Modelling of Spacecraft Orbit via Neural Networks

This paper presents a general framework for determining average program execution times and their variance, based on the program's interval structure and control dependence graph. Average execution times and variance values are computed using frequency information from an optimized counter-based execution profile of the program.

Evaluation of Level of Confidence and Optimization of Roll-back Recovery with Checkpointing for Real-Time Systems

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.

Relevance. When solving problems of stabilization of dynamic objects, classical model predictive control is widely used. It provides high quality control by solving the optimization problem at each step, but it has significant computing costs, which limits its application in real-time systems with high requirements for update frequency. Therefore, the question of investigating the applicability of a neural network regulator trained on a model predictive regulator (MPC) when solving the problem of stabilizing the position of a dynamic object with a limited computational and time resource is relevant. Goal.  The purpose of the presented work was to develop and study a neural network regulator trained on the basis of an MPC regulator to stabilize the position of a dynamic object on a mobile platform. Methods. When performing the work, methods of system analysis, simulation modeling, as well as experimental tests on the bench were used. Results and conclusions. As part of the study, a neural network regulator was developed and trained that approximates the behavior of MPC based on data obtained when controlling a real balancing platform. The training was conducted on the input and output data of the MPC without using the internal model of the system, which made it possible to reproduce the dynamics of the regulator at significantly lower computational costs. Experimental results showed that the neural network model provides a stabilization quality comparable to the original MPC, while the calculation time was reduced from 47 ms to 1.6 ms, which amounted to an acceleration value of 29 times. The proposed approach demonstrates the potential of neural network control methods in the problems of replacing complex optimization regulators for systems with limited computing resources.

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

The validation of modified software depends on the success of Regression testing. For this, test cases are selected in such a way that can detect a maximum number of faults at the earliest stage of software development. The selection process in which the most beneficial test case are executed first is known as test case prioritization which improves the performance of execution of test cases in a specific or appropriate order. Many optimizing techniques like greedy algorithm, genetic algorithm, and metaheuristic search techniques have been used by many researchers for test case prioritization and optimization. This research paper presents a test case prioritization and optimization method using genetic algorithm by taking different factors of test cases like statement coverage data, requirements factors, risk exposure, and execution time.

Software testing is an important and complex part of the software development life cycle. Along with version changes and defect repairs of the software system under test, regression testing is required to ensure that the modified parts have no impact on the unmodified parts. In a resource-constrained environment, it is necessary to select a more valuable test case from the test case library to execute first. However, the existing prioritization methods of test cases are still insufficient in terms of Average Percentage of Faults Detected (APFD) and time execution performance, and there is a problem oflarge search space. Aiming at the test case priority ranking problem, this paper proposes a meta-heuristic test case prioritization method based on a hybrid model to reduce test cost. This method first establishes a hybrid model by using the correlation between test cases and the importance of test data, and then uses an improved firefly algorithm based on the hybrid model to find an optimal test sequence. This article has carried out experiments on three benchmark test programs. The test suite is from Software-artifact Infrastructure Repository (1 SIR). The experimental results show that the method proposed in this paper has better performance in terms of APFD and time execution compared with existing methods, such as Greedy, Particle Swarm optimization (PSO) and Firefly Algorithm (FA).1https://sir.csc.ncsu.edu/content/sir.php

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. As the software evolves the size of test suites also grows. When the no of test cases generated are more, obviously size of the test suite will also be more. So the testing time is to be minimized by reducing the execution time of the algorithm used for test data generation and also by introducing minimization procedure for test suite reduction. Due to limited resources and timing constraints for testing, test suite minimization techniques are needed to eliminate redundant test cases as possible. By considering multiple objectives rather than the coverage alone, the test cases are being generated which satisfies the testing requirements. Most of the existing techniques are code-based. In this article we present an approach by modifying an existing heuristic for test suite minimization. Genetic algorithm has been used for random test data generation and the output of GA is given to the minimization procedure for reducing the total no of generated test cases, collectively named as Hybrid Algorithm (HA). The results are satisfactory and show significant improvements in reducing test suite size with minimum execution time. Experiments have been done for simple to medium complexity java programs taken from SIR and execution time is reduced to 5,685ms for a test set. The results are compared with existing method Mutant Gene Algorithm and size of test suite is minimized upto 13.6% using Hybrid Algorithm.

