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Video surveillance is a crucial part of public safety and security systems. State-of-the-art techniques either use back-tracing for owner identification or there is no provision of reporting ownership of the abandoned objects. We provide a novel object association technique which very elegantly tackles this issue. We propose a convolution neural network based framework for abandoned object localization and owner identification in video surveillance systems that performs exceptionally well on publicly available datasets and our newly developed dataset. For testing the usability and robustness of these systems, an extensive set of test cases is a prerequisite, to this end we provide an elaborate surveillance dataset covering many complex cases which are not available in existing datasets. Also, the developed dataset has been tested with various state-of-the-art techniques, showing the complexity and challenging nature of the cases. Results obtained from the proposed framework are highly motivating as it provides event detection along with the time of abandonment and ownership information, which can help further investigation and prevent unwanted incidents.

В роботі визначено фактори, що впливають на проектування та якість відтворення веб-ресурсів, систематизовано технології створення веб-ресурсів та види їх тестування, проведено експериментальне дослідження автоматизованого та ручного тестування, увиразнено процедуру тестування веб-ресурсів, розроблено тест-кейс для перевірки веб-сторінок, досліджено час, потрібний для розроблення односторінкових веб-ресурсів. Надано рекомендації з особливостей наповнення контентом веб-сторінок для найшвидшого їх відтворення на пристроях користувачів. The paper identifies the factors influencing the design and reproduction of web resources, systematizes technologies for creating web resources and types of testing, conducted an experimental study of automated and manual testing. Typical page structures have been developed for selected species. The procedure for testing web resources was clarified, a test case to verify web pages was developed, the time required to develop one-page web resources was explored. Recommendations on the features of filling the content of web pages for the fastest playback on users’ devices were developed.

Near-field communication (NFC) is a set of communication protocols that enable two electronic devices. Its security and reliability are welcomed by mobile terminal manufactures, banks, telecom operators, and third-party payment platforms. Simultaneously, it has also drawn more and more attention from hackers and attackers, and NFC-enabled devices are facing increasing threats. To improve the security of the NFC technology, the paper studied the technology of discovering security vulnerabilities of NFC Data Exchange Format (NDEF), the most important data transmission protocol. In the paper, we proposed an algorithm, GTCT (General Test Case Construction and Test), based on fuzzing to construct test cases and test the NDEF protocol. GTCT adopts four strategies to construct test cases, manual, generation, mutation, and “reverse analysis,” which can detect logic vulnerabilities that fuzzing cannot find and improve the detection rate. Based on GTCT, we designed an NDEF vulnerability discovering framework and developed a tool named “GNFCVulFinder” (General NFC Vulnerability Finder). By testing 33 NFC system services and applications on Android and Windows Phones, we found eight vulnerabilities, including DoS vulnerabilities of NFC service, logic vulnerabilities about opening Bluetooth/Wi-Fi/torch, design flaws about the black screen, and DoS of NFC applications. Finally, we give some security suggestions for the developer to enhance the security of NFC.

In this research, an innovative comparison between 3D reconstructions obtained by means of Artificial Intelligence, in particular NeRF Neural Networks, and by Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) open-source algorithms is proposed. The 3D reconstruction comparison is performed on two test cases, one of cultural interest, one useful only for technical discussion. It is known that the approaches are traditionally used with different objectives and in different contexts but they can however also be used with similar purpose, i.e., 3D reconstruction. In particular, we were interested in evaluating how NeRF reconstructions are accurate from a metric point of view and how the models obtained from the application of NeRF differ from the model obtained from the classical photogrammetry. By analyzing the results in the considered test cases, we show how NeRF networks, although computationally demanding, can be an interesting alternative or complementary methodology, especially in cases where classical photogrammetric techniques do not allow satisfactory results to be achieved. It is therefore suggested to expand efforts in this direction by exploiting, for example, the numerous improvement proposals of the original NeRF network.

This article lists the basic crystallographic equations underlying molecular-replacement software. The prospects for future developments are considered, and a brief account of past progress is included., This article provides an introduction to the crystal phasing technique known as molecular replacement. The available software is reviewed, and the prospects for future developments are considered. Several examples are described in detail to illustrate potential problems. A brief account of past progress is included. The basic crystallographic equations underlying the procedures are given in an appendix.

