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HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade. [ABSTRACT FROM AUTHOR]

Krillscan software was developed to automatically process echosounder data and achieve an accelerated and transparent analysis of backscatter data that allows calculation of target biomass. Herein, the fishery for Antarctic krill (Euphausia superba, Henceforth Krill) was used as a case study to develop the approach. Implementation of a sustainable management strategy for the krill fishery is complicated by a lack of regularly updated krill abundance data on spatiotemporal scales of the fishery. To increase krill biomass data availability, automatic echosounder data processing and swarm detection software was tested against traditional manual scrutinization with LSSS software and agreed with only minor offsets in estimated nautical area scattering coefficients. In addition to automatic processing and data transfer, Krillscan also has a graphical user interface to supervise automatic krill swarm detection. Echogram size can be compressed up to 100 times and raw data are processed faster than generated, thereby enabling near‐real time analysis and data transfer. Compressed data can be transmitted online to allow fishing vessels to conduct surveys without having scientific personnel with special expertise on board. [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]

The purpose of this study is to identify and classify studies published on software testing techniques applied to blockchain systems. Previously published reviews in related areas have a narrow focus and/or do not follow a systematic review protocol. We conducted a systematic mapping based on an initial selection of 1025 studies. A rigorous selection process resulted in a final pool of 17 primary studies. These studies are categorized with respect to the employed testing methods, considered quality attributes, and functionality. We observe that most of the publications focus on testing functional correctness or security, whereas the testing of runtime performance attracts less attention. Existing approaches mostly employ fuzz testing or mutation testing. Search-based testing is usually combined with these techniques. The application of model-based testing is rare. The adaptability of fuzz testing and model-based testing techniques to changing blockchain platforms and languages remains a concern. On the other hand, performance and scalability issues are noted for search-based techniques and mutation testing. The use and integration of multiple testing techniques also stand out as a viable research direction. [ABSTRACT FROM AUTHOR]

Crowdsourced software development (CSSD) is a fast-growing field among software practitioners and researchers from the last two decades. Despite being a favorable environment, no intelligent mechanism exists to assign price to CSSD tasks. Software development effort estimation (SDEE) on the other hand is already an established field in traditional software engineering. SDEE is largely facilitated by machine learning (ML), particularly, ML-based ensemble effort estimation (EEE) which targets accurate estimate by avoiding biases of single ML model. This accuracy of EEE can be exploited for CSSD platforms to establish intelligent cost assignment mechanism. This study aims to integrate EEE with CSSD platform to provide justified costing solution for crowdsourced tasks. Effort-based cost estimation model is proposed, implementing EEE to predict task's effort along with natural language processing (NLP) analysis on task's textual description to assign effort-based cost. TopCoder is selected as targeted CSSD platform, and the proposed scheme is implemented on TopCoder QA category comprising software testing tasks. Ensemble prediction is incorporated via random forest, support vector machine and neural network as base learners. LDA topic modeling is utilized for NLP analysis on the textual aspects of CSSD task, with a specific emphasis on the testing and technology factors. Effort estimation results confirm that EEE models, particularly stacking and weighted ensemble, surpass their base learners with 50% overall increased accuracy. Moreover, R2, log-likelihood and topic quality measures confirm considerable LDA model significance. Findings confirmed that cost adjustment achieved from EEE and NLP defines acceptable price range, covering major testing aspects. [ABSTRACT FROM AUTHOR]

Dysfunction of the facial musculature can have significant physical, social, and psychological consequences. In surgeries such as cleft surgery or craniofacial bimaxillary osteotomies, the perioral facial muscles may be detached or severed, potentially altering their functional vectors and mimicry capabilities. Ensuring correct reconstruction and maintenance of anatomical sites and muscle vectors is crucial in these procedures. However, a standardized method for perioperative assessment of the facial musculature and function is currently lacking. The aim of this study was to develop a workflow to analyse the three-dimensional vectors of the facial musculature using magnetic resonance imaging (MRI) scans. A protocol for localizing the origins and insertions of these muscles was established. The protocol was implemented using the 3DMedX computer program and tested on 7 Tesla MRI scans obtained from 10 healthy volunteers. Inter- and intra-observer variability were assessed to validate the protocol. The absolute intra-observer variability was 2.6 mm (standard deviation 2.0 mm), and absolute inter-observer variability was 2.6 mm (standard deviation 1.5 mm). This study presents a reliable and reproducible method for analysing the spatial relationships and functional significance of the facial muscles. The workflow developed facilitates perioperative assessment of the facial musculature, potentially aiding clinicians in surgical planning and potentially enhancing the outcomes of midface surgery. [ABSTRACT FROM AUTHOR]

