The software development life cycle (SDLC) is incomplete without the software testing phase. It's the act of checking that a piece of software really does what it's supposed to. Test case creation is one of the testing tasks that have a major impact on the quality and speed with which the process is completed. Research into the automated production of test cases has been extensive because of the time and energy it can save over the human method of creating test cases. While most of the recommended methods are based on UML models, other publications have given a specifications-based method of creating test cases. This literature analysis focuses on automated test case generation strategies based on use case specifications and the techniques used to verify them. The analysis also highlights the ways in which the methods diverge when used to solving certain pressing problems in software testing.

Twin Precision Treatment (TPT) is a novel Whole-Body Digital Twin (WBDT) enabled precision nutrition for reversing diabetes, that uses AI and the Internet of Things, to integrate multidimensional data, from non-invasive sensors, to give precision nutrition and health recommendations via the TPT app and by coaches. We conducted a prospective randomized trial to compare the changes in the glycemic metrics and the fatty liver parameters. The Intent to Treat (ITT) analysis among 233 people with T2DM who underwent WBDT was done at one year. 84 T2DM were provided the standard of care (SC). Remission was defined as an A1C level <6.5% for ≥ six months without diabetes medication use for at least 90 days. Normal values were: BMI <25kg/m2, HbA1c <6.5%, FPG <126 mg/dl, HOMA2-IR <1.22, NAFLD Liver Fat Score (NAFLD-LFS) <-0.64, Fatty Liver Score (FLS) <23, Framingham Steatosis Index (FSI) <23. Of the 233 people with T2DM, 209 subjects completed more than six months of WBDT. 72.7% (152/209) sustained remission at one year. The mean age, duration of diabetes, were 43 years (±9.5, range 17 to 69) and 3.4 years (±2.7, range 0.1 to 8). WBDT (n=233) intervention led to a significant number (p<0.0001) of subjects achieving the normalized values from baseline and at 1 year, for BMI (76 to 133), HbA1c (16 to 184), FPG (57 to 181), HOMA2-IR (48 to 130), NAFLD-LFS (26 to 149), FLI (30 to 120), FSI (29 to 135). ALT (68 to 140). One-year improvement in HbA1C was significantly better for WBDT than SC -3.0 (±1.8) vs. -0.2 (±1.2); p<0.001. Our ITT analysis shows that WBDT enables a high rate of remission of T2DM with a durable benefit, beyond six months for the glycemic control. There is a significant increase in the number of people who achieve the normalised biochemical and clinical values, for glycemic and fatty liver indices, while they remain in T2DM remission, at one year

The Low Code Development Platform (LCDP) is a versatile platform to handle process, database, mobile and web based applications. The platform provides us opportunity to digitize the activities in IT, telecommunication, government and all other industries as well as different departments of the organization in the form of applications. The manufacturing industry can also incorporate low-code apps for data analysis and their manufacturing processes to automate the process.This research work has proposed a novel sustainable LCDP with optimization techniques for data analysis. The proposed platform allows user to execute data analysis applications in optimization and without optimization mode. The result shows that optimized LCDP reduces both space and time required for the any type of application.

Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. The suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual Machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. The whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance.

Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.

Software Reliability (SR) is a substantial aspect of software quality and one of the most important variables to consider when evaluating the quality of a software product. Software managers have a difficult task in selecting the optimum SR model for a real software development project. Throughout the software life cycle, it is concerned with the creation and maintenance of dependable software systems. Software measurement, software modeling, and software enhancement are 3 steps of a three-step method for boosting software dependability. Each of these stages is critical to establishing a solid software system. To control software quality, it is critical to improving accuracy in predicting dependability. A variety of measures for assessing software dependability have been suggested in the literature. Machine learning (ML) methods have been demonstrated to be effective in assessing many aspects of software dependability. To capture the many aspects of a software system, numerous machine learning approaches have been developed in this work. In this review paper the results of the various algorithms are compared. ML classifiers are compared with each other based on accuracy, precision, and recall parameters to determine the best classifier. Machine learning methods have been used in comparative analysis to estimate the defect level of the software instances. Comparison was placed among SVM, NB, DT, RF, and ANN classifiers. Where ANN shows the best results among other classifiers it has highest accuracy of 65.5%.

Software Reliability is the challenge about predicting the failure free operation of software program with appreciates to time underneath particular environmental conditions. Software reliability can be additionally described as the likelihood which a software program machine can fulfill all affiliated project in a given constraints when it is having predefined range of enter cases. Here we are assuming that the hardware and the enter are free of error. While we have to consider highly stable software, software reliability is playing an important role because it is a major tool which is used to test information engineering technology. Based on the prediction, we can decide the reliability of the software. In the software reliability computation technique, previously standard statistical approaches are being used. But now these statistical approaches are replaced by intelligent reliability computation techniques which use machine learning techniques. These techniques have resulted in a major rise in software reliability requirements in recent years. Machine Literacy is dealing with automated data handling system which generates predefined rules & can learn from new data. In this paper, we are proposing architecture to enhance, to optimize and to validate software reliability using machine learning techniques.

