Software Quality Engineering

The article discusses the use of asynchronous programming in Node.js to improve the performance of web services. The study aims to identify the advantages and methodological approaches to using asynchronous methods such as callbacks, promises, and async/await to optimize request processing and minimize response time. The methodology is based on analyzing the features of the event-oriented Node model.js, which provides non-blocking I/O operations. The results demonstrate that asynchronous programming significantly improves the performance and scalability of web services, especially in high-load and multitasking environments. The findings confirm that using asynchronous approaches in Node.js allows you to manage resources efficiently, reducing latency and improving overall application performance. Further research may be aimed at integrating asynchronous methods with other technologies to increase the stability and fault tolerance of systems.

Today'senterprise systems based on increasingly complexsoftware architectures often exhibit poor performance and resource efficiencythus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly.W epropose anew performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-levelp erformance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose at ailorable model solving approach to provide flexibility in viewo fp rediction accuracyand analysis overhead.

This guide explores a detailed framework for assessing test automation platforms on scalability, flexibility, security, integrations, and business value. It is designed for quality engineering leaders, IT architects, and procurement teams responsible for enterprise tooling decisions.

Design patterns are repeatable fixes for common issues in software design. Even if it's helpful for software analysis, finding design patterns may be difficult, particularly in big and intricate software systems. A number of tools have been put out in this area to automate this procedure. Review the many studies on software design patterns in the literature in this topic. This review highlights the varied impact of design patterns on software quality and maintainability. While some studies suggest that design patterns enhance software quality, others argue they can be detrimental, with results varying based on factors like failure rates, performance, and maintainability. Design patterns such as Data Management UI Page and Dependent Dropdown Filters improve consistency, code reuse, and development efficiency. Among 42 design pattern detection (DPD) tools, only ten are available online, with low detection accuracy and weak agreement among tools. GEML, a novel approach using evolutionary machine learning, improves detection accuracy but may generate false positives due to limited training data.

Design patterns are repeatable fixes for common issues in software design. Even if it's helpful for software analysis, finding design patterns may be difficult, particularly in big and intricate software systems. A number of tools have been put out in this area to automate this procedure. Review the many studies on software design patterns in the literature in this topic. This review highlights the varied impact of design patterns on software quality and maintainability. While some studies suggest that design patterns enhance software quality, others argue they can be detrimental, with results varying based on factors like failure rates, performance, and maintainability. Design patterns such as Data Management UI Page and Dependent Dropdown Filters improve consistency, code reuse, and development efficiency. Among 42 design pattern detection (DPD) tools, only ten are available online, with low detection accuracy and weak agreement among tools. GEML, a novel approach using evolutionary machine learning, improves detection accuracy but may generate false positives due to limited training data.

Software process evaluation measures the effectiveness of the software processes employed in a software development organisation. The two prevalent evaluation techniques are SCE and ISO/IEC 15504. An endeavour to improve a software process can begin with a software process review. Software system assessment as well as enhancement go hand in hand with software process modelling. The most pertinent findings from both methods are reported in this work, and, With SCE and ISO/IEC 15504 being the two most popular evaluation methods, software process evaluation gauges the effectiveness of the software processes employed in software development organisations. The first step in any effort to improve a Software process evaluation and improvement are one thing, and software process modelling is another. In this study, the most pertinent findings from both methods are provided. Process improvement: Firms can identify areas for improvement in the method of developing software and put those improvements into practise to increase effectiveness, productivity, and quality. They can improve resource allocation, remove bottlenecks, and streamline procedures thanks to it. Quality assurance: Analysing the software manufacturing procedure enables the early detection of potential flaws and errors. Effective evaluation techniques can help organisations identify problems early and fix them before they have an impact on the final output. This enhances client happiness and helps deliver high-quality software to end users. Cost and resource management: Organisations can spot inefficiencies and wasteful resource allocation with the aid of a well-evaluated software development process. Organisations can lower development costs, increase resource utilisation, and more efficiently use budget and staff by optimising the process. The DEMATEL (Decision Making Trial and Evaluation Laboratory) method addresses a specific issue, pinup binding. Work through problems with a hierarchical structure. Contribute to identifying workable solutions. Structural modelling techniques are used for one reason: interrelationships between organizational components. Dependency identification and context It can affect the basic concept of relationships. and chart direction due to the influence of elements. makes more use of graphs. Requirements/ analysis, Design, Coding, Testing and Maintenance. The Rank using the DEMATEL for Software Development Process Evaluation in Requirements/ analysis is got the first rank whereas is the Design is having the Lowest rank.

