent use. [ABSTRACT FROM AUTHOR]

The quality of software test data is assessed through mutation testing. This technique involves introducing various modifications (mutants) to the original code of the program. The test data's effectiveness, known as the test score, is quantified by the proportion of mutants that are successfully detected. A prominent issue within software mutation testing is the generation of an excessive quantity of mutants in programs. The primary objectives of this research are to diminish the total count of mutants by consolidating those that are duplicative, to decrease the overall mutation testing time by lessening the quantity of mutants produced, and to lower the expenses associated with mutation testing. This research introduces a machine learning-based strategy to recognise and eliminate redundant mutants. Building a machine learning-based classifier to classify the instructions according to the rate of error propagation is the first contribution of this study. Next, the remaining instructions of the source code are analyzed by the designed parser to generate single-line mutants. Unlike traditional approaches, mutants are not generated as distinct full-fledged programs. Instead, mutants consisting of a single line are selectively run using a developed instruction evaluator. Following this, a clustering technique is employed to categorise single-line mutants yielding identical outcomes into groups, where only one complete execution is needed per group. Testing on Java benchmarks with the new method has shown a decrease in the mutant count by 56.33% and a time reduction of 56.71% when compared with parallel tests using the MuJava and MuClipse tools. Despite the marked decrease in both mutant count and testing time, the mutation score remained consistent. Comparable outcomes were also observed with other mutation testing tools such as Pitest, Jester, Jumble, and JavaLancer. [ABSTRACT FROM AUTHOR]

Quality and productivity needs are considered together in software. For this reason, any existing software should be tested automatically with test automation. Software test automations is automated software testing activities. Automating constantly manually written tests, on the other hand, saves time, reduces error rates, produces better quality software, and reduces costs. This study aims to produce an automatic unit testing framework that is planned to work in run-time on software products. This developed application performs unit test transformations that can respond to the desired test scenarios on the product being studied.. Java agent is used as the basis of all these transformations. All information about the objects, methods, and variables of the sample java classes to be worked on is converted into data in run-time using byte code. During this transformation, information is saved in the database, and unit tests are created automatically through the template engine. Compared to the products developed on automatic unit test generation in the literature, the opcode parsing method was developed for this study. This method reads a byte code at run-time, uses the properties of the java class it belongs to, and automatically creates the unit test class and test methods. The study can also examine different object definitions and conditional and loop structures within a method and produce alternative test scenarios. The automatic unit test scenario produced has been turned into a flexible framework that can encounter minimum errors at run-time. Considering the scarcity of studies in the field of national software testing; It is thought that the automatic unit test generation product developed within the scope of this study, using byte code, will contribute to the work area. [ABSTRACT FROM AUTHOR]

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps. [ABSTRACT FROM AUTHOR]

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the challenges blind individuals face when comprehending mathematical expressions within a CAS environment. We propose "Math for Everybody" (Math4e, version 1.0), a software module to reduce barriers for blind users in education. Math4e is a Sonification Module for CAS that generates a series of auditory tones, prosodic cues, and variations in audio parameters such as volume and speed. These resources are designed to eliminate ambiguity and facilitate the interpretation and understanding of mathematical expressions for blind users. To assess the effectiveness of Math4e, we conducted standardized tests employing the methodologies outlined in the Software Engineering Body of Knowledge (SWEBOK), International Software Testing Qualifications Board (ISTBQ), and ISO/IEC/IEEE 29119. The evaluation encompassed two scenarios: one involving simulated blind users and another with real blind users associated with the "Asociación de Invidentes Milton Vedado" foundation in Ecuador. Through the SAM methodology and verbal surveys (given the condition of the evaluated user), results are obtained, such as 90.56% for pleasure, 90.78% for arousal, and 91.56% for dominance, which demonstrates significant acceptance of the systems by the users. The outcomes underscored the users' commendable ability to identify mathematical expressions accurately. [ABSTRACT FROM AUTHOR]

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system's frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events. [ABSTRACT FROM AUTHOR]