For effective fault localization, we propose a modified Fisher’s test-based statistical method that makes use of test execution results as well as statement coverage information to determine the suspiciousness of each executable statement. Our technique returns a rank list of statements based on their suspiciousness of containing a fault. We also discuss an extension to our proposed approach for localizing programs with multiple faults. This involves partitioning the failed test cases into clusters such that they target different faults. Our experimental studies show that on an average, our proposed fault localization technique requires examination of 37.09% less code than existing techniques for localizing faults.

The peculiarities of software product testing processes while working in the Scrum-team were studied. The types of testing characteristic of the flexible testing technique are singled out, that make it possible to make the testing process constantly integrated into teamwork, both at the stage of requirements formation and design, and at the stage of coding and generation of test sets. This is possible by making the tester's recommendations in the process of creating user stories, planning the release of the software version in terms of testing and defects, planning a sprint based on user stories and defects, performing a sprint with continuous testing, regression testing after sprint ends and reporting of results of testing. The article highlights the stages of the flexible testing process in the Scrum team. It is proposed to automate the process of flexible testing for a training team project in the Visual Studio software environment based on Team Foundation Server. Author demonstrates an example of automation of the process of flexible testing by dividing it into four quadrants to comply with the principles of working on a flexible methodology of software development. In the first quadrant there is a study of the quality of the internal code of the software product, the modular testing. To implement modular testing in Visual Studio, a new solution is created in order to generate a class for testing, where the conditions for testing are prescribed in the body of the class. The second quadrant implements the principles of system testing. At this stage the features of creating such a test artifact as test cases are demonstrated. That provides for the fulfillment of certain conditions to verify the functionality of the developing software product; linking between created test cases to user stories presented as team project tasks Product Backlog. The study demonstrates an example of manual testing using a special tool Microsoft Test Manager, which allows you to create plans, add and update test cases, perform manual tests. In the third quadrant, it was performed a trial test in Microsoft Test Manager and it was created another test artifact - error reports. The fourth quadrant automatically tests non-functional software requirements.

The digital transformation of all the conventional systems in the field, such as healthcare, transportation and education, has led to the massive outflow of data. The bulk of data generated by the digital devices is challenging to handle, resulting in latency issues while provisioning services to the end-users. To address this issue, a promising solution is provided by osmotic computing, which exploits as well as integrates cloud, edge, and Internet of things platforms. Osmotic computing is a growing research domain that has laid the foundation of a new state-of-the-art computing paradigm by objectifying the scenarios for handling hefty data and computations according to user’s requirement and resource availability. However, the eclectic range of computational resources and services available between datacenters of different platforms in the federated environment is a tedious task to manage. In this paper, we propose a task mapping algorithm by incorporating osmotic computing principles to complete the tasks in latency-sensitive systems. We present an osmotic task manager, which aims to optimize the mapping decisions for all the services based on two-fold categorization, namely task-level categorization and processing-level categorization. The objective is to categorize all the incoming requests on the basis of their resource requirement and efficient mapping of the tasks to resources. The proposed algorithm is simulated and evaluated using various synthetic test cases to show its efficacy.

This paper analyses the contribution of residual network (ResNet) based convolutional neural network (CNN) architecture employed in two tasks related to plant phenotyping. Among the contemporary works for species recognition (SR) and infection detection of plants, the majority of them have performed experiments on balanced datasets and used accuracy as the evaluation parameter. However, this work used an imbalanced dataset having an unequal number of images, applied data augmentation to increase accuracy, organised data as multiple test cases and classes, and, most importantly, employed multiclass classifier evaluation parameters useful for asymmetric class distribution. Additionally, the work addresses typical issues faced such as selecting the size of the dataset, depth of classifiers, training time needed, and analysing the classifier’s performance if various test cases are deployed. In this work, ResNet 20 (V2) architecture has performed significantly well in the tasks of Species Recognition (SR) and Identification of Healthy and Infected Leaves (IHIL) with a Precision of 91.84% and 84.00%, Recall of 91.67% and 83.14% and F1 Score of 91.49% and 83.19%, respectively.

In light of the importance of helium production in influencing the behaviour of fast reactor fuels, in this work we present a burn-up module with the objective to calculate the production of helium in both in-pile and out-of-pile conditions tracking the evolution of 23 alpha-decaying actinides. This burn-up module relies on average microscopic cross-section look-up tables generated via SERPENT high-fidelity calculations and involves the solution of the system of Bateman equations for the selected set of actinide nuclides. The results of the burn-up module are verified in terms of evolution of actinide and helium concentrations by comparing them with the high-fidelity ones from SERPENT, considering two representative test cases of (U,Pu)O2 fuel in fast reactor conditions. In addition, a code-to-code comparison is made with the independent state-of-the-art module TUBRNP (implemented in the TRANSURANUS fuel performance code) for the same test cases. The herein presented burn-up module is available in the SCIANTIX code, designed for coupling with fuel performance codes.