In this research work, a novel Two-Branch DCNN framework has been proposed for the classification of disease in tomato plant that efficiently works in CIE Lab colour space. By considering achromatic (L channel) and chromatic (AB channels) information in separate dedicated branches, the model is both more accurate and efficient. Experiments on Plant Village dataset have shown consistent and competitive performance, and the 20%L + 80%AB model yielded 99.48% classification accuracy, which outperformed some competitive state-of-the-art GoogLeNet (98.37%) and AlexNet (97.82%). The architecture also retains strong performance on more difficult Cropped-PlantDoc dataset, with 50%L + 50%AB achieving an accuracy of 76.91%, outperforming baselines by 6 percentage points at least, while decreasing the number of trainable parameters and floating point operations by 30-50%. An ablation study indicates that the colour and the greyscale branches influence significantly on the overall performance, and the diverse optimal filter partitioning between datasets supports that the design of the architecture is able to adapt to various imaging conditions. We demonstrate a resource-efficient application of tomato plant disease diagnosis which could be installed and used in resource-limited-solution for field-level indoor disease detection on farmers’ side with real-time analysis. An Efficient Method of Tomato Diseases Based on Deep Learning in CIE lab colour field.

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Several organizations look for improving their business processes in order to enhance their efficiency and competitiveness. Business process management approach includes techniques allowing continuous business process improvement. Process mining is a business process management technique allowing to extract knowledge from event logs commonly available in today’s information systems. Business process model extension is a process mining technique enriching a business process model with different perspectives useful for decision making support. Furthermore, financial costs incurred during business process execution is prominent information needed for business process improvement decision making in terms of cost reduction. We propose a solution for business process model extension with cost perspective based on process mining. The solution is based on cost extension of the high-level process structure, which is a meta-model enabling the integration of different perspectives into one mod...

In today's fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. In semi-supervised learning, we tested are autoencoders, semi-supervised support vector machines, and generative adversarial networks. For self-supervised learning, we utilized are autoencoder, simple framework for contrastive learning of representations, and bootstrap your own latent. After comparing the performance of each machine learning algorithm, we identified the most effective one. Among these, the gradient boosting Ad-aBoost classifier demonstrated superior performance based on an accuracy of 90%, closely followed by the AdaBoost classifier at 89%. Finally, we applied ensemble methods to predict software defects, leveraging the collective strengths of these diverse approaches. This enables software developers to significantly enhance defect prediction accuracy, thereby improving overall system robustness and reliability.

Subscription-based digital platforms and payment systems operate at the intersection of reliability, performance, and financial compliance. Failures in these systems can result in financial loss, regulatory repercussions, and reputational damage. Ensuring resilience-the ability to maintain service availability and data consistency under stress or failure conditions-is therefore paramount. This paper presents a comprehensive set of test strategies to validate and improve the resilience of subscription apps and payment platforms. The proposed methodologies span chaos testing, load testing, disaster recovery simulation, and fault injection, all integrated within automation and CI/CD to enable continuous resilience assurance.

Data mining is the process of discovering patterns, correlations, and useful information from large datasets using techniques from statistics, machine learning, and artificial intelligence. The growing volume of data in fields such as business, healthcare, social media, and finance has made data mining an essential tool for extracting valuable insights that can inform decision-making and optimize processes. This analysis explores the key methodologies in data mining, including classification, clustering, regression, association rule mining, and anomaly detection. It also examines the challenges in data preprocessing, such as handling missing data, data normalization, and feature selection. Furthermore, the paper discusses the ethical concerns related to data privacy and the impact of biased algorithms. By evaluating the current trends and applications of data mining, the study provides an understanding of its transformative role in various industries and highlights future directions for research and development in this rapidly evolving field.