The field of automatic detection of quality attributes from software requirements' text stands as one of the most pioneering realms within software requirements research. Such automatic quality attributes aim to aid stakeholders in establishing the system architecture and preemptively circumventing faults. A considerable number of classifier models have been put forward, many of which show encouraging results. However, our analysis has identified substantial gaps in these studies, including (a) a limited dataset volume, (b) the absence of an evaluation study for cross‐domain test sets, (c) the problem of real‐time prediction scenarios where a vast amount of unlabeled data floods the system each second, and (d) a dearth of comparative studies scrutinizing diverse software requirements datasets and multiple machine learning models, with particular emphasis on in‐domain and cross‐domain testing. Hence, there is a pressing need to construct an alternative framework to enhance classifier performance under such conditions. Our research is primarily centered on developing a semisupervised methodology that hinges on GAN‐BERT, introducing two datasets for the requirements of the engineering community and delivering comparative studies that consider a variety of classifiers and two labeling paradigms, namely, binary and multi. Remarkably, even with fewer data in a multiclassification scenario, our model outperforms other classifiers when assessing data from both identical and different domains. [ABSTRACT FROM AUTHOR]

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

Copyright of Anuario Turismo y Sociedad is the property of Universidad Externado de Colombia, Facultad de Empresas Turísticas y Hoteleras and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The European Union is committed to modernising and improving air traffic management systems to promote environmentally friendly air transport. However, the safety-critical nature of ATM systems requires rigorous user testing, which is hampered by the scarcity and high cost of air traffic controllers. In this article, we address this problem with a novel approach that involves non-experts in the evaluation of expert software in an A/B test setup. Using a transformation model that incorporates auxiliary information from a newly developed psychological questionnaire, we predict the performance of air traffic controllers with high accuracy based on the performance of students. The transformation model uses multiple linear regression and auxiliary information corrections. This study demonstrates the feasibility of using non-experts to test expert software, overcoming testing challenges and supporting user-centred design principles. [ABSTRACT FROM AUTHOR]

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made. [ABSTRACT FROM AUTHOR]

A covering array is a combinatorial object that is used to test hardware and software components. The covering array number is the minimum number of rows needed to construct a specific covering array. The search for better upper bounds for covering array numbers is a very active area of research. Although there are many methods for defining new upper bounds for covering array numbers, recently the use of covering perfect hash families has significantly improved a large number of covering array numbers for alphabets that are prime powers. Currently, excellent upper bounds have been reported for alphabets 2, 3, 4, and 5; therefore, the focus of this article is on defining new upper bounds on the size of covering arrays for the alphabet seven using perfect hash families. For this purpose, a greedy column extension algorithm was constructed to increase the number of columns in a covering perfect hash family while keeping the number of rows constant. Our greedy algorithm begins with a random covering perfect hash family containing only eight columns and alternates between adding and removing columns. Adding columns increases the size of the covering perfect hash family while removing columns reduces the number of missing combinations in covering perfect hash families with different column counts. The construction process continues with the covering perfect hash family with the smallest number of columns being refined (i.e., missing combinations reduced). Thus, columns are dynamically added and removed to refine the covering perfect hash families being built. To illustrate the advantages of our greedy approach, 152 new covering perfect hash families of order seven with strengths 3, 4, 5, and 6 were constructed, enabling us to improve 12,556 upper bounds of covering array numbers; 903 of these improvements are for strength three, 8910 for strength four, 1957 for strength five, and 786 for strength six. [ABSTRACT FROM AUTHOR]

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

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

Due to the increasing complexity of vehicle software, it is becoming increasingly difficult to comprehensively test all requirements. This inevitably means that alternative test methods, e.g., simulation-based methods, must be used more frequently. However, the challenge involves identifying appropriate requirements that can be technically tested in a simulation environment initially. The present work is aimed at evaluation and optimization of the effectiveness of software-in-the-loop (SiL) simulations in the testing process of vehicle software. The focus is on supporting the testing process by shifting specific test cases from hardware-in-the-loop (HiL) test benches to SiL-based simulations. For this purpose, a systematic approach was developed to analyze and categorize requirements, enabling precise and efficient allocation of test cases. Furthermore, a detailed review and recommendation for improving the ProSTEP iViP standard for virtual electronic control units (vECU) was carried out. The developed matrix associates the defined requirement clusters with different classifications of vECUs, facilitating the identification of suitable test environment types for conducting specific test cases. By assigning test cases to appropriate vECU levels, the testing processes can be targeted and cost-optimized. Finally, the theoretical results were evaluated in an SiL simulation environment. It was observed that a significant part of the requirements could effectively be tested using a vECU. These findings confirmed the potential of SiL simulation environments to not only support, but also enhance, the testing process for vehicle software by providing a cost-effective and flexible complement to traditional HiL test benches. [ABSTRACT FROM AUTHOR]