Resource provisioning in scalable cloud using bio-inspired artificial neural network model

Aim. Development of method for control by orbital spacecraft magnetic cleanliness based on multiple magnetic dipole models using compensation of the initial magnetic field with consideration of magnetic characteristics uncertainty. Methodology. Orbital spacecraft multiple magnetic dipole models calculated as solution of nonlinear minimax optimization problem based on near field measurements for prediction orbital spacecraft far magnetic field magnitude. Nonlinear objective function calculated as the weighted sum of squared residuals between the measured and predicted magnetic field. Weight matrix calculated as inverse covariance matrix of random errors vector. Values of magnetic moments and coordinates of placement of compensating magnetic dipoles for compensation of the orbital spacecraft initial magnetic field also calculated as solution of nonlinear minimax optimization problem. Both solutions of this nonlinear minimax optimization problems calculated based on particle swarm nonlinear optimization algorithms. Results. Results of prediction spacecraft far magnetic field magnitude based on orbital spacecraft multiple magnetic dipole models using near field measurements and compensation of the initial magnetic field with consideration of orbital spacecraft magnetic characteristics uncertainty for ensuring the orbital spacecraft magnetic cleanliness. Originality. The method for control by orbital spacecraft magnetic cleanliness based on multiple magnetic dipole models using compensation of the initial magnetic field with consideration of magnetic characteristics uncertainty is developed. Practical value. An important practical problem of ensuring orbital spacecraft magnetic cleanliness based on orbital spacecraft multiple magnetic dipole models using near field measurements and compensation of the initial magnetic field with consideration of orbital spacecraft magnetic characteristics uncertainty solved.

Forest fires pose a serious threat to the environment with the potential of causing ecological harm, financial losses, and human casualties. While research suggests that climate change will increase the frequency and severity of these fires, recent developments in deep learning and convolutional neural networks (CNN) have greatly enhanced fire detection techniques and capability. These models can be leveraged by unmanned aerial vehicles (UAVs) to automatically monitor burning areas. However, drones can carry only limited computational and power resources; therefore, on-board computing capabilities are constrained by hardware limitations. This work focuses on the design of segmentation models to identify and localize active burning areas from aerial RGB images processed on limited computing resources. To achieve this goal, the research compares the performance of different variants of the DeepLabv3 neural network model for fire segmentation when trained and tested with the FLAME dataset using a k-fold cross validation approach. Experimental results are compared with U-Net, a benchmark model used with the FLAME dataset, by implementing this model in the same codebase as the DeepLabv3 model. This work demonstrates that a refined version of DeepLabv3, with a MobileNetv2 backbone using pretrained layers and a simplified atrous spatial pyramid pooling (ASPP) module, yields a similar performance to U-Net, with a precision of 87.8% and a recall of 83.2%, while only requiring 20% of the number of parameters involved with the U-Net topology. This significantly reduces memory and power consumption, enabling longer UAV flight duration and reducing the processing overhead associated with sensor input, making it more suitable for deployment on unmanned aerial vehicles. The model’s compact architecture, implemented using TensorFlow and Keras for model design and training, along with OpenCV for image preprocessing, makes it portable and easy to integrate with edge devices such as NVIDIA Jetson boards.