Existing test case prioritization (TCP) techniques have limitations when applied to real-world projects, because these techniques require certain information to be made available before they can be applied. For example, the family of input-based TCP techniques are based on test case values or test script strings; other techniques use test coverage, test history, program structure, or requirements information. Existing techniques also cannot guarantee to always be more effective than random prioritization (RP) that does not have any precondition. As a result, RP remains the most applicable and most fundamental TCP technique. This article proposes an extremely simple, effective, and efficient way to prioritize test cases through the introduction of a dispersity metric. Our technique is as applicable as RP. We conduct empirical studies using 43 different versions of 15 real-world projects. Empirical results show that our technique is more effective than RP. Our algorithm has a linear computational complexity and, therefore, provides a practical solution to the problem of prioritizing very large test suites (such as those containing hundreds of thousands, or millions, of test cases), where the execution time of conventional nonlinear prioritization algorithms can be prohibitive. Our technique also provides a practical solution to TCP when neither input-based nor execution-based techniques are applicable due to lack of information.

Natural language processing (NLP), as a theory-motivated computational technique, has extensive applications. Automated test case generation based on path coverage, which is a popular structural testing activity, can automatically reveal logic defects that exist in NLP programs and can save testing consumption. NLP programs have many paths that can only be covered by specific input variables. This feature makes conventional search-based algorithm very difficult covering all possible paths in NLP programs. A strategy is required for improving the search ability of search-based algorithms. In this paper, we propose a scatter search strategy to automatically generate test cases for covering all possible paths of NLP programs. The scatter search strategy empowers search-based algorithms to explore all input variables and cover the paths that require specific input variables within a small amount of test cases. The experiment results show that the proposed scatter search strategy can quickly cover the paths, which requires specific input variables. Many test cases and running time consumptions will be saved when search-based algorithms combine with scatter search strategy.

Acoustic metamaterials are becoming promising solutions for many industry applications, but the gap between theory and practice is still difficult to close. This research proposes an optimization methodology of acoustic metamaterial designs for sound insulation that aims to start bridging this gap. The proposed methodology takes advantage of a hybrid analytical-numerical approach for computing the sound transmission loss of the designs efficiently. As a result, the implementation of optimization techniques on numerical model designs becomes practically possible. This is exemplified with two test cases: (i) optimization of the sound transmission loss of a single gypsum board panel and (ii) optimization of the noise reduction of outdoor HVAC units. Two resonator designs, one used previously for sound radiation in flat panel speakers and the other for enhancing the sound transmission loss at the mass-air-mass resonance of double panels, are here optimized for the two test cases. This shows how an existing resonator can be adapted for new purposes, thus making the design of acoustic metamaterials efficient. The optimized metamaterials outperform the original designs as well as traditional approaches to sound insulation.

The use of robotics in executing agricultural tasks has significantly improved productivity over the years as a result of automation in performing such activities as spray, harvesting, planting etc. In order to optimize both crop yield and quality while minimizing costs, there will be need for the application of navigation strategies. These will provide optimal as well as autonomous navigation capability which is built entirely upon field coverage plan thereby making robot navigation approach a paramount scheme. In this paper, the autonomy of an agricultural mobile robot is enhanced in a structured environment (greenhouse farm) to locate an optimum route such that the robot performs a selective and variable spray of pesticides to the plants. To realize this, a robust vehicle routing problem (VRP) scheme is designed to navigate the robot autonomously while making intelligent decisions to fulfil the pesticide demands at each node (infected plants). The improved non-dominated sorting genetic algorithm (INSGA-III) is adopted to solve this fully integer problem based on three (3) test cases carried out with 8, 32 and 56 infected plants respectively for validation. The results obtained show a trade-off solution as the Optimal INSGA-III is significantly lower than NSGA-III in terms of solution quality. On the other hand, a significant reduction in run times of between 66% and 76% and 76–93% was obtained for all test case scenarios for population sizes of 100 and 1500 respectively.

Phase-field methods have the potential to simulate large scale evolution of networks of fractures in porous media without the need to explicitly track interfaces. Practical field simulations require however that robust and efficient decoupling techniques can be applied for solving these complex systems. In this work, we focus on the mechanics-step that involves the coupling of elasticity and the phase-field variable. We develop a multirate scheme in which a coarser time grid is employed for the mechanics equation (i.e., the displacements) and a finer time grid is taken for the phase-field problem. The performance of this algorithm is demonstrated for two test cases.

