Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

The severity of traffic accidents is a serious global concern,particularly in developing nations.Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents.There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and Facebook.Although such approaches are popular,there exists an issue of data management and low prediction accuracy.This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social media.Initially,a Tweet dataset has been formed by using an exhaustive keyword-based searching strategy.In the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule mining.The output of this phase has been forwarded to a three-layer deep learning model for final prediction.Numerous experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM model.It has been shown that the proposed model achieved 94.2%prediction accuracy.

Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

Role of Fuzzy Approach towards Fault Detection for Distributed Components

Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment.Among other communication,teamwork,and coordination problems in global software development,the detection of faults is seen as the key challenge.Thus,there is a need to ensure the reliability of component-based applications requirements.Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations.In this study,we propose an approach for fault detection from componentbased systems requirements using the fuzzy logic approach and historical information during acceptance testing.This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing.For the evaluation,we used empirical study,and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing.The comparison to the conventional procedures,i.e.,requirement criteria,and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures.Consequently,the F-measures of the proposed approach define the accurate selection of components,and faults identification increases in components using the proposed approach were higher(i.e.,more than 80 percent)than requirement criteria,and code coverage criteria procedures(i.e.,less than 80 percent),respectively.Similarly,the rate of fault detection in the proposed approach increases,i.e.,92.80 compared to existing methods i.e.,less than 80 percent.The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers.

Systematic Review of Web Application Security Vulnerabilities Detection Methods

In recent years, web security has been viewed in the context of securing the web application layer from attacks by unauthorized users. The vulnerabilities existing in the web application layer have been attributed either to using an inappropriate software development model to guide the development process, or the use of a software development model that does not consider security as a key factor. Therefore, this systematic literature review is conducted to investigate the various security vulnerabilities used to secure the web application layer, the security approaches or techniques used in the process, the stages in the software development in which the approaches or techniques are emphasized, and the tools and mechanisms used to detect vulnerabilities. The study extracted 519 publications from respectable scientific sources, i.e. the IEEE Computer Society, ACM Digital Library, Science Direct, Springer Link. After detailed review process, only 56 key primary studies were considered for this review based on defined inclusion and exclusion criteria. From the review, it appears that no one software is referred to as a standard or preferred software product for web application development. In our SLR, we have performed a deep analysis on web application security vulnerabilities detection methods which help us to identify the scope of SLR for comprehensively investigation in the future research. Further in this SLR considering OWASP Top 10 web application vulnerabilities discovered in 2012, we will attempt to categories the accessible vulnerabilities. OWASP is major source to