Recent Advances in Computer Science and Communications - Volume 15, Issue 5, 2022

Background: Software development is the transition from centralized to decentralized version control systems. This transition is driven by the limited features of centralized version control systems in terms of branching, merging, time, space, offline commits & builds and repository aspects. Transition from Subversion; a centralized version control system, to Git; a decentralized version control system has been focused in a limited way. Objective: In this work transition process from Subversion Version Control System (VCS) to Git VCS has been investigated in terms of time, space, branching, merging and repository aspects from the software developer’s point of view; working individually or in a large team over a large and complex software having a legacy of many decades. Experimentation was conducted in SRLC Software Research Lab, Chicago, USA. Methods: Various scripts have been developed and executed for version control using Git and performed over a few legacy software. Results: Results show that branching in Git and Subversion has a difference of about 39 times, i.e. branching operation of Git is about 39 times faster than Subversion. Merging in the case of Git is trivial and automatic, while Subversion needs a manual process of merging, which is error prone. Using an example of Mozilla with fsfs backend, it is observed that disk space can be saved up to 30 times in Git compared to Subversion. By taking a typical example of a large sized project it is observed that Git requires almost half of the revisions compared to Subversion, further with fsfs backend a project having ten years of history with 240,000 commits needs 240 directories in case of Subversion while Git requires only 2 directories. Using offline commits and offline builds of Git, it is observed that in Git whitespace changes, in contrast to significant business logic changes, can be staged in one commit only. These are not possible in Subversion, which requires a complicated system of diffing to temporary files. It is also observed that Git provides offline commit facility, i.e. in case if for some reason, remote repository is unavailable due to disaster or network failure, then still developers can commit their offline code and execute the offline build. Conclusion: However, no previous study was found that focused on how the choice actually affects software developers and it forms the motivation for the present work. In this work, a list of how the choice between Git and Subversion affects software developers is worked out. Although software developers in many aspects are unaffected by the choice, few interesting findings were achieved. One of the most interesting findings of the proposed work is that software developers seem to publish their code to the main repository more often in Git than in Subversion. It is also found that the majority of the software developers perform at least two commits per push, which means that Git repositories will contain a lot more saved points in history than Subversion repositories.

Background: Part of Speech (POS) Tagging is a process of defining a suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval. Objectives: The performance of NLP applications depends on the accuracy of POS taggers since assigning the right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed for Arabic language, but more investigations are needed to improve the efficiency of Arabic POS taggers. Methods: In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm, which consists of a group of particles. Each particle represents a sequence of tags. The PSO algorithm is applied to find the best sequence of tags, which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find fitness of the particles in the swarm. Results: The performance of the proposed approach is evaluated on the KALIMAT dataset, which consists of 18 million words and a tag set consists of 45 tags, which cover all Arabic POS tags. The proposed tagger achieved an accuracy of 90.5%. Conclusion: Experimental results revealed that the proposed tagger achieved promising results compared to four existing approaches. Other approaches can identify only three tags: noun, verb and particle. Also, the accuracy for some tags outperformed those achieved by other approaches.

Objectives: From the literature review, it is evident that the concept of “regression testing” inherited in agile software testing originates from software maintenance practices. Therefore, the existing algorithms for regression testing revolve around software maintenance principles rather than agile methodology. This paper aims to evaluate the degree of fitness of the existing regression test-suite development algorithms for performing the regression testing in agile. Methods: This paper performs a systematic literature review of research work published from 2006 to 2018, which includes a survey of the existing regression testing algorithms to identify and overcome the challenges associated with them while performing regression testing in agile. This paper considers the four research questions into scope for analyzing the suitability of the existing regression test-suite development algorithm for performing regression testing under agile methodology. Further, this paper proposes an approach to develop a suitable regression test-suite for regression testing under agile methodology. Results: Since four new key challenges were found to be associated with it, the current regression test-suite development algorithm was found unsuitable for performing the regression testing under agile methodology. Conclusion: The current regression test-suite development algorithms are aligned with software maintenance principles rather than agile methodology. In addition, the newly proposed approach for regression test-suite development was found to be easily adaptable by agile teams as it aligns with agile methodology principles. Finally, for developing a suitable regression test-suite to perform regression testing under agile methodology, this paper recommends adopting the agile principle using the newly proposed approach.

Background: There has been continuous advancement in technologies for the past few decades by incorporating new features in accordance with the market demand. The evolution of software projects/applications has an intricated debugging process by generating numerous faults in it. Objectives: In this study, an attempt is made to develop a software reliability growth model (SRGM) taking into account the software project/application’s characteristics, such as complexity of code and testing environment. The simulation is based on previous fault data in order to foresee the future latent faults occurring in the system for a given time frame. This model not only forecasts the number of faults but is an extended version of Kapur and Garg’s error removal phenomenon model incorporating factors that might have an influence on the model. Methods: The performance of the model is validated using three data sets and finally compared with extant models, namely GO model and Yamada model, to assess the proposed model’s efficiency. Results: The parameter estimations were significant, and the proposed model performed better in comparison to the other two models. Conclusion: The proposed model is a contribution to the studies on the reliability of the project and can be extended in the future by generalizing the results over various datasets and models.