Algorithms for encoding and decoding information play a critical role in the optimization of modern systems, enabling efficient data representation, transmission, storage, and retrieval. This paper explores a landscape of encoding and decoding algorithms in payment systems with a focus on enhancing system performance across diverse domains, including communication networks, distributed computing, and machine learning. We analyze lossless and lossy encoding techniques, discussing their trade-offs in terms of compression ratio, computational complexity, and resilience to errors. Special attention is given to entropy-based methods such as Huffman and arithmetic coding, as well as modern techniques like neural compression and error-correcting codes including LDPC and Reed-Solomon codes. Furthermore, we investigated adaptive and context-aware encoding schemes that dynamically adjust to data patterns and system requirements, contributing to real-time optimization. By integrating these algorithms within larger system architectures, substantial gains in throughput, latency, and energy efficiency are demonstrated. The paper concludes with a discussion on current challenges, including scalability and security, and proposes future research directions that leverage advances in artificial intelligence and quantum computing to further enhance encoding and decoding strategies for next-generation system optimization.

The problems related to not meeting deadlines and overflow costs, should not be deposited only to the techniques and technologies used, but also the absence of processes focusing on the development and qualification of its members. This paper propose a group of some workflow instructions called H2B-MPS, an extension of the process presented by Horita et al. (H2B) (2012), and developed to be aligned to the process of Human Resource Management (GRH) present in the MR-MPS-SW. For this reason, in each of H2B activities, we defined a workflow instruction that seeks not only to meet expected results, but incorporate the best practices of GRH and the MR-MPS-SW. Through the mapping between their work instructions and expected outcomes of the GRH is possible to verify that the model is in accordance with the MR-MPS-SW.

The proliferation of AI-assisted development tools has created a new challenge: configuration fragmentation. Developers using multiple AI coding assistants must manage platform-specific configuration formats, leading to vendor lock-in, maintenance overhead, and reduced experimentation. We present AI Context Schema, a universal configuration framework that enables developers to define AI assistant context once and deploy it across multiple platforms automatically. Our approach uses a platform-agnostic schema with intelligent adapters that preserve behavioral intent while handling platform-specific constraints. We demonstrate the effectiveness of our approach through a reference implementation supporting four major AI platforms (Claude Code, Cursor, Windsurf, GitHub Copilot) and show that universal schemas can eliminate configuration fragmentation while maintaining platform-specific optimizations. Our work establishes the foundation for interoperable AI development tooling and provides a path toward standardization in this rapidly evolving ecosystem.

Home healthcare worker scheduling is a hard combinatorial problem concerned with the allocation of care tasks to healthcare givers at a minimal cost while considering healthcare service quality by striving to meet the time window restrictions specified by the patients. This paper proposes a group genetic algorithm (GGA) for addressing the scheduling problem. The approach utilizes the strengths of unique group genetic operators to effectively and efficiently address the group structure of the problem, providing good solutions within reasonable computation times. Computational results obtained show that the GGA approach is effective.

As the need for software increased, the number of software firms and the competition among them also increased. The software companies in developing countries like India can no longer survive based on cost advantage alone. The firms need to deliver competitively priced quality software products on time. This can be achieved through quantitatively managing the different phases or sub processes in software development process. But quantitative management of a process consisting of a set of interlinked sub processes or stages with the output of one sub process influencing that of subsequent stages and final output is not easy. The process performance models developed for quantitative management of software development process often model the final outcome in terms of factors from various stages together or focuses only on quantitatively managing a particular sub process independently. In manufacturing and other engineering industries, the processes with multiple sub process are monitored and controlled using multistage process control methodology. This paper is an application of multistage statistical process control for managing the software development process. The suggested methodology is a combination of process performance models and control charts. The proposed methodology can be easily implemented for controlling various types of software projects like development projects, incremental development projects, testing projects etc. The methodology also provides the project manager the opportunity to tighten or relax the control at various sub processes based on the project team's strengths and still achieve the goal on the final outcome. 2017 Growing Science Ltd.

Software reusability facilitates the engineering of new software or systems functionalities without having to start coding from scratch. This software quality provides numerous merits to the software developers including coming up with larger systems within a short time, with reduced development cost and reduced developer effort. However, software reusability is an external software quality attribute that is not measurable directly, and this is a major hindrance factor to its adoption in the software engineering process. This indicates that there is a need to come up with an approach to measure or predict software reusability to give advisories to the software developers on the prediction of software reusability. Therefore, this study aims at coming up with a prediction model to estimate software reusability by employing the use of a fuzzy logic approach, the study focused on Object-Oriented software. The research followed a guideline of four objectives and assumed descriptive and d...