In dynamic models, threats (vulnerabilities) can be viewed as a flow of temporary events. If the intervals of real-ized cyber threats are recorded, then a continuous log-list of events related to software security can be formed. In some cases and models, only the number of realized cyber threats for an arbitrary time interval can be recorded. In this case, the software response to threats can be represented only at discrete points. In static models, the implementation of cyber threats is not related to time, but the dependence of the number of errors or the number of implemented test cases (models by error area) on the characteristics of the input data (models by data area) is taken into account. The article analyzes the methods of mathematical formalization of the software penetration testing process. This software testing method is one of many approaches to testing the security of computer systems. The article substantiates the importance of the processes of preliminary prototyping and mathematical formalization. The classification is carried out and the advantages and disadvantages of the main approaches of mathematical modeling are highlighted. The list and main characteristics of dynamic and static models are presented. One of the negative factors of formalization is indicated - the neglect of the factors of a priori uncertainty in the safety parameters in static models. У динамічних моделях загрози (уразливості) Software можна розглядати як потік тимчасових подій. Якщо фіксуються інтервали реалізованих кіберзагроз, то може сформуватися безперервний log-лист подій, відносящіхся до безпеки Software. У ряді випадків і моделей може фіксуватися тільки число реалізованих кіберугроз за довільний інтервал часу. У цьому випадку реакція Software на загрози може бути представлена тільки в дискретних точках. У статичних моделях реалізацію кіберзагроз не пов'язують з часом, при цьому враховують завиності кількості помилок або число реалізованих тест-кейсів (моделі по області помилок) від характеристики вхідних даних (моделі по області даних).У статті проаналізовано методи математичної формалізації процесу тестування на проникнення програмного забезпечення. Цей метод тестування програмного забезпечення є одним із багатьох підходів до перевірки безпеки комп’ютерних систем. У статті обґрунтовано важливість процесів попереднього прототипування та математичної формалізації. Проведено класифікацію та висвітлено переваги та недоліки основних підходів математичного моделювання. Представлено перелік та основні характеристики динамічних та статичних моделей. Вказується один із негативних факторів формалізації - нехтування факторами апріорної невизначеності параметрів безпеки в статичних моделях.

Damping Loss Factor (DLF) is an important input parameter in Statistical Energy Analysis (SEA) simulations, therefore its accurate determination via measurement is crucial from simulation accuracy point of view. This paper presents a methodology for the accurate measurement of DLF via the Half Power Bandwidth Method (HPBM) for three test cases: a) a rectangular steel plate, b) the same plate with viscoelastic bitumen sheets imposed on the middle of plate and c) the same plate with the viscoelastic bitumen sheets placed randomly. The aim of the different bitumen layer applications was to develop a methodology for representing them in Statistical Energy Analysis, since this simulation technique takes into account only the coverage of the damping layers but not their distribution over a panel. Measurement of the DLF in random locations on the plate gave the appropriate parameters for the simulation. Measurement results were then applied in SEA simulations, which showed good agreement with the experimental results in all test cases, with the maximum difference of only 2 dB.

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, retesting of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of coverage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Commission respectively for test suite selection and minimization which gave comparatively superior result in terms of reduction (%) in number of test cases required for testing. Conclusion: The combination of parameters used in slicing based approach, reduces the number of test cases making software testing an economical, feasible and time saving option without any fault in the source code. This proposed technique can be used by software practitioners/experts to reduce time, efforts and resources for selection and minimization of test cases.

Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a knowledge map, which is the set of concepts and skills that are necessary to complete an assignment, followed by the design of the assignment and that of a comprehensive test suite for identifying logical errors in the submitted code. Test cases are used to test the student submissions and learn classes of common errors. For each assignment, we train a classifier that automatically categorizes errors in a submission based on the outcome of the test suite. The instructor maps the errors to corresponding concepts and skills and writes hints to help students find their misconceptions and mistakes. We apply this methodology to two assignments in our Introduction to Computer Science course and find that the automatic error categorization has a 90% average accuracy. We report and compare data from two semesters, one semester when hints are given for the two assignments and one when hints are not given. Results show that the percentage of students who successfully complete the assignments after an initial erroneous submission is three times greater when hints are given compared to when hints are not given. However, on average, even when hints are provided, almost half of the students fail to correct their code so that it passes all the test cases. The initial implementation of the framework focuses on the functional correctness of the programs as reflected by the outcome of the test cases. In our future work, we will explore other kinds of feedback and approaches to automatically generate feedback to better serve the educational needs of the students.