Background: The entire world is shifting towards electronic communication through Email for fast and secure communication. Millions of people, including organization, government, and others, are using Email services. This growing number of Email users are facing problems; therefore, detecting phishing Email is a challenging task, especially for non-IT users. Automatic detection of phishing Email is essential to deploy along with Email software. Various authors have worked in the field of phishing Email classification with different feature selection and optimization techniques for better performance. Objectives: This paper attempts to build a model for the detection of phishing Email using data mining techniques. This paper's significant contribution is to develop and apply Feature Selection Technique (FST) to reduce features from the phishing Email benchmark data set. Methods: The proposed Pruning Based Feature Selection Technique (PBFST) is used to determine the rank of feature based on the level of the tree where feature exists. The proposed algorithm is integrated with already developed Bucket Based Feature Selection Technique (BBFST). BBFST is used as an internal part to rank features in a particular level of the tree. Results: Experimental work was carried out with open source WEKA data mining software using a 10-fold cross-validation technique. The proposed FST was compared with other ranking based FSTs to check the performance of C4.5 classifier with Phishing Email data set. Conclusion: The proposed FST reduces 33 features out of 47 features which exist in phishing Email dataset and C4.5 algorithm produces remarkable accuracy of 99.06% with only 11 features and it has been found to be better than other existing FSTs.

Background: In the cloud environment, the satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in a minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumers and service providers. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objectives: The research aims to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, the authors have proposed a new load-balancing algorithm named EBGAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant- Colony algorithm. A fitness value is assigned to all virtual machines based on the availability of resources and load on a virtual machine. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of the amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment. Conclusion: Through experimental results, it can be concluded that the proposed load balancing approach EB-GAP has outperformed other load balancing approaches based on relevant parameters.

Variation is an inherent phenomenon, whether in natural things or man-made. Thus, it seems essential to estimate this variation. Various authors have worked in the direction of improved estimation of population variance utilizing the known auxiliary parameters for better policymaking. Methods: In this article, a new Searls ratio type class of estimator is suggested for elevated estimation of population variance of the main variable. As the suggested estimator is biased, its bias and mean squared error (MSE) has been derived up to the order-one approximation. The optimum values for the Searls characterizing scalars are obtained. The minimum MSE of the introduced estimator is obtained for the optimum Searls characterizing scalars. A theoretical comparison between the suggested estimator and the competing estimators has been made through their mean squared errors. The efficiency conditions of the suggested estimator over competing estimators are also obtained. These theoretical conditions are verified using some natural data sets. The computation of R codes for the biases and MSEs of the suggested and competing estimators is developed and used for three natural populations in the study by Naz et al. The estimator with the least MSE is recommended for practical utility. The empirical study has been done using R programming. Results: The MSEs of different competing and the suggested estimators are obtained for three natural populations. The estimator under comparison with the least MSE is recommended for practical applications. Discussion: The aim to search for the most efficient estimator for improved estimation is fulfilled through the proper use of the auxiliary parameters obtained from the known auxiliary variable. The suggested estimator may be used for elevated estimation of the population variance. Conclusion: The introduced estimator has the least MSE as compared to competing estimators of population variance for all three natural populations. Thus, it may be recommended for application in various fields.

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting of multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results: Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

Objectives: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on the basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then the wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterward, features are divided into training data and testing data. MKSVM, with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm, is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

Objectives: By constructing a monitoring system, the dynamic monitoring of various statistical indexes is realized, and scientific evaluation is carried out at the same time, so as to promote the rational allocation of medical resources in hospitals and facilitate management. Methods: Starting from the design of the monitoring system database and main functions, the development of the monitoring system is initiated through index collection, interface configuration, report display, data warehouse construction, etc., and then the key and difficult points of the system construction are analyzed. Results: To build a daily statistical index monitoring system under the background of big data, and help hospital managers master the actual operation of hospital medical quality, medical technology and resources. Conclusion: Improving the level of statistical informatization and ensuring the quality of statistical data is of great significance.

Background: Software Reliability Growth Models (SRGMs) are the most widely used mathematical models to monitor, predict and assess the software reliability. They play an important role in industries to estimate the release time of a software product. Since 1970s, researchers have suggested a large number of SRGMs to forecast software reliability based on certain assumptions. They all have explained how the system reliability changes over time by analyzing failure data set throughout the testing process. However, none of the models is universally accepted and can be used for all kinds of software. Objectives: The objective of this paper is to highlight the limitations of SRGMs and to suggest a novel approach towards improvement. Methods: We have presented the mathematical basis, parameters and assumptions of the software reliability model and analyzed five popular models, namely Jelinski-Moranda (J-M) model, Goel Okumoto NHPP model, Musa-Okumoto Log Poisson model, Gompertz Model and Enhanced NHPP model. Conclusion: The paper focuses on challenges like flexibility issues, assumptions, and uncertainty factors of using SRGMs. It emphasizes considering all affecting factors in reliability calculation. A possible approach has been mentioned at the end of the paper.