Beth Clarke MBCS, a BCS SIGiST committee member and PhD student, tells Martin Cooper MBCS how she discovered a love for software testing, how software makes Earth and space safer and why diversity across test teams is critically important. [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]

In this research, authors give a literature analysis of the methods used to detect and anticipate security risks in software testing by using a number of deep learning models. The purpose of this study is to conduct a literature review on the use of deep learning models in software testing for the purpose of detecting and predicting security issues. Moreover, the motive of this work is to analyze the exiting techniques related as software testing model and its application oriented performance. A thorough search of the available literature across several databases and resources forms the basis of this evaluation. There are a total of 69 publications found via the search, 34 of which are original research. Convolutional neural networks, long short-term memory networks, generative adversarial networks, and capsule networks are only some of the state-of-the-art methods for identifying and predicting security risks that are the topic of this research study. Datasets, performance indicators, and assessment criteria are all dissected for this examination. Finally, the benefits and drawbacks of using deep learning models to detect and anticipate software security flaws during testing are outlined in this article. Software testing experts and academics will benefit from this study since it will shed light on the present state of the art in the area and provide direction for the creation of innovative approaches to the detection and forecasting of security risks in software testing. [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]

When creating a software model, it is necessary that it accurately captures the desired behaviour, while at the same time ensuring that any undesired behaviour is excluded. On the one hand, formal verification tools can be used to check the internal consistency of a software system, ensuring that the behaviour of one software component does not contradict another. On the other hand, software testing is essential to check the external validity of the model more comprehensively. Unfortunately, software testing is often overlooked in curricula, resulting in graduates with inadequate software testing skills for industry. Software testing tools such as TesCaV can be used to help teachers teach software testing topics in a non-intrusive and less time-consuming way. Previous research has shown that TesCaV is easy to use and that novice users produce better quality software tests when using TesCaV. However, it has remained unclear whether learners retain the skills they gain from using TesCaV even when the tool is not offered for help. In order to understand the positive effect of TesCaV on learners' software testing skills, this study conducted an experiment with 45 participants. The experiment used a pretest-treatment-posttest design. The results show that participants feel equally confident about the completeness of their test coverage, even though they identify more test cases. It is concluded that for course design, a capsule such as TesCaV can help students to understand the full complexity of software testing and help them to be more systematic in their approach. [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]

Hepatitis C virus core antigen (HCVcAg) testing can simplify and decrease costs of HCV infection confirmation compared to molecular testing (nucleic acid testing). We piloted HCVcAg testing for the confirmation of active infection. The study was conducted during June through December 2022 among the police and the general population of Islamabad, Pakistan age 18 years and older. Initial screening for HCV antibody was conducted using a rapid diagnostic test (RDT) for all consenting participants. Those who tested positive had venous blood samples tested for HCVcAg, platelets and aspartate aminotransferase (AST). Persons with HCVcAg values ≥3 fmol/L were defined as viremic, and they were offered treatment with direct acting antiviral (DAA) medications, sofosbuvir and daclatasvir. Aspartate aminotransferase to platelet ratio index (APRI) was calculated for each HCV infected person, and those with an APRI score <1.5 received treatment for 12 weeks, while those with APRI ≥ to 1.5 received 24 weeks of treatment. A total of 15,628 persons were screened for anti‐HCV using RDT and 643 (4.1%) tested positive. HCVcAg values of ≥3 fmol/L was found in 399/643 (62.1%), and all were offered and accepted treatment. Of those treated, 273/399 (68.4%) returned for a follow‐up SVR and HCVcAg was not detected in 261/273, a 95.6% cure rate. The pilot study demonstrated the effectiveness of reaching and treating an urban population using RDT for screening and HCVcAg for confirmation of infection and test of cure. [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]