This paper presents some advances towards the quantitative evaluation of design attributes of object-oriented software systems. We believe that these attributes can express the quality of internal structure, thus being strongly correlated with quality characteristics like analyzability, changeability, stability and testabilility, which are important to software developers and maintainers.<br> An OO design metrics set is reviewed, along with its rationale. An experiment for collection and analysis of those metrics is described and several suppositions regarding the design are evaluated. A considerable number of class taxonomies written in the C++ language were used as a sample. A tool to collect those metrics was built and used for that purpose. Statistical analysis was performed to evaluate the collected data. Results show that some design heuristics can be derived and used to help guide the design process. It was also clear that a number of follow-up topics deserve further re...

As cyber threats targeting financial institutions grow more complex and persistent, the integration of threat intelligence into DevSecOps pipelines has emerged as a critical strategy for enhancing security in the banking sector. This study investigates how banking organizations leverage threat intelligence to strengthen proactive defenses, automate risk responses, and ensure regulatory compliance. Using a descriptive quantitative approach and structured questionnaires distributed to 60 IT and cybersecurity professionals across various financial institutions, the study examines the level of awareness, utilization, perceived benefits, and challenges associated with threat intelligence in DevSecOps environments. The results reveal a high level of threat intelligence adoption and strong agreement on its benefits-particularly in early threat detection and automation. However, challenges such as a shortage of skilled personnel, integration complexity, and financial constraints hinder effective implementation. The study concludes by recommending enhanced training, adoption of scalable platforms, improved team collaboration, and the use of AI-driven filtering mechanisms to optimize the impact of threat intelligence in secure DevSecOps workflows.

Cited in: Adebayo, Ayobami. 2025. “LEVERAGING THREAT INTELLIGENCE IN DEVSECOPS.” figshare. https://doi.org/10.6084/m9.figshare.29602481.v1

Global Software Development (GSD) involves multiple sites which comprise of different cultures and time zones apart from geographical locations. It is a common software development approach adopted to achieve competitiveness. However, due to multiple challenges it can result in misunderstandings and rework. Rework raises the chance of project failure by delaying the project and increasing the estimated budget. The aim of this study is to identify and categorize the rework causes to reduce its frequency in GSD. To identify the empirical literature related to causes of rework, we performed a Systematic Literature Review (SLR). A total of 23 studies are included as a result of final inclusion. The empirical literature from the year 2009 to 2020 is searched. The overall identified causes of rework in GSD are categorized into 6 major categories which are communication, Requirement Management (RM), roles of stakeholders, product development/integration issues, documentation issues, and differences among stakeholders. The most reported rework causes are related to the category of communication & coordination and RM. Moreover, an industrial survey is conducted to validate the identified rework causes and their mitigation practices from practitioners. This study will help practitioners and researchers in addressing the identified causes and therefore reduce the chances of rework.

Various models and methods are used to support the design process of SOA (Service Oriented Architecture), but still after many years of practice, there are a lot of questions and unsolved problems that cause the failure of SOA development projects. One of the reasons is that rapid changes in the business environment make it necessary to introduce the decision design, which should be efectively supported by SOA. Indeed, it is a big challenge to create a system that help the human resource development in industry to make their work easier without missing an opportunity to get a best employee. The objective of this study is to develop a decision making and Service Oriented Architecture for employee recruitment using analytic hierarchy process. This study explored the relationship between SOA and decision making during the recruitment process. To achieve our goal, we use the new method SOA+d to develop the SOA architecture. Also we provide the decision using the new standard Decision Mo...

Payment systems are vital for the functioning of today's economy, from retail transactions through to the settlement of interbank final payment obligations. Even with the increasing migration to electronic money transfers from the dominance of cash and checks, the basic aspects of payment systems are broadly similar to when the Bank of England was established to provide a facility for banks to convert their notes and settle interbank obligations. Fundamental concepts in payment systems are risk transfer from payer to payee, and relinquishing control over resources by the payer.