Featured Application: The technique uses Machine Learning (ML) models to support decision-making on software testing scope and resource allocation to augment the outcomes with the available resources. As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models' in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach's applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort. [ABSTRACT FROM AUTHOR]

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

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

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

There currently exists a need for a platform that provides users with the ability to perform extensive, repeatable and meaningful simulation and testing for the hardware and software which compose vehicle/autonomous vehicle systems whilst being broadly accessible, widely supported and provides robust features and development tools. The contemporary implementations of similar systems are either financially exorbitant or highly contained. The system reflected in this paper aims to fill a gap in the industry of vehicle/autonomous vehicle development by extending on currently existing open-source software to provide a highly streamlined platform to support the production of general road vehicle and autonomous vehicle driving systems. The software tools and hardware components chosen for the system will be discussed, followed by the features constructed throughout the development process. The end result of the system is a platform that allows for quick, repeatable, accurate, and nearly endless testing of a digital twin of real life vehicles. This system will allow users to gain valuable simulation and testing data of hardware and software components in a manner which is not always feasible using the traditional methods of autonomous vehicle testing. [ABSTRACT FROM AUTHOR]

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

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study. [ABSTRACT FROM AUTHOR]

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

Locating software faults during debugging is crucial yet challenging. Automated techniques, such as spectrum-based fault localization (SBFL), aid developers in efficiently localizing faults by analyzing program execution information and utilizing statistical approaches to rank program entities according to their suspiciousness. SBFL is also known as lightweight fault localization because of its scalability and minimal computational overhead. While essential, there has been limited research on how test suites affect fault localization. In this paper, we show how test suites impact fault localization and how they can be optimized for better results. SBFL techniques have some inherent limitations, especially in diagnosing faults within loop bodies or iteration statements. Additionally, identical suspiciousness levels can result in ties. While SBFL techniques effectively rank faulty program entities among the Top-N suspicious entities, they might not consistently position the faulty entity within the initial few positions. To address these research gaps, this paper proposes a hybrid approach that combines test suite optimization, statement execution frequency, and fault context concepts to enhance the performance of existing SBFL techniques in single fault scenarios. We evaluate our approach using three popular SBFL methods (Ochiai, Jaccard, and DStar) on Siemens benchmarks and four large real-world programs (flex, grep, sed, and space) with their test suites. The results demonstrate a significant enhancement in fault localization performance when applying our proposed approach to existing SBFL methods. For example, when applied to Ochiai, it reduces examined statements by 62.76% and 65.23% on average for the two test suites, respectively. Furthermore, it identifies 52% of faults by examining only 1% or less of the code and locates 60% of faults by analyzing only 0.1% or less of the code in Siemens and four large real-world programs, respectively. Similar improvements are observed when our approach is applied to Jaccard and DStar methods on the same test suites. We also show that our results are statistically significant, validating that our approach substantially improves the performance of existing SBFL techniques. [ABSTRACT FROM AUTHOR]

The objective of the research was to find if it was possible to apply fuzz testing, a technique that can be used to test software to discover vulnerabilities, on an embedded avionic software using an open-source fuzz tool. The open-source fuzz tool AFL++ was applied to an NH90 Airbus Helicopter embedded software component to find the vulnerabilities. The proposed setup was able to find a few crashes related to data parsing associated with Ada strong typing declaration requirement. Moreover, the experiments outline practical guidelines and considerations for implementing fuzz testing on embedded avionic software. [ABSTRACT FROM AUTHOR]

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

The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L'Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows. [ABSTRACT FROM AUTHOR]

Software testing is a crucial step in the development of software that guarantees the dependability and quality of software products. A crucial step in software testing is test case minimization, which seeks to minimize the number of test cases while ensuring maximum coverage of the system being tested. It is observed that the existing algorithms for test case minimization still suffer in efficiency and precision. This paper proposes a new optimization algorithm for efficient test case minimization in software testing. The proposed algorithm is designed on the base parameters of the metaheuristic algorithms, inspired by scientific principles. We evaluate the performance of the proposed algorithm on a benchmark suite of test cases from the literature. Our experimental results show that the proposed algorithm is highly effective in reducing the number of test cases while maintaining high coverage of the system under test. The algorithm outperforms the existing optimization algorithms in terms of efficiency and accuracy. We also conduct a sensitivity analysis to investigate the effect of different parameters on the performance of the proposed algorithm. The sensitivity analysis results show that the performance of the algorithm is robust to changes in the parameter values. The proposed algorithm can help software testers reduce the time and effort required for testing while ensuring maximum coverage of the system under test. [ABSTRACT FROM AUTHOR]