Path-coverage oriented testing is a crucial method in software testing, and the rapid generation of high-quality test data to satisfy path coverage requirements has been a persistent research challenge. To address issues such as long running times, unstable exploration processes, and the generation of redundant test cases in existing intelligent optimization methods, this paper proposed a selection strategy based on the reinforcement learning paradigm applied to test data generation with path coverage as the criterion. By defining executable paths as the state of the intelligent agent, it defined the data selection after each iteration update as the agent's action. It associated the reward function with state changes, and employed a greedy strategy during the state update process to guide input data towards continuous variations in unexplored states. This iterative selection process aimed to continuously choose data that covered new executable paths, thereby achieving the goal of covering all execution paths of the target program. Experimental results demonstrate that compared to other algorithms, the proposed strategy significantly reduces running times and iteration counts while achieving notable improvements in coverage. Theoretical analysis supports the conclusion that the proposed strategy effectively realizes path coverage and enhances the efficiency of test data generation in practical applications. [ABSTRACT FROM AUTHOR]

As software projects evolve and grow in size and complexity, so do their test suites. Test suite reduction (TSR) aims at reducing the size of a test suite by removing redundant and obsolete test cases based on a coverage metric while preserving its fault detection capabilities. The contributions of this paper are twofold: (1) We examine a lesser‐known coverage criterion, that is, checked coverage. Checked coverage not only investigates if a part of the code was executed but also if it was checked by a test oracle. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open‐source Java projects with our own TSR tool to determine the most effective and efficient combination of metric and method. (2) Given the results of the first evaluation, we further investigate the potential of parameter optimization in regard to a genetic reduction algorithm. In particular, we focus on finding a general setting for the parameters crossover rate and mutation rate such that test suites can be reduced in a reasonable time while maintaining a high fault detection power. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

As software vulnerabilities can cause cybersecurity threats and have severe consequences, it is necessary to develop effective techniques to discover such vulnerabilities. Fuzzing is one of the most widely employed approaches that has been adapted for software testing. The mutation-based fuzzing approach is currently the most popular. The state-of-the-art American Fuzzy Lop (AFL) selects mutations randomly and lacks knowledge of mutation operations that are more helpful in a particular stage. This study performs a systematic review to identify and analyze existing approaches that optimize the selection of mutation operations. The main contributions of this work are to draw attention to the importance of mutation operator selection, identify optimization algorithms for mutation operator selection, and investigate their impact on fuzzing testing in terms of code coverage and finding new vulnerabilities. The investigation shows the effectiveness and advantages of optimizing the selection of mutation operations to achieve higher code coverage and find more vulnerabilities. [ABSTRACT FROM AUTHOR]

Projections for future air mobility envisage intensely utilized airspace that does not simply scale up from existing systems with centralized air traffic control. This paper considers the implementation and test of a software and hardware framework for decentralized control of aerial vehicles within intensely used airspace. Up to 10 rotary wing vehicles of maximum all up mass of 1 kg are flown in an outdoor volume with length scale of 100 m with GPS and WiFi connectivity. Flight control is implemented using a Pixhawk 4 flight controller running the PX4 firmware with guidance algorithms run on a separate onboard companion computer. Deconfliction is implemented using a simple elastic repulsion model with a guidance update rate of 10 Hz . Traffic structures are constructed from a path of directed waypoints and associated cross sectional geometry. Junctions are implemented when two paths converge into one or when one path diverges into two. Agents engage with structures through execution of flow, merge and swirl velocity rules. Calibration experiments showed that the worst case latency in agents sharing position information was of the order of 0.5 s made up from delays due to finite guidance update rate, WiFi processing and centralized message processing. A choice of vehicle cruise speed of 2 m∕s and conflict radius of 2.5 m provided an acceptable compromise between experiment time efficiency (speed) and spatial efficiency (resolution) within the test volume. Results from recirculating junction experiments show that peak deconfliction activity occurs at the junction node, however biased distribution of agents within a corridor means the peak intensity is pushed ahead of the node. Use of meshed helical junction structures significantly reduces the intensity of conflict at the expense of reduced junction time efficiency. [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]