Payment systems are vital for the functioning of today's economy, from retail transactions through to the settlement of interbank final payment obligations. Even with the increasing migration to electronic money transfers from the dominance of cash and checks, the basic aspects of payment systems are broadly similar to when the Bank of England was established to provide a facility for banks to convert their notes and settle interbank obligations. Fundamental concepts in payment systems are risk transfer from payer to payee, and relinquishing control over resources by the payer.

Every vehicle manufactured today contains a fundamental programming flaw that wastes fuel on every downhill slope. We prove through deduction that automatic neutral coasting on declines would increase fuel efficiency by 23-31% without any hardware modifications. When engines idle at 600-800 RPM in neutral versus 1,500-2,500 RPM in drive, fuel consumption drops by 68%. Given that 18% of road distance consists of downhill grades exceeding 2%¹,², simple transmission programming could save 41 billion gallons of fuel annually in the US alone. Any driver can verify this: reset your MPG calculator before a downhill, shift to neutral, and watch efficiency spike from 30 to 150+ MPG. Using Bayesian analysis, we demonstrate P(Fuel_saved| Neutral_coasting) = 1 while P(Current_practice_optimal) < 10⁻ 2 0 7 6. This deduction reveals automakers have overlooked the simplest efficiency gain in automotive history-not through innovation, but through logic.

Traditional software testing methods, which rely heavily on manual quality assurance towards the end of the development lifecycle, were sufficient for static, simpler systems. However, they fall short for increasingly complex and large applications with multi-service architectures, where developers lose individual visibility, increasing the risk of regressions. Manual testing is time-consuming and prone to errors, lacking the scalability and comprehensive quality coverage required for modern, rapidly evolving systems. This approach often results in delayed feedback, inconsistent outcomes, and higher costs within an agile methodology. Although automated testing is superior to manual testing, current automated testing methods still rely on manually scripted tests and testing environments. As codebases grow, these become challenging to maintain, often leading to fragmented workflows, false positives, or overlooked defects due to inadequate integration of processes within the deployment cycle. This review proposes addressing these challenges by integrating an automated code testing framework directly into the CI/CD pipeline for Kubernetes-based deployments. It includes high-level automation for triggering events in GitHub using pull requests, enabling comprehensive automated checks such as unit, static, and vulnerability tests to ensure correctness. Subsequently, temporary deployments to Kubernetes are made for functional and performance testing in production-like environments. This process ensures that only thoroughly tested code is pushed and deployed, reducing the likelihood of production bugs, downtime, and security vulnerabilities. Additionally, it automates environment creation and teardown, conserving engineering resources. The key benefits include improved code quality, faster feedback cycles, enhanced security, and adherence to modern DevOps practices of automation and collaboration. However, challenges remain in maintaining test suite consistency, creating realistic test environments, and managing continuous integration complexity. Overall, this research contributes to scalable, robust, and highquality software delivery in cloud-native architecture, setting a new standard for deployment assurance.

This comprehensive article explores the transformative impact of AI-driven predictive analytics in modern Enterprise Resource Planning (ERP) systems. The article examines how the integration of artificial intelligence and machine learning capabilities has revolutionized organizational decision-making processes, operational efficiency, and strategic planning. The article investigates key application areas including financial forecasting, inventory optimization, and customer behavior analysis, while also addressing technical implementation considerations and system architecture requirements. The article demonstrates how AI-enhanced ERP systems have enabled organizations to achieve significant improvements in operational performance, risk management, and market competitiveness through advanced data processing and predictive modeling capabilities.

Most software developers aims at having a highquality software that is easily maintainable, easily understandable, well structured, reliable, etc. Measuring software complexity is quite important as high complexity has been identified to be the reason behind difficult understanding and reading and problematic changes in the future. Added to that, it has been the source of defects and poor software quality in the present time. Accordingly, high complexity entails the presence of serious faults in the software and duly higher costs to maintain and fix. Software complexity metrics determine the improvement of software quality and project controllability to a large extent. In this work, we have calculated the software complexity metric (McCabe's Cyclomatic Complexity (CC)). Simplifying testing by guaranteeing the execution of at least one statement during testing. That is to say, the number of test cases will be equal to the Cyclomatic Complexity of the program. Such information is important for testing. Identifying reliability risk level, testability level; and cost/effort so as to maintain (maintenance risk level) by depending on Cyclomatic Complexity CC. Also, identifying Bad fix probability of an error that is unintentionally set into a program at the time of fixing a previous error, or while maintaining a code. As the complexity of the software increases, the probability to introduce new errors also increases. Finally, we have explained the results which are under discussion by specific tables and figures to illustrate the value and information of the metric in detail.