Content is a user-designed form of information, for example, observation, perception, or review. This type of information is more relevant to users, as they can relate it to their experience. The research problem is to identify the credibility and the percentage of credibility as well. Assessment of such content is important to convey the right understanding of the information. Different techniques are used for content analysis, such as voting the content, Machine Learning Techniques, and manual assessment to evaluate the content and the quality of information. In this research article, content analysis is performed by collecting the Movie Review dataset from Kaggle. Features are extracted and the most relevant features are shortlisted for experimentation. The effect of these features is analyzed by using base regression algorithms, such as Linear Regression, Lasso Regression, Ridge Regression, and Decision Tree. The contribution of the research is designing a heterogeneous ensemble regression algorithm for content credibility score assessment, which combines the above baseline methods. Moreover, these factors are also toned down to obtain the values closer to Gradient Descent minimum. Different forms of Error Loss, such as Mean Absolute Error, Mean Squared Error, LogCosh, Huber, and Jacobian, and the performance is optimized by introducing the balancing bias. The accuracy of the algorithm is compared with induvial regression algorithms and ensemble regression separately; this accuracy is 96.29%.

A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.

The design of Hadoop has ability to elimination of fault tolerance, which consists of rescheduling the task on the defective nodes to run on other devices in the system.However, this strategy is ineffective if an error arises after most of the task has been completed.As a result, it is essential to make an early detection of the problem at the node to ensure that the resumption of the work will not result in a significant loss of both time and productivity.The ability to predict these problems provides us with the required time to move the workload onto different nodes, which helps to avoid data loss or processing time.In this paper, we propose an identification of faulty nodes from a large Hadoop distributed environment using machine learning techniques.Initially, we deployed one controller node and numerous data nodes as virtual machines in a distributed manner.The execution performs when the end-user submits a specific job to the controller node.The master node is the middleware controller that continuously communicates with other data nodes and assigns a task to each data node accordingly.However, this conventional process of HDFS that can generate data leakage or high computation whenever the specific node is heated or straggler.In our approach, we initially collect the log history of each data node and apply some statistical and a few machine learning algorithms to identify nodes' status.According to the achieved outcome of each node, we can decide to eliminate the specific node for task execution.We applied five machine learning algorithms in the extensive experimental analysis, including a Support Vector Machine (SVM).The SVM obtains 96.7% higher accuracy over the conventional machine learning classifiers for the entire execution.

Today, Low Code Development Platforms (LCDP) like Mendix, OutSystems, Zoho creators, Lansa, Quick base, and Salesforce works for modernize regular processes and quickly deliver new digitized applications. These platforms are capable to generate desktop apps, mobile apps, database apps and general-purpose apps. Basically, LCDPs has Integrated Development Environment (IDE) provides drag and drop, visual modelling, rapid application development and automatic code generation capabilities which makes them more popular. In proposed low code system, we have introduced optimization techniques for code to improve time and space complexity as well as to increase the sustainability of software. These tools often have What You See Is What You Get (WYSIWYG) editors and drag-and-drop interfaces to make development even easier. Once User Interface (UI) or visual model is prepared by using required tools/components; the respective source code will be automatically generated and optimized, then the application is ready for user to use.

The demand of new application development increases as the organizations grow rapidly. So, the development companies are facing various challenges to meet the new requirements of the customers, who demand for the applications within very less time. Low code development platform (LCDP) plays vital role to overcome this gap. This platform becomes a significant step towards successful development of applications for customer facing problems. Instead of traditional hand coding, it refers to the development of application through visual modelling tools using approach of Model Driven Development (MDD). It allows developer to more focus on functionality rather than syntax of the code. Rapid app delivery, minimum coding, scalability, agile development, rich functionality makes role of low code development platform more strategic in any digital transformation. Because of these features, LCDP enables organizations to build their own quickly with less time and money. This paper is based on study different low code platforms, identification of their functionalities and services. The paper also initiates a proposed system methodology of new optimized low code platform.

The organizations always ask development teams for new applications because off-the-shelf solutions are not enough to face the rapid changes in market. To overcome this problem and to have solution on tip of finger low code development platforms are developed. These platforms help enterprises to create their own business applications in very less time because it offers drag and drop interfaces and blocks of pre-build modules which minimize handwritten code. Low Code Development Platforms (LCDP) are capable to develop general-purpose, process-based, database, request handling, and mobile-based applications. In response, low code development platforms are growing in popularity to speed up the productivity and delivery of required applications. It makes development easier so that even people without programming skills (i.e. Citizen Developers) can develop applications on their own. The low code programming is based on different approaches like visual modeling, model-driven development, rapid application development and automatic code generation. Because of all these features, LCDP enables organizations to build their own apps by configuring the functions instead of coding the software from scratch. In this paper, we are going to understand limitation of present platforms/techniques from various papers and identify that scope of improvement.