In software that is free from defects or errors, a fixed method and technique of software testing is required. For this step, an Information System is needed to help eliminate software defects, so that testing or testing work is not a burden on software costs but is a perfection of software development work so that Zero Defect Application Software can be realized. From several software testing methods and techniques, a strategy suitable for the size of a software development project is required. So even though the software development project is small, it still requires a suitable test. And for software development testing to have reliable quality, it is necessary to measure and criteria for each software testing practice, so that a software development project can be determined in quality. To support software testing work, an Information System is needed that supports the need for processors to assess a quality software development test. With the Software Defect Elimination Information System, an objective quality of software development testing can be produced. [ABSTRACT FROM AUTHOR]

Summary: To ensure the quality of software systems, software engineers can make use of a variety of quality assurance approaches, for example, software testing, modern code review, automated static analysis, and build automation. Each of these quality assurance practices have been studied in depth in isolation, but there is a clear knowledge gap when it comes to our understanding of how these approaches are being used in conjunction, or not. In our study, we broadly investigate whether and how these quality assurance approaches are being used in conjunction in the development of 1454 popular open source software projects on GitHub. Our study indicates that typically projects do not follow all quality assurance practices together with high intensity. In fact, we only observe weak correlation among some quality assurance practices. In general, our study provides a deeper understanding of how existing quality assurance approaches are currently being used in Java‐based open source software development. Besides, we specifically zoom in on the more mature projects in our dataset, and generally we observe that more mature projects are more intense in their application of the quality assurance practices, with more focus on their ASAT usage, and code reviewing, but no strong change in their CI usage. [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]

Testing is one of the most time‐consuming and unpredictable processes within the software development life cycle. As a result, many test case optimization (TCO) techniques have been proposed to make this process more scalable. Object Constraint Language (OCL) was initially introduced as a constraint language to provide additional details to Unified Modeling Language models. However, as OCL continues to evolve, an increasing number of systems are being expressed by this language. Despite this growth, a noticeable research gap exists for the testing of systems whose specifications are expressed in OCL. In our previous work, we verified the effectiveness and efficiency of performing the test case prioritization (TCP) process for these systems. In this study, we extend our previous work by integrating the test case minimization (TCM) process to determine whether TCM can also benefit the testing process under the context of OCL. The evaluation of TCO approaches often relies on well‐established metrics such as the average percentage of fault detection (APFD). However, the suitability of APFD for model‐based testing (MBT) is not ideal. This paper addresses this limitation by proposing a modification to the APFD metric to enhance its viability for MBT scenarios. We conducted four case studies to evaluate the feasibility of integrating the TCM and TCP processes into our proposed approach. In these studies, we applied the multi‐objective optimization algorithm NSGA‐II and the genetic algorithm independently to the TCM and TCP processes. The objective was to assess the effectiveness and efficiency of combining TCM and TCP in enhancing the testing phase. Through experimental analysis, the results highlight the benefits of integrating TCM and TCP in the context of OCL‐based testing, providing valuable insights for practitioners and researchers aiming to optimize their testing efforts. Specifically, the main contributions of this work include the following: (1) we introduce the integration of the TCM process into the TCO process for systems expressed by OCL. This integration benefits the testing process further by reducing redundant test cases while ensuring sufficient coverage. (2) We comprehensively analyze the limitations associated with the commonly used metric, APFD, and then, a modified version of the APFD metric has been proposed to overcome these weaknesses. (3). We systematically evaluate the effectiveness and efficiency of OCL‐based TCO processes on four real‐world case studies with different complexities. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence (AI) is revolutionizing various industries, including IT project management. The object of research is the principle of augmented competence, which is a new approach that uses AI to strengthen and expand the capabilities of IT project teams. The essence of this principle lies in the complementary interaction of AI and the competence of project teams. Instead of replacing project managers, AI complements their competencies (knowledge, skills and experience). One of the hot spots is the application of AI in the process of automating routine tasks, analyzing large volumes of data and providing recommendations and predictions, freeing up time for team members to focus on more complex and creative tasks. The possibility of automating tasks and providing new knowledge, which will significantly improve the efficiency and productivity of the team, has been obtained through the use of the principle of augmented competence. As a result, data-driven recommendations and predictions enable teams to make more informed and effective decisions. Access to new knowledge and insights stimulates innovation and leads to new ideas and solutions, helps identify and mitigate potential risks, which can lead to more successful projects. Applying this principle to IT project management audits will automate software testing with AI, which replaces testers so they can focus on more complex types of testing such as exploratory testing, performs customer data analysis with AI, and enables companies to better understand your customers and their needs, which can lead to better marketing campaigns and products. It is important to note that this principle does not involve replacing project managers with AI. Instead, AI is used as a tool to empower human teams and help them achieve better results. As AI technologies continue to evolve, the principle of augmented competence is likely to play an even more important role in IT project management. AI can help teams overcome complex challenges, make better decisions, and succeed in a more dynamic and competitive environment. [ABSTRACT FROM AUTHOR]