The economic load dispatch (ELD) is a crucial problem in electrical power system domain. It is tackled by managing the outputs of predefined generation units in accordance with equality constraints related to the balance of power of units and inequality constraints related to the output of units. Due to its complexity, ELD is modeled as a non-linear, non-convex, and non smooth optimization problem. Therefore, several optimization algorithms have been tailored to tackle ELD. The main contribution of this paper is to utilize the island based harmony search algorithm (iHS) for ELD problem. iHS divides the ELD population to a set of sub-populations called islands. The original Harmony Search (HS) is used for each island as an isolated run. At specific migration frequency, some solutions counted by migration rate are asynchronously migrated from one island to another based on migration policy controlled by migration topology. To ensure the feasibility of obtained ELD solutions, the repair process is also proposed as another contribution. In order to evaluate the proposed method, five real-world ELD test cases including generators with 3 units, two versions of generators with 13 units, 40 units and finally 80 units. Initially, the effect of parameters (Number of island, migration frequency, and migration rate) on the convergence behaviour of iHS is studied for all test cases. For comparative evaluations, the results of each ELD test case is compared with those produced by other well-established methods. The proposed iHS is able to produce the best overall results for three out of five real-world ELD test cases. For further validations, other five complex ELD versions introduced for IEEE-CEC2011 including more constraints (Ramp Rate Limits and prohibited operating zones) are also used which are generators with 6 units, 13 units, 15 units, 40 units, and 140 units. The comparative results of ELD versions for IEEE-CEC2011 test cases show that the proposed iHS-based ELD method is competitively comparable with other eight hybrid-based comparative methods. In a nutshell, The successful performance of the proposed method show that it is viable and efficient which can be very useful for many researchers in the power system domain.

RESTful APIs are very popular in industry, especially when developing enterprise systems using a microservice architecture. Testing such APIs is challenging, as tests will be composed of not only HTTP calls, but also settings of the environment, like databases. Different blackbox testing techniques have been shown to easily find real faults in many RESTful APIs, with very little human effort from software engineers. However, whitebox techniques could lead to much better results, although having an up-front cost for the engineers. In this paper, we report on the use of the open-source tool EvoMaster, on eight RESTful APIs. We show how EvoMaster can be used to automatically generate test cases that can find several bugs, even when using a naive blackbox approach. When enhancing the search with whitebox information, significantly better results are achieved. However, there are several challenges that need to be taken into account when an engineer wants to use a tool such as EvoMaster to test their projects. This work is funded by the Research Council of Norway (project on Evolutionary Enterprise Testing, grant agreement No 274385).

To assess the potential of building-integrated photovoltaics (BIPV) for large numbers of buildings, i.e., for a whole city or beyond, automated and fast but sufficiently accurate methods are required. As a part of a whole tool chain, this article presents an approach for the automated electric layout of a BIPV system based on the polyomino algorithm and a simplified approach for the power output calculation. In two used cases based on existing buildings, the calculated layout comes close to the theoretical maximum where each module is operated in its maximum power point. Unlike other existing approaches, the proposed algorithm prioritizes the connection between neighboring modules and leads to practically feasible wiring structures.

While the particle-in-cell (PIC) method is quite mature, verification and validation of both newly developed methods and individual codes has largely focused on an idiosyncratic choice of a few test cases. Many of these test cases involve either one- or two-dimensional simulations. This is either due to availability of (quasi) analytic solutions or historical reasons. Additionally, tests often focus on investigation of particular physics problems, such as particle emission or collisions, and do not necessarily study the combined impact of the suite of algorithms necessary for a full featured PIC code. As three dimensional (3D) codes become the norm, there is a lack of benchmarks test that can establish the validity of these codes; existing papers either do not delve into the details of the numerical experiment or provide other measurable numeric metrics (such as noise) that are outcomes of the simulation. This paper seeks to provide several test cases that can be used for validation and bench-marking of particle in cell codes in 3D. We focus on examples that are collisionless, and can be run with a reasonable amount of computational power. Four test cases are presented in significant detail; these include, basic particle motion, beam expansion, adiabatic expansion of plasma, and two stream instability. All presented cases are compared either against existing analytical data or other codes. We anticipate that these cases should help fill the void of bench-marking and validation problems and help the development of new particle in cell codes.

Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai.