Background Information: Usability testing is essential for ensuring a seamless user experience in modern applications. Traditional methods often lack scalability and adaptability to real-world scenarios. By integrating A/B testing, AI-driven contextual testing, and codeless automation tools, testing efficiency improves, enabling dynamic UI evaluation, real-world usability assessments, and streamlined automation for comprehensive, data-driven usability testing. Objectives: This study aims to enhance usability testing by leveraging A/B testing for UI optimization, AIdriven contextual testing for real-world adaptation, and codeless automation tools for efficiency. The goal is to increase accuracy, improve test scalability, and streamline the usability evaluation process for faster, more reliable application development. Methods: A/B testing analyzes multiple UI versions for user engagement effectiveness. AI-driven contextual testing simulates real-world user interactions to detect usability issues dynamically. Codeless automation enables no-code usability test execution, ensuring broader test coverage and increased efficiency while reducing manual intervention in the usability evaluation process. Empirical Results: The proposed method achieved 95% usability issue detection, 96%time efficiency, and 98% scalability. AI-driven contextual testing identified hidden usability flaws, while codeless automation improved resource utilization and testing accuracy, ensuring robust usability assessments.

The software industry is continuously evolving, requiring robust frameworks to manage innovation, efficiency, and scalability. The People, Processes, Products, and Technology (PPPT) framework provides a holistic approach to software development and management. This paper explores the PPPT framework in detail, discussing its significance, implementation strategies, and real-world case studies that illustrate its effectiveness. By synthesizing existing research and industry insights, we provide a comprehensive understanding of how organizations can leverage this framework to achieve competitive advantages.

Software Production Lines (SPLs) aim to manage cost-based activities for product delivery. Our company has been using SPL engineering for about 10 years and successfully implemented cost-controlled production cycles for SPLs during past two years, which are based on well-known Function Point (FP) approach supported by International Function Point User Group (IFPUG). Cost-based product delivery in SPLs requires the complete transformation of requirements gathering, cost estimation, time planning and productivity measuring steps. At the maturity level reached so far, every contributing part of the production line can be measured and cost-attached effectively and new targets can be set accordingly. Moreover, production bandwidth can be estimated precisely based on statistical productivity coefficients of every working team. This paper introduces our cost-controlled SPL approach, the achievements so far and our future plans for improvement. Keywords-Function Point; Software Measurement;...

Desde el año 2005, se implementó el modelo denominado "Colectivo Docente", una propuesta que nace desde el proyecto educativo institucional y que en el artículo presentado evidencia, como conclusión fundamental, la generación de mayor claridad conceptual y metodológica en estudiantes y profesores, sobre temáticas abordadas y tradicionalmente complejas, cuando se diseña e implementa un currículo problémico.

Many maturity models have been used to assess or rank e-government portals. In order to assess electronic services provided to the citizens, an appropriate e-government maturity model should be selected. This paper aims at comparing 25 e-government maturity models to find the similarities and differences between them and also to identify their weaknesses and strengths. Although the maturity models present large similarities between them, our findings show that the features included in those models differ from a maturity model to another. Furthermore, while some maturity models are covering some features and introducing new ones, it seems that others are just ignoring them.

Component based development has become an wellknown move towards developing quality software and promote reusability. The tester of software component determines the quality of software before it is delivered to component integrator. For developing a new product with little recourse we use component based development approach. When we integrate different components together to make a new product then the quality of the product depends upon the software component. We gather different components from different sources including component vendor, component developer. Testing of each component is necessary to ensure the quality of final product. This process reduces the bugs but increases the teething troubles to the tester. During testing the problem arises that the tester have limited access to the component as the source code is unavailable. This paper demonstrate and analyze the technique to improve the testing of component based software and identifies the challenges and issues concerning to testing of final component based software product. Some recommendations are also mentioned to improve the testing technique of component based software.