Revolutionary software developing methodologies have captured the attention of many information technology industries nowadays. The complexity of software has increased to a higher level but the time limit and budget are also increasing. The customer also demands maintenance and quality of software. Henceforth, the requirements cannot be given at the starting and this is the main drawback of heavyweight methods. In heavyweight methods, changing and incomplete requirements create difficulty for developers. The recent methodology aims to remove all these challenges. Recently, agile methodology is adopted by various organizations for improving efficiency of software. While many researchers have gathered various information on Agile and suggested their own contribution, this paper has identified the success rate of projects, which are using any one of the agile methodology.

Faulty nodes are expected to have a significant effect on the performance of Big Data systems. The cause of discharges, on the other hand, is a complex issue. The previous work has mostly focused on identifying Stragglers, optimizing the quality of programming, and root cause investigation. These methods can't offer useful information unless they can assist users automate their services. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. This research proposed a dynamic log collection of heterogeneous virtual machine data and detect the faulty node based on execution performance using various parameters. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions of nodes. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead of eliminating those nodes. The major benefit of this research is that the system can handle massive data with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naive inter learning frameworks.

Node straggling is most impactful thing to improve the Quality of Service (QoS) in distributed as well as cloud environment. On the other hand, the reason for which node discharges are caused is complicated. The precedents work mainly focus on detecting Stragglers, optimizing the level of programming and analysis of the root cause. Without order to help users automate their services, these approaches cannot provide valuable information. We in proposed new approach, a general method that incorporates framework and characteristics of the system for the analysis of root cause for detection of stragglers in the big data system, which basically reduce the process execution time and generate job failure issues during execution. We proposed an framework for straggler node detection from distributed environment using machine learning algorithm based on large scale job performed log data. After execution of machine learning algorithm system will predict straggler nodes list and dynamically eliminate such nodes from execution list. Q-Learning based reinforcement learning algorithm initially proposed for execution and validate the system result in multi node environments.

As per recent discussions, Agile is a software development framework, which has developed software by using 1-4 week iterations, which will produce better quality software as per customer requirements. Agile methodology mostly deals with the frequent changing requirement in the software project. Instead of traditional method, it is always beneficial to use Agile as a stakeholder because traditional software methodologies are less prone to changes. In this research paper, authors have studied the effect of agile approach in the software development process in terms of quality, business worth and architectural framework.

The opportunity of early estimating of the possibly faultiness of software should assist on making plans, controlling and executing software development sports correctly. It will also give the developer and users the confidence to appropriately select and apply models to their required systems. It is very essential to understand and quantify the relation between faults and failure for efficient detection and prevention for quality software development. The proper investigation of the sources of fault and failure helps developers to focus their efforts in right direction. This enhances the fault and failure detection and prevention mechanism for the quality and cost effective solution in software development. So in this paper we are going to use data extraction method to extract data and to predict values we are going to use random forest algorithm also here we have defined flow diagram and Failure rate decreases, schedule time execution. In next step we are trying to implement these technique to give very high reliability value.

Methodology of aspects is a combination of multiple concerns. Multiple types of concerns are facts, logic, area of interest, security, properties of systems. In the Development phase of the aspect model Aspect considers different systems concerns, aspects divide these system concerns into software modules, Different aspects modules use as a way for analysis of systems requirements. In some aspects, methodology use UML design modeling for understanding system requirements, in aspects Methodology UML class is for knowing the system necessities in modeling phases. a UML class structure is used in the Aspects process model for requirement analysis of the system. Class design in UML design consists of various types of attributes, classes, objects, methods, Join of points & Point of cut, various approaches to defining various concerns of the system. Few tools for crafting graph grammar rules for analysis system concerns, crafting G-graph grammar rules start from pre-condition G-grammar rules after that crafting post condition G-graph grammar rules, after crafting G-graph pre and post condition grammar rules G-graph transformation process done on rules in tools, next step is a method of creating a matrix, a matrix is basically cross applying rules to each other and find output, Two types of the matrix created first is a matrix of dependency and matrix of conflicts, this matrix for analysis conflict and dependency in crafted G-graph grammar rules, these G-rules apply as input to aspect methodology Tool. Next step transformation G-graph grammar, G-graph rules shows pre and post transformations of G-graph grammar rules when applying matrix of dependencies with a matrix of conflict, the conflict shows clash in G-graph rules, dependencies show requirement among the G-graph rules.