With the widespread application of smart contracts, there is a growing concern over the quality assurance of smart contracts. The data flow testing is an important technology to ensure the correctness of smart contracts. We propose an approach named IABC‐TCG (Improved Artificial Bee Colony‐Test Case Generation) to generate test cases for the data flow testing of smart contracts. With a dominance relations‐based fitness function, an improved artificial bee colony algorithm is used to generate test cases, in which the bee colony search coefficient is adaptively adjusted to improve the effectiveness and efficiency of the search. In addition, an improved test case selection and updation strategy is used to avoid unnecessary test cases. The experimental results show that IABC‐TCG achieves 100% coverage for all the test requirements on a dataset of 30 smart contracts and outperforms the baseline approaches in terms of the number of test cases and the execution time. Performing tests with the generated test cases, IABC‐TCG can find more errors with less test cost.

In the rapidly evolving telecommunications industry, organizations face the challenge of ensuring that a diverse range of products perform effectively across various configurations. The emergence of new technologies, particularly in radio access and frequency bands, complicates testing and validation processes. Companies must strike a balance between comprehensive testing to guarantee quality and the constraints of time and resources, necessitating innovative approaches to enhance testing strategies. This thesis proposes a holistic framework that leverages insights from historical test data and domain expert knowledge to derive actionable insights, streamline processes, improve compatibility, and reduce costs while maintaining product quality in a competitive market. We employ various reverse engineering techniques, including deep learning-based natural language processing, to navigate the complexities of testing artifacts and extract a machine-readable mapping between information about test cases and the functions of the system under examination. This machine-readable feature mapping is then fed into an interpretable machine-learning engine to extract actionable insights. The research is structured into two phases: Phase 1: Data collection procedures focus on extracting information from diverse data sources using API access, analyzing test complexity, and mapping features to test cases through deep learning techniques. It involves identifying data sources and creating a mapping between features and test cases. Phase 2: Data analysis procedures analyze the feature mapping to extract actionable insights using interpretable machine learning techniques. This phase classifies mappings based on a classification target, enabling predictions, such as which product configuration combinations new test cases should prioritize. The combined insights from both phases contribute to developing an AI/ML-driven streamlining procedure that enhances testing efficiency through: -Optimized Configuration Selection: Classifying test cases by execution time helps identify configuration combinations that yield the shortest execution time while considering contextual relevance and interdependencies. - Effective Test Effort Allocation: Analyzing classified test cases with a specific classification target identifies test frequency imbalances and informs better resource allocation. -Test Gap Analysis: Data insights reveal gaps in testing coverage, highlighting under-tested configuration combinations. Through this structured approach, this thesis aims to bridge knowledge gaps, mitigate uninformed decision-making, and address challenges stemming from data silos and heterogeneity in test information.

Maximum coverage with minimum testing time is the main objective of a test case generation activity which leads to a multi-objective problem. Search-Based Testing (SBT) technique is a demanding research area for test case generation. Researchers have applied various metaheuristic (searching) algorithms to generate efficient and effective test cases in many research works. Out of these existing search-based algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are the most widely used algorithms in automatic test case generation. In this paper, a test case generation approach is proposed using Cuckoo Search (CS) algorithm. CS has a controlling feature, Levy flights, which makes it more efficient in searching the best candidate solution. It helps to generate efficient test cases in terms of code coverage and execution time. In our proposed method, test cases are generated based on path coverage criteria. Fitness of a test case is evaluated using branch distance and approximation level combined functions. The result is compared with PSO and with its variant Adaptive PSO (APSO). The experimental result shows that both the algorithms give nearly equal to the same result. Though the results are nearly equal, the implementation of CS is simple as it requires only one parameter to be tuned.

The purpose of Graphical User Interfaces (GUI) testing is to diagnose and expose faults in planning time. It is difficult because the input space of GUI is extremely large due to different permutations of inputs and events. To test GUI needs to run a lot of test cases. Neural networks (NN) were explored to reduce test cases to expose new faults. The main idea is as follows. Firstly, NN was trained by subset of test cases that had executed and their test results. Trained NN could recognize fault patterns that had been exposed. Secondly, from the test suite that hadn't been executed, trained NN was used to select test cases that don't belong to the fault patterns. The test cases selected were more likely to expose new faults in GUI. By the method new faults could be exposed by executing fewer test cases. The experimental results show that the strategy is effective.