The reliability of AI-based object detection models has gained interest with their increasing use in safety-critical systems and the development of new regulations on artificial intelligence. To meet the need for robustness evaluation, several authors have proposed methods for testing these models. However, applying these methods in industrial settings can be difficult, and several challenges have been identified in practice in the design and execution of tests. There is, therefore, a need for clear guidelines for practitioners. In this paper, we propose a method and guidelines for assessing the robustness of AI-based 2D object detection systems, based on the Goal Question Metric approach. The method defines the overall robustness testing process and a set of recommended metrics to be used at each stage of the process. We developed and evaluated the method through action research cycles, based on two industrial cases and feedback from practitioners. Thus, the resulting method addresses issues encountered in practice. A qualitative evaluation of the method by practitioners was also conducted to provide insights that can guide future research on the subject. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper. [ABSTRACT FROM AUTHOR]

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

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas. [ABSTRACT FROM AUTHOR]

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

Software testing is an important phase as it ensures the software quality. The software testing process comprises of three steps: generation, execution, and evaluation of test cases. Literature claims the usage of single and multiple 'Unified Modeling Language' (UML) diagrams to generate test cases. Using multiple UML diagrams increases test case coverage. However, the existing approaches show limitations in test case generation from UML diagrams. Therefore, in this research study, we propose an approach to generate the test cases using UML State Chart Diagram (SCD), Activity Diagram (AD), and Sequence Diagram (SD). The proposed approach transforms UML diagrams into intermediate forms: SCD Graph, AD Graph, and SD Graph respectively. Furthermore, by integrating these three graphs, a System Testing Graph (STG) is formed. Finally, test cases are identified from STG by using a traversal algorithm such as Depth First Search (DFS) that is an optimization method. The results show that the proposed approach is better compared to existing approaches in terms of coverage and performance. Moreover, the generated test cases have the ability to detect faults at the unit level, integration, and system level testing. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

New rapid, reliable, and cost-effective alternative systems are needed for the rapid diagnosis of Streptococcus pyogenes. The aim of this study was to fabricate a microfluidic test device to detect Streptococcus pyogenes by combining the Loop-mediated isothermal amplification method via a 3D printer. Microfluidic test devices were designed in CATIA V5 Release 16 software, and data were directly transferred to a 3D printer and produced using the FDM method with biocompatible PLA filament. The S. pyogenes ATCC 19615 and different ATCC strains was used. Following identification by classical culture methods, a 0.5 McFarland suspension was prepared from the colonies, and DNA isolation was performed from this liquid by a boiling method. S. pyogenes specific speB gene was used to desing LAMP primer sets in PrimerExplorer V5 software and tested on a microfluidic device. LAMP reactions were performed on microfluidic device and on a microcentrifuge tube separately. Both results were analyzed using the culture method as the standard method to diagnostic values. Melting curve analysis of the amplicons of the LAMP reactions performed on a LightCycler 480 system to detect amplification. Among the 50 positive and 100 negative samples, only four samples were found to be false negative by LAMP reaction in a microcentrifuge tube, while eight samples were found to be false negative by LAMP reaction on a microfluidic device. Six samples were found to be false positive by the LAMP reaction in the microcentrifuge tube, while ten samples were found to be false positive by the LAMP reaction on a microfluidic chip. The sensitivity, specificity, positive predictive value, and negative predictive value of the LAMP reactions performed in the microcentrifuge tube and on the microfluidic device were 92–84%, 94–90%, 88.46–80.77%, and 95.92–91.84%, respectively. The limit of detection (LOD) was found to be the same as 1.5 × 102 CFU/mL and the limit of quantification (LOQ) values of the LAMP reactions were performed on the microcentrifuge tube and on the microfluidic device were 2.46 × 102–7.4 × 102 CFU/mL, respectively. Cohen's kappa (κ) values of the LAMP reactions were performed on the microcentrifuge tube and on the microfluidic device were 0.620–0.705, respectively. In conclusion, our data showed that the LAMP method can be combined with microfluidic test device to detect S. pyogenes, this microfluidic device can be manufactured using 3D printers and results are close to gold standard methods. These devices can be combined with LAMP reactions to detect different pathogens where resources are limited and rapid results are required. [ABSTRACT FROM AUTHOR]