Semiconductor devices are the essential building blocks in today's information technology and society. Next-generation semiconductor devices such as FinFETs, GAA-FETs, and nanowires have been proposed to serve for high-performance and lowpower applications in the deep-submicron technology nodes. However, with the rapid scaling of semiconductor technology nodes, traditional test generation methods face large runtime and memory footprint challenges to ensure efficient fault diagnosis and reliability screening. In this regard, machine learning (ML) has emerged as a new paradigm to address the above issues with great success. This paper presents recent advanced testing frameworks for next-generation semiconductor devices by enabling ML. Several successful demonstrations are covered, creating and exploiting novel ML models of various levels of complexities. At the device level, fast test generation methods using shallow ML models are presented, yielding significant improvement in runtime and memory efficiency compared to existing commercial tools. At the circuit level, the applicability of deep learning-based approaches for stuck-at fault identification and location is explored. Finally, a massive data generation and representation learning framework for deep neural network-based built-in self-test (BIST) generation is presented to improve the design robustness of the built-in test (BIT) architectures for large-scale applications. The basic concepts and implementations are first introduced, followed by successful demonstrations and industrial applications. This opens up questions on how advanced ML models can improve existing approaches, where they might fail, and how to mitigate the biases. Manual test development for semiconductor chips relies on designerś knowledge, experience & heuristics, requiring substantial time & effort. ML models representative of test-influencing factors and their likely trade-offs can be created directly from the available test data. Such auto-generated ML models can then be exploited for test generation or validation, or to estimate the test cost based on semiconductor models. A bottleneck in this approach is the time-consuming, complex, intensive operation of taking different process steps to generate quality physical or logical test data on the specific test chips. ML methods are exceptionally datahungry, requiring a large volume to train and generalize successfully. However, there are many untapped sources of ample simulation data both from older chips and different fabrication processes. It is possible to create new problem representations, or simulation domains, that standardize/normalize all factors not relevant to a particular problem. Various progressive domain adaptation methods can then be used to adapt existing ML models from these pre-trained representations to the new problem domain. Once adapted, the production ML models can be used for fully automated test generation or engineering studies.

El aprendizaje profundo es un subconjunto del aprendizaje automático que utiliza redes neuronales multicapa, llamadas redes neuronales profundas, para simular la compleja capacidad de toma de decisiones del cerebro humano. Alguna forma de aprendizaje profundo impulsa la mayoría de las aplicaciones de inteligencia artificial (IA) en nuestras vidas actuales. La principal diferencia entre el aprendizaje profundo y el aprendizaje automático reside en la estructura de la arquitectura de red neuronal subyacente. Los modelos de aprendizaje automático tradicionales, denominados «no profundos», utilizan redes neuronales simples con una o dos capas computacionales. Los modelos de aprendizaje profundo utilizan tres o más capas, pero normalmente cientos o miles de capas para entrenarlos.

Software Development as a field is exponentially growing. It is deemed essential to innovate and lead. Though technologies from time to time have greatly impacted the development methodologies, it is AI now that has ushered software development into a new era of possibilities. It truly has helped with optimizing the SDLC (Software Development Lifecycles), but there are myriad challenges that its integration into software engineering entails. AI tools are fast transforming the ways the codes are written, generated, improved, optimized, reviewed, debugged, and managed. From saving time that often goes into carrying out repetitive tasks to analyzing or testing code, AI tools have made almost all things simple. NLP (Natural Language Processing) is one of the best core features of AI-human interaction. The users provide queries in simple English and get the results they need to speed up their development process. This paper highlights the current practices, strategies, methodologies, and procedures enterprises employ to speed up their development processes. It systematically reviews the existing literature and discusses the difficulties underlying AI integration into enterprise software development practices, the complex nature of the AI models that the companies use, data storage, and data security. Further, it analyzes the AI-human collaboration from different perspectives, and how it fosters a culture of continuous learning, enabling the developers to learn and grow their development skills too. It also sheds light on the use of the current practices and what the future possibilities are.