Cloud computing has established itself as the support for the vast majority of emerging technologies, mainly due to the characteristic of elasticity it offers. Auto-scalers are the systems that enable this elasticity by acquiring and releasing resources on demand to ensure an agreed service level. In this article we present FLAS (Forecasted Load Auto-Scaling), an auto-scaler for distributed services that combines the advantages of proactive and reactive approaches according to the situation to decide the optimal scaling actions in every moment. The main novelties introduced by FLAS are (i) a predictive model of the high-level metrics trend which allows to anticipate changes in the relevant SLA parameters (e.g. performance metrics such as response time or throughput) and (ii) a reactive contingency system based on the estimation of high-level metrics from resource use metrics, reducing the necessary instrumentation (less invasive) and allowing it to be adapted agnostically to different applications. We provide a FLAS implementation for the use case of a content-based publish–subscribe middleware (E-SilboPS) that is the cornerstone of an event-driven architecture. To the best of our knowledge, this is the first auto-scaling system for content-based publish–subscribe distributed systems (although it is generic enough to fit any distributed service). Through an evaluation based on several test cases recreating not only the expected contexts of use, but also the worst possible scenarios (following the Boundary-Value Analysis or BVA test methodology), we have validated our approach and demonstrated the effectiveness of our solution by ensuring compliance with performance requirements over 99% of the time.

We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable and cost-effective power system operation.DeepOPF is inspired by the observation that solving SC-DCOPF problems for a given power network is equivalent to depicting a high-dimensional mapping from the load inputs to the generation and phase angle outputs. We first train a DNN to learn the mapping and predict the generations from the load inputs. We then directly reconstruct the phase angles from the generations and loads by using the power flow equations. Such a predict-and-reconstruct approach reduces the dimension of the mapping to learn, subsequently cutting down the size of the DNN and the amount of training data needed. We further derive a condition for tuning the size of the DNN according to the desired approximation accuracy of the load-generation mapping. We develop a post-processing procedure based on $\ell_1$-projection to ensure the feasibility of the obtained solution, which can be of independent interest. Simulation results for IEEE test cases show that DeepOPF generates feasible solutions with less than 0.2% optimality loss, while speeding up the computation time by up to two orders of magnitude as compared to a state-of-the-art solver., Comment: 14 pages, 6 figures, accepted for publication in IEEE Transactions on Power Systems. (Conference version in IEEE SmartGridComm 2019.) arXiv admin note: text overlap with arXiv:1905.04479

Purpose With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast-enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners. Methods The proposed DL model consists of a set of methods that improve the model robustness and generalizability. The steps include multi-planar reconstruction, 2.5D model, enhancement-weighted L1, perceptual, and adversarial losses. The proposed model predicts contrast-enhanced images from corresponding pre-contrast and low-dose images. With IRB approval and informed consent, 640 heterogeneous patient scans (56 train, 13 validation, and 571 test) from 3 institutions consisting of 3D T1-weighted brain images were used. Quantitative metrics were computed and 50 randomly sampled test cases were evaluated by 2 board-certified radiologists. Quantitative tumor segmentation was performed on cases with abnormal enhancements. Ablation study was performed for systematic evaluation of proposed technical solutions. Results The average peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between full-dose and model prediction were 35.07 ± 3.84 dB and 0.92 ± 0.02 , respectively. Radiologists found the same enhancing pattern in 45/50 (90%) cases; discrepancies were minor differences in contrast intensity and artifacts, with no effect on diagnosis. The average segmentation Dice score between full-dose and synthesized images was 0.88 ± 0.06 (median = 0.91). Conclusions We have proposed a DL model with technical solutions for low-dose contrast-enhanced brain MRI with potential generalizability under diverse clinical settings.

Modern-day demands for services often require an availability on a 24/7 basis as well as online accessibility around the globe. For this sake, personalized virtual assistants, called chatbots, are implemented. Such systems offer services, goods or information in natural language. These natural language processing (NLP) programs respond to the user in real time and offer an intuitive and simple interface to interact with. Advantages like these make them increasingly popular. Therefore, ensuring correct functionality of chatbots is of increasing importance. However, since different implementations and user behaviour result in unpredictable results, the chatbot’s input and output data are difficult to predict and classify as well. Under such circumstances, test cases can be inferred from the domain of possible inputs of a system under test (SUT). Ontologies are concepts used in AI to provide formal representations of knowledge for a specific domain. Such ontological models contain structured information that is used for test generation. On the other hand, testing of chatbots represents a challenge because of the absence of a test oracle. In this paper, both challenges are addressed by conceptualizing ontologies for input generation and output processing in form of a metamorphic testing approach. In this scenario, both concepts are applied for automated testing of chatbots. The approach is demonstrated on a real system from the tourism domain, thereby discussing the obtained results.