During scrum iterations, it is possible to apply cost forecasting for software testing and operation, if the data from previous iterations are known. Since the data for estimating the scope of work and the deadline within one sprint are accumulated during the project execution, it is possible to use such data to build a forecasting algorithm for the estimated parameters of the subsequent sprints. The approach is based on refining the assessment provided by the development team and the scrum master in a specific metric. The main parameters for evaluation are the execution time and the amount of work performed. As a result of forecasting, it is possible to obtain clarifications for the team’s assessment regarding the scope of work for the next sprint. This estimate is based on planned and actual data from the previous sprints. The article discusses the method of least squares and the proposed code for a machine learning model based on this method. An example and graphs for iterations in scrum and corresponding forecasting for the next sprints are presented. The use of the least squares method allows creating a mathematical model that can be adapted to different project conditions, providing flexibility and accuracy in forecasting. For example, the study uses the real data from the previous sprints, which includes the team’s resource assessment and actual expenditures. Based on these data, a model was built that demonstrates a high correlation between predicted and actual costs, confirming the effectiveness of using the least squares method. So, the least squares method is an effective tool for forecasting software development costs in scrum iterations. This method allows development teams to better plan their resources and timelines, contributing to the overall efficiency of the project [ABSTRACT FROM AUTHOR]

Micro-mixers and piezoelectric pumps have been studied as independent parts, which achieves only single mixing or pumping functions. A new valveless piezoelectric mixing pump (VPMP) driven by a sinusoidal signal is proposed in this paper to achieve a structure with both pumping and mixing functions. It accomplishes this by placing obstacles in the pump chamber and in the flow channel then adding active mixing. Because it is small in size, has a simple structure and is easy to control, it can be used in micro mixing applications. In this study, numerical simulation analysis using the Fluent software tests if the new VPMP has good mixing and pumping performance, and analyzes the effect of the obstacles. Simultaneously, the results of numerical simulation are verified with experiments. It was proven that the flow rate of the VPMP can reach 12.26 mL/min, and that the liquid can achieve uniform mixing. Moreover, the obstacle placed in the pump chamber has a more obvious impact on the mixing capability, and the obstacle placed in the channel has a more obvious impact on the flow rate. In addition, with the increase of the amplitude of driving signal, the flow rate of the VPMP increased correspondingly. With the increase of the frequency, the flow rate first increased and then decreased. The capability of mixing increased with an increase of working frequency. The experimental results showed that the VPMP can achieve good pumping and mixing performance, and that one structure can possess both mixing and pumping functions. [ABSTRACT FROM AUTHOR]

With the rapid growth of modern retail in Pekanbaru, where Lotte Grosir is one of the many existing retailers. The company must understand the importance of analyzing the marketing mix in relation to customer satisfaction to innovate and face competition. In this context, the marketing mix includes product, location, price and promotion, known as 4Ps. The purpose of this study is to analyze whether the components of the marketing mix have an impact on customer satisfaction, providing a descriptive overview of the current conditions and serving as a consideration for the company in improving customer satisfaction. This research uses a descriptive quantitative method. The sample selected in this study consist of Lotte Grosir Pekanbaru customers using the Simple Random Sampling method, resulting in 105 respondents who filled out the questionnaire. Data were analyzed using instrument test, validity test, reliability test, classical assumption test, multiple linear regression, and hypothesis testing with SPSS software. The result show that partially, the product variable (X1) does not significantly affect customer satisfaction (Y), while the location (X2), price (X3) and promotion (X4) variables have an impact on customer satisfaction (Y) variable. Other results indicate that simultaneously, product location, price and promotion have an influence on customer satisfaction with a coefficient of determination of 81.8% and the remaining 18.2% is influenced by others factors not examined in this study. [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]