The paper examines a review of techniques and criteria for software evaluation. It reveals software evaluation as a serious aspect of software engineering process that involves choosing one software product amongst other alternatives. Techniques for software evaluation are discussed such as Analytic Hierarchy Process (AHP) Software Evaluation Technique, weighed average sum and Fuzzy Based Approach Software Evaluation Technique. The strength and weakness of each technique is presented to give a broad understanding of the techniques for software evaluation. Criteria to be considered for software evaluation are usually classified in several groups. Several groups of evaluation criteria are needed for software evaluation such as vendor, hardware/software, cost/benefits, security and reliability. It is concluded that software evaluation is important as it enables Software companies and individuals/organizations that make use of software to make sure they provide software solutions that are reliable and can meet the needs of users.

This paper further elaborates on how the emergent effects of CBDCs, blockchain technology, and artificial intelligence alter the global financial landscape regarding transaction costs, customer satisfaction, and operational efficiency. The research uses a dataset of 100 institutions drawn from diversified global markets in pre-and post-implementations of CBDCs and blockchain technology. It considers that AI reduces transaction costs, besides providing customers with an experience with even better operational performance. SPSS was used during the statistical and correlation analysis computation. At the same time, Python was implemented during visualization work, and a box plot with multi-line graph drawing was used to represent results. The adoption of CBDCs and blockchain implementation resulted in high reductions in transaction costs, improvements in customer satisfaction and efficiency through AI-driven solutions, and, to a large extent, measured improvement in speed and service quality of transactions. In that sense, the findings show that these technologies change the face of financial transactions towards becoming faster, cheaper, and more personal. All of these research findings suggest that these new digital technologies could make the world's financial sector more efficient, accessible, and competitive thereafter.

Abstract—Cloud Computing is offering competitive advantages to companies through flexible and, scalable access to computing resources. More and more companies are moving to cloud environments; therefore understanding the requirements for this process is both important and beneficial. The requirements for migrating from a traditional computing environment to a cloud hosting environment are discussed in this paper, considering this migration from a supply chain lifecycle perspective. The cloud supply chain is examined from a lifecycle perspective for the management of the migration project. This paper illustrates the requirements that need to be considered when adopting a cloud migration strategy and the steps to take in order to manage this process. Index Terms—cloud computing; supply chain; cloud sourcing; cloud lifecycle.

Cloud Computing is offering competitive advantages to companies through flexible and, scalable access to computing resources. More and more companies are moving to cloud environments; therefore understanding the requirements for this process is both important and beneficial. The requirements for migrating from a traditional computing environment to a cloud hosting environment are discussed in this paper, considering this migration from

Agile methodologies are among the most popular methodologies to develop software in recent times. They aim to build up software wherein requirements are constantly changing and seek to make development easy while ensuring quality. The different agile techniques like XP, Scrum etc. use best ways that help to improve Quality. Thus ensuring Software Quality Assurance (SQA) in the product delivered. The agile process extreme programming is targeted towards smaller development teams and requires relatively few details. In this paper Quality Assurance techniques of extreme programming has been focused upon. The analysis of the different techniques has been carried out on basis of important features, quality factors achieved, timing, and cost.

Software quality has been an important issue since long. There are lots of disciplines related to the quality of software but we have focused towards the defect prevention for outsourcing projects. Nowadays, quality is considered to be the most important aspect which has direct impact on success of the software product. Significant research pertaining to quality of the software and its methodologies has been done earlier. Nevertheless, defect prevention is the key aspect to achieve quality of a product. Many organizations outsource their projects and as a result the quality issues arose. A number of quality models, metrics and standards have been development for the software to run on standalone and distributed environment. However, the issue of quality for the outsourced projects particularly for the projects which are partially outsourced still needs further research. In this paper, we propose a framework for effective monitoring the software quality for both fully and partially o...

Most of the software projects fail to meet the desired level of quality and standards due to different types of defects introduced during the course of requirement solicitation, designing and development. These defects inexorably hinder the secure deployment or smooth operations of the software systems. One of the key reasons for this misfortune is the lack of proper defect prevention planning while formulating the software architecture. Defect prevention needs to be a thorough and critical phase because it has a direct impact on quality of the product which cannot be compromised. This paper looks into different defect prevention techniques and analyses them critically. The scope of this study is restricted to the identification of the modern trends in defect prevention.