Ontology matching has become one of the main research topics to address problems related to semantic interoperability on the web. The main goal is to find ways to make different ontologies interoperable. Due to the high heterogeneity in the knowledge representation of each ontology, several matchers are proposed in the literature, each seeking to capture a specific aspect of the ontology. Generally, different matchers are complementary and none stand out in all test cases. In this paper, we present a meta-matching approach employing the prey–predator meta-heuristic in order to define a set of weights to find the best possible result from a set of matchers. The approach was evaluated on the Ontology Alignment Evaluation Initiative benchmark and results showed that the prey–predator algorithm is competitive with other popular algorithms as it achieves an average f-measure of 0.91.

We present a mixed finite element solver for the linearized R13 equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS's Unified Form Language (UFL) to define required differential operators for tensors above second degree. The presented solver serves as an example for implementing tensorial variational formulations in FEniCS, for which the documentation and literature seem to be very sparse. Using the software abstraction levels provided by the UFL allows an almost one-to-one correspondence between the underlying mathematics and the resulting source code. Test cases support the correctness of the proposed method using validation with exact solutions. To justify the usage of extended gas flow models, we discuss typical application cases involving rarefaction effects. We provide the documented and validated solver publicly., 29 pages, 13 figures, 8 listings, 3 table. Submitted to ACM Transactions on Mathematical Software (TOMS)

This work was developed in an industrial setting towards UI regression testing, where we do not have access to source code and the majority of test cases are manually executed (and only part of the regression-based test cases can be executed due to limited resources). Test case prioritization (TCP) is indicated for such a scenario. But characteristic of many TCP techniques is that they rely on source code coverage information, whereas we just have access to test cases, change requests, and their features. Thus, our goal is to investigate which criteria is the most relevant for prioritization. Thus, according to the literature we create an optimization model based on historical data. This model is embedded in a constraint solver designed for optimization. Our optimization function is based on the APFD (Average of the Percentage of Faults Detected) metric, but other metrics can be used as well. We have found that our partner already uses an appropriate criterion to identify failures which is statistically equivalent to other criteria used in experiments using our optimization model.

Homing and synchronizing sequences (HSs and SSs) are used in Finite State Machine (FSM)-based testing for state identification when deriving a test suite with guaranteed fault coverage or when performing a non-intrusive (passive) testing or monitoring. However, such preset sequences do not always exist for nondeterministic FSMs or can be rather long if existing. Adaptive HSs and SSs are known to exist more often and can be much shorter which makes them attractive for deriving test suites and adaptive checking sequences. As nowadays, a number of specifications are represented by nondeterministic FSMs, there is a need for the deeper study of such sequences, their derivation strategies, and related complexity estimation / reduction. In this paper, given an FSM and a subset $$S'$$ of its states, we are concerned with the existence check and derivation of $$S'$$ -homing and $$S'$$ -synchronizing adaptive sequences (test cases) which allow to reduce the uncertainty about the current state of the FSM up to a state of the subset $$S'$$ . There are a number of research papers on evaluating the complexity of the existence check of adaptive HS / SS as well as on estimating the length of an HS / SS, and in the ICTSS’19 conference, we presented the complexity of their derivation for non-initialized complete nondeterministic FSMs. In this paper, we utilize the criteria of the ICTSS’19 paper to estimate the complexity of the existence check and derivation of $$S'$$ -homing and $$S'$$ -synchronizing test cases for non-initialized observable nondeterministic FSMs. Some sufficient conditions for weakly initialized FSMs to have adaptive $$S'$$ -homing and $$S'$$ -synchronizing test cases are also established.

The guarantee of software quality is very important. Thus, before a software is released to the end users, the flaws in the software should be detected by using high quality test cases. Currently, gauging the quality of test cases is carried out without any particular model and the criteria for good test cases is still unclear. Therefore, this study studies the literature using Systematic Literature Review (SLR) technique to identify the criteria of good test cases. The SLR considered papers between 2010 and 2018 in IEEE Xplore, ACM Digital Library, and Science Direct databases. Through the searching, it was found 310 papers are related. After filtering using exclusion and insertion criteria, 14 papers were considered for analysis. As a result, the test managed to identify 30 quality factors from the selected articles. These quality factors were additionally inspected, arranged and finished to be incorporated as the quality factors of test cases evaluation metrics.