Introduction: Today, technology and internet both are proliferating due to which information access is becoming easier, and is creating new challenges and opportunities in all fields, especially when working in the field of education. For example, the e-learning education system can be personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning plays a significant role. For many researchers, it has become one of the most important subjects, as through the use of elearning, the whole education system would revolutionize. There are many areas of e-learning in which research work is being carried out, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objectives: To meet the various needs of the learners such as talents, interests and goals an elearning system needs to be designed as a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have been used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of the literature of previous work that has been conducted in e-learning area, especially in the recommendation system. Current research works on e-learning has been discussed in this work in order to discover the research developments in this discipline. Conclusions: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information resources as e-learning plays a vital role in preventing virus spread during COVID-19 pandemic.

Background: Detection technology is a product development technique that serves as a basis for quality assurance. As electric energy meters (EEMs) are measurement instruments whose use is mandatory in several nations, their accuracy, which directly depends on their reliability and proper functioning, is paramount. In this study, to eliminate electromagnetic interference, a device is developed for testing a set of EEMs under a constant magnetic field interference. The detection device can simultaneously test 6 electric meters; moreover, in the future, it will be able to measure the influence of magnetic field strength on the measurement accuracy of EEMs, thereby improving the production efficiency of electric meter manufacturers. Methods: In this study, we first design a 3D model of the detection device for a single meter component; then, we establish a network, which includes a control system and performs the planning of the path of a block that generates a constant magnetic field. Finally, we control the three-axis motion and rotation of the block using a PLC to implement detection for the five sides of the EEM. Results and Discussion: The designed device can accurately determine whether an EEM can adequately function within the error range prescribed by Chinese national standard, under electromagnetic interference; this can enable reliable, automatic testing and fault detection for EEMs. Experiments show that our device can decrease the labor cost for EEM manufacturers and improve the production efficiency.

Objectives: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at the right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for the successful completion of tasks in a dynamic environment efficiently. Methods: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on the response time of container using mechanism of control theory. Results: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload. Conclusion: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.

Background: User-generated digital content, typically from mobile devices, can be of a personal nature. There have been several instances in which this personal content, including but not limited to, photographs, videos, messages and personal information, has been misused by the recipients. The misuse has ranged from violating privacy through unauthorized sharing to manipulating/ modifying the original content and finally forgery and fraud. Objectives: The study aims to create an Advanced Secure Messaging Application (ASMA), allowing personal digital content to be shared across mobile devices in a secure, controlled and privacypreserving manner. The application allows the user to explicitly specify a micro-policy to control the way digital content sent by the user, is consumed, shared or modified by the recipient(s). The users should be able to check the veracity of a shared news item besides controlling group formation and enforcing content sharing guidelines within a group. Methods: A micro-policy based novel mechanism is introduced for exercising fine-grained control over the manner in which digital content generated by users is shared and distributed among their contacts. Fact-checking is supported by cross-checking the message content against credible news sources. Results: Measurements of message throughput show a consistent and manageable overhead, due to a per-message micro-policy travelling with the message data compared to traditional message sharing applications. The high user-ratings received from users in a pilot test establish acceptance of the app and the need for its advanced features. Moreover, the overheads in implementing additional security features are imperceptible at the user level. Conclusion: ASMA is a highly secure and privacy-preserving messaging application, as demonstrated by experimental results and the pilot usability study. It offers novel security and content control features, which are not available in existing mobile messaging applications.

Objectives: The objective is to provide a precise segmentation technique based on ACRF, which can handle the variations between major and minor vessels and reduce the interference present in the model due to overfitting and can provide a high-quality reconstructed image. Therefore, a robust method with statistical properties needs to be presented to enhance the performance of the model. Moreover, a statistical framework is required to classify images precisely. Methods: The Adaptive Conditional Random Field (ACRF) model is used to detect DR disease in the early stages. Here, major vessel potential and minor vessel potential features are extracted for precise segmentation of vessel and non-vessel regions. This feature enhances the efficiency of the model. These major vessel and minor vessel potential features rebuild the retinal vasculature parts precisely and help to capture the contextual information present in the ground truth and label images. This method utilizes an ACRF model to reduce interference and computation complexity. Here, two efficient features are extracted to segment fundus images efficiently, such as major vessel potential and minor vessel potential. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potential, unlike state-of-arttechniques, which provide patterns for only labels and model the contextual information only in labels, which is essential while performing vessel segmentation. Results: The performance results are tested on the DRIVE dataset. Experimental results verify the superiority of the proposed vessel segmentation technique based on the ACRF model in terms of accuracy, sensitivity, specificity, and F1-measure and segmentation quality. Conclusion: A highly efficient vessel segmentation technique is evaluated to describe major and minor vessel regions efficiently based on the ACRF to recognize DR in early stages and to ensure an effective diagnosis using eye Fundus Images. The segmentation process decomposes input images into RGB components through histogram labels based on the proposed ACRF model. Here, the Gabor filtering approach is used for pre-processing and predicting parameters. The proposed segmentation method can provide the smooth boundaries of minor and major vessel regions. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potentials, unlike state-of-art-techniques, which provide patterns for only labels and model the contextual information only in labels.