To build highly reliable software products, software testing is required. The software engineer has a problem determining whether to complete the testing process and when to release the software system to the market. As the focus on high software increases, project managers must consider the cost of testing, testing availability time, and release time strategy. This research article utilizes a phase type non-homogeneous Poisson process to investigate the cost analysis and operational availability of a software reliability model. The software failure inter-arrival time is considered to follow a phase type distribution. The phase type software reliability model is valuable in reducing the time and effort required to select the appropriate models for software reliability evaluation. An explicit expression for the expected, bi-criterion cost analysis and operational availability is derived using standard results of the renewal reward theorem. The optimal software release policy T* is obtained analytically. Numerical illustrations and sensitivity analysis have been presented to illustrate the cost and availability analysis's efficiency and demonstrate conformity with the study's observations. [ABSTRACT FROM AUTHOR]

Graphical user interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, exploratory testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience‐based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the diversity of testers' performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re‐executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step toward filling this gap in the literature, in this work, we conduct an empirical evaluation involving four open‐source Android apps and 20 masters students that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a capture and replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage that different‐sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions. [ABSTRACT FROM AUTHOR]

Determining "software release time" and "testing stop time" is a significant challenge in software projects, as both greatly affect the software cost and reliability. To overcome the drawbacks of past research, this study presents a novel robust optimization approach considering the interval estimation of input parameters for a software reliability growth model. It aims to detect the optimal "software release time" and "testing stop time" to minimize software development costs in an uncertain environment. Additionally, it considers the time value of money for calculating model costs by considering the interest rate and inflation. Generally, this research is the first attempt to use a robust approach for optimizing the software development cost considering the time value of money. The paper investigates the model efficiency in practical situations through a case study and analyzes the effect of the discounted rate and parameters uncertainty on the development cost using a software reliability growth model. The results confirm the prominent role of uncertain parameters and the discounted rate value on software development cost. They also indicate that the proposed mathematical model is more consistent with the actual situation and flexible than the past models with deterministic parameters. [ABSTRACT FROM AUTHOR]

The impact of a group of parallel or connected faults causes the creation of fault zones. The effect of brittle spots on the region's rocks differs, and the amount of stress in other parts of the fault varies. In addition to the different amounts of stress in other parts of the fault, the fracture rate of the rock mass in the fault area is variable in such a way that the parts that have not been deformed, in the form of blocks surrounded by severely crushed elements that create a weaker matrix and cause Inhomogeneity in the brittle fault zone, which is called bimrock. Bimrock (block-in-matrix rock) includes mélanges, fault rocks, weathered rocks, and other complex geological mixtures in strong blocks surrounded by a weaker matrix. Investigation and research on the geometry of brittle faults and the impact of these faults on the rocks of the region and establishing a relationship between the mechanism of faults and resistance characteristics is necessary. In this research, we did the geometrical analysis of the North Tehran fault and the Pourkan Verdij fault by examining fault branches and s-c structures in the fault zone. Also, the geomechanical properties of the fault rocks and Bimrocks in the area were investigated using two-dimensional analysis of the outcrop images of the fault zone by "Image Analysis" software and rock mechanics tests. As a result, the survey conducted in the cross-section of these two faults from the front side of the fault zone to the end of the fault zone showed that the volume ratio and uniaxial strength of the fault stone blocks of the North Tehran fault zone show regular changes. In such a way that as we move from the front of the site to the end of the fault zone, the amount of rock crushing decreases, and as a result, the volume ratio of the blocks and their resistance increases. While in the Porkan-Verdij fault zone, these changes are not visible in a particular order from the forehead to the end of the site, and the amount of these changes is sudden and less than in the North Tehran fault zone. This is related to the displacement rate created by the fault, the presence of micro-faults, and the distance of the shear planes relative to each other. This study can solve the most severe geotechnical problems related to rock engineering caused by brittle faults. [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]

Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on metamorphic relations (MRs) that govern multiple related inputs and their outputs. However, designing MRs is challenging, as it requires domain-specific knowledge. This hinders the widespread adoption of MT. We observe that developer-written test cases can embed domain knowledge that encodes MRs. Such encoded MRs could be synthesized for testing not only their original programs but also other programs that share similar functionalities. In this article, we propose MR-Scout to automatically synthesize MRs from test cases in open-source software (OSS) projects. MR-Scout first discovers MR-encoded test cases (MTCs), and then synthesizes the encoded MRs into parameterized methods (called codified MRs), and filters out MRs that demonstrate poor quality for new test case generation. MR-Scout discovered over 11,000 MTCs from 701 OSS projects. Experimental results show that over 97% of codified MRs are of high quality for automated test case generation, demonstrating the practical applicability of MR-Scout. Furthermore, codified-MRs-based tests effectively enhance the test adequacy of programs with developer-written tests, leading to 13.52% and 9.42% increases in line coverage and mutation score, respectively. Our qualitative study shows that 55.76% to 76.92% of codified MRs are easily comprehensible for developers. [ABSTRACT FROM AUTHOR]

Testing Autonomous Driving Systems (ADSs) is crucial to ensure their reliability when navigating complex environments. ADSs may exhibit unexpected behaviours when presented, during operation, with driving scenarios containing features inadequately represented in the training dataset. To address this shift from development to operation, developers must acquire new data with the newly observed features. This data can be then utilised to fine tune the ADS, so as to reach the desired level of reliability in performing driving tasks. However, the resource-intensive nature of testing ADSs requires efficient methodologies for generating targeted and diverse tests. In this work, we introduce a novel approach, DeepAtash-LR, that incorporates a surrogate model into the focused test generation process. This integration significantly improves focused testing effectiveness and applicability in resource-intensive scenarios. Experimental results show that the integration of the surrogate model is fundamental to the success of DeepAtash-LR. Our approach was able to generate an average of up to 60× more targeted, failure-inducing inputs compared to the baseline approach. Moreover, the inputs generated by DeepAtash-LR were useful to significantly improve the quality of the original ADS through fine tuning. [ABSTRACT FROM AUTHOR]

Software vulnerabilities (SVs) have become a common, serious, and crucial concern due to the ubiquity of computer software. Many AI-based approaches have been proposed to solve the software vulnerability detection (SVD) problem to ensure the security and integrity of software applications (in both the development and testing phases). However, there are still two open and significant issues for SVD in terms of (i) learning automatic representations to improve the predictive performance of SVD, and (ii) tackling the scarcity of labeled vulnerability datasets that conventionally need laborious labeling effort by experts. In this paper, we propose a novel approach to tackle these two crucial issues. We first exploit the automatic representation learning with deep domain adaptation for SVD. We then propose a novel cross-domain kernel classifier leveraging the max-margin principle to significantly improve the transfer learning process of SVs from imbalanced labeled into imbalanced unlabeled projects. Our approach is the first work that leverages solid body theories of the max-margin principle, kernel methods, and bridging the gap between source and target domains for imbalanced domain adaptation (DA) applied in cross-project SVD. The experimental results on real-world software datasets show the superiority of our proposed method over state-of-the-art baselines. In short, our method obtains a higher performance on F1-measure, one of the most important measures in SVD, from 1.83% to 6.25% compared to the second highest method in the used datasets. [ABSTRACT FROM AUTHOR]