Most of the test cases minimization reduced test cases during regression testing to generate new test suite to cover the same software requirements.The objective of this paper is to present new framework that integrate the idea of minimization and prioritization.Hence, reduction and prioritization able to reduce test cases based on the statements covered by the previous test cases to avoid redundancy.Beginning from the reduction of the test cases, followed by weighted prioritizationaccording to their usefulness.The framework was tested using sample test suite and the results obtained shown increases on the average percentage of faults detection (APFD). Future plan is to test on the larger size of test suite.

In Agile model where the software prototypes are developed frequently and also rapidly, testing becomes more critical. Generating an effective Test case for complex system is a challenging task involved in software testing. The major research challenge in this area includes the test case generation with limited resources, identifying the essential functional requirement that plays a crucial role and automation of the test case generation process. To solve this issue, a hybridized bio inspired approach is proposed to generate test cases from the user stories which accepts the business requirements as input, processed using NLP and develop functional test cases from it. The proposed algorithm is compared with other existing algorithms and the experimental results proved that the proposed algorithm is more efficient in many cases.

Every product has defects and identifying defects in the process of development and rectifying them before the launch of the product is very important. Embedded software testing process find the bugs in the software and report to the developer to fix issues. Sometimes to meet the product release deadlines, test engineers will not get much time to cover all test cases. That is why most software testing depends on test automation. In this paper, we focused on the area of automotive and home appliances embedded software test automation. Test automation is the only solution to improve the test phase and meet the timeline of the product launch. There are many test Automation tools like LabVIEW, test stand, and automation desk to automate testing embedded software. However, there is still manual efforts are required to use these tools. This paper deals automate those manual efforts. This Works shows how to generate test scripts from test cases to reduce the manual efforts, time, and cost.

In the process of product development, stakeholders and top management summarize the concept and document the requirements in natural language. These ideas and descriptions documented as software requirements by the technical department. Developers develop software following this software requirement document. For testing this developed software, they derive test cases from natural language requirements and then do the testing process to find the bugs. This process involves understanding requirements and derives test cases that are used to understand by developers and testers. Due to increasing the advanced features, deriving the test cases is monotonous and takes more time. This research article shows a method to automate this process which is deriving test cases from requirements using NLP algorithms. This approach useful to reduce the time and cost of software development.

In this paper, we address the formulation of a novel scheme for reliability-based design optimization, in which the design optimization problem is characterized by constraints that must be met with a certain probability. Assessment of the aforementioned is typically referred to as reliability analysis. Conventional methods rely on sampling approaches or by reformulating the problem as a two-level optimization that requires gradient or Hessian information of the constraints to obtain a trustworthy solution. However, the computational cost of such methods makes them often impractical. To overcome the aforementioned, a surrogate-assisted asymptotic reliability analysis (SARA) is presented that makes use of surrogate-derived gradient and Hessian information. The sub-optimization problem is reformulated as a set of constraints using the Karush-Kuhn-Tucker conditions and fitted in an efficient global optimization-like setting through the formulation of the reliability-based expected improvement (RBEI), obtaining the novel efficient single-loop approach (ESLA). The method is tested on a series of test cases which prove the effectiveness of the novel scheme.

Testing complex software systems is an extraordinarily difficult task. Test engineers are faced with the challenging prospect of ensuring that a software system satisfies its requirements while wor...

Time-sensitive networking (TSN) is an upcoming set of Ethernet standards designed for real-time and safety-critical Internet-of-Things (IoT) applications in automotive, aerospace, and industrial automation domains. With the combination, complexity, and flexibility of flow control mechanisms in TSN connected systems, the performance analysis for mixed-critical messages is becoming a difficult challenge. The flexible window-based gate control list (GCL) scheduling model has been proposed as a relaxation to assumptions on frames-to-window allocation, mutually exclusive gates opening, and scheduled end systems and switches, which offers more flexibility in the configuration of GCLs. In this article, we are interested in providing a reliable verification method based on the network calculus theory to drive GCL configurations for TSN networks. To the best of our knowledge, this is the first performance analysis method suitable for the general flexible window-based GCLs in entire TSN networks, by reflecting the relative positional relationships of windows for same priority queues on consecutive nodes and constructing the window limitations into the shaper curve, in order to reduce the pessimism of the latency bounds. We validate the proposed method through Industrial IoT synthetics test cases and two large realistic cases, showing the significant reduction in pessimism on delay bounds, and the correctness and scalability by comparing with results from the previous work and simulation results.