Defect predictors, static bug detectors, and humans inspecting the code can propose locations in the program that are more likely to be buggy before they are discovered through testing. Automated test generators such as search-based software testing (SBST) techniques can use this information to direct their search for test cases to likely buggy code, thus speeding up the process of detecting existing bugs in those locations. Often the predictions given by these tools or humans are imprecise, which can misguide the SBST technique and may deteriorate its performance. In this article, we study the impact of imprecision in defect prediction on the bug detection effectiveness of SBST. Our study finds that the recall of the defect predictor, i.e., the proportion of correctly identified buggy code, has a significant impact on bug detection effectiveness of SBST with a large effect size. More precisely, the SBST technique detects 7.5 fewer bugs on average (out of 420 bugs) for every 5% decrements of the recall. However, the effect of precision, a measure for false alarms, is not of meaningful practical significance, as indicated by a very small effect size. In the context of combining defect prediction and SBST, our recommendation is to increase the recall of defect predictors as a primary objective and precision as a secondary objective. In our experiments, we find that 75% precision is as good as 100% precision. To account for the imprecision of defect predictors, in particular low recall values, SBST techniques should be designed to search for test cases that also cover the predicted non-buggy parts of the program, while prioritising the parts that have been predicted as buggy. [ABSTRACT FROM AUTHOR]

Quantum computing has emerged as a promising field with the potential to revolutionize various domains by harnessing the principles of quantum mechanics. As quantum hardware and algorithms continue to advance, developing high-quality quantum software has become crucial. However, testing quantum programs poses unique challenges due to the distinctive characteristics of quantum systems and the complexity of multi-subroutine programs. This article addresses the specific testing requirements of multi-subroutine quantum programs. We begin by investigating critical properties by surveying existing quantum libraries and providing insights into the challenges of testing these programs. Building upon this understanding, we focus on testing criteria and techniques based on the whole testing process perspective, spanning from unit testing to integration testing. We delve into various aspects, including IO analysis, quantum relation checking, structural testing, behavior testing, integration of subroutine pairs, and test case generation. We also introduce novel testing principles and criteria to guide the testing process. We conduct comprehensive testing on typical quantum subroutines, including diverse mutants and randomized inputs, to evaluate our proposed approach. The analysis of failures provides valuable insights into the effectiveness of our testing methodology. Additionally, we present case studies on representative multi-subroutine quantum programs, demonstrating the practical application and effectiveness of our proposed testing principles and criteria. [ABSTRACT FROM AUTHOR]

Voice-based virtual assistants are becoming increasingly popular. Such systems provide frameworks to developers for building custom apps. End-users can interact with such apps through a Voice User Interface (VUI), which allows the user to use natural language commands to perform actions. Testing such apps is not trivial: The same command can be expressed in different semantically equivalent ways. In this article, we introduce VUI-UPSET, an approach that adapts chatbot-testing approaches to VUI-testing. We conducted an empirical study to understand how VUI-UPSET compares to two state-of-the-art approaches (i.e., a chatbot testing technique and ChatGPT) in terms of (i) correctness of the generated paraphrases, and (ii) capability of revealing bugs. To this aim, we analyzed 14,898 generated paraphrases for 40 Alexa Skills. Our results show that VUI-UPSET generates more bug-revealing paraphrases than the two baselines with, however, ChatGPT being the approach generating the highest percentage of correct paraphrases. We also tried to use the generated paraphrases to improve the skills. We tried to include in the voice interaction models of the skills (i) only the bug-revealing paraphrases, (ii) all the valid paraphrases. We observed that including only bug-revealing paraphrases is sometimes not sufficient to make all the tests pass. [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]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Educational CubeSats still remain valuable hands-on practice activity in an academic environment and provide effective and innovative solutions from technical and management points of view. Fast delivery and low cost is the paradigm driving such projects. Contemporary, the simplicity of the solutions has to match with reliability to guarantee the success of the on orbit operations. The present paper shows the entire life cycle of a Sensing Suite System suitable as scientific data collector within a CubeSat, developed in less than four months from the conceptual design to the delivery and reaching the orbit in less than six months. Technical solutions for hardware and software development and test and management good practices are provided. Circuits details to withstand and/or tolerate the space environment and data handling to maximize the number of data and measurements gathered onboard are explained. The use of breadboards and flat-sat arrangements support both the design and verification of the protoflight comprising the schedule and increasing the confidence level on the goodness of the entire solution. The reliability of the design is proven through the entire assembly, integration and verification campaign and validated by the data sent by the satellite during the operative mission. • Fast delivery and low budget educational cubesat payload is presented. • Simple and reliable avionics system for small satellites is provided with details on software and hardware development. • Derails of schedule definition and management with the related lesson learned are shown. [ABSTRACT FROM AUTHOR]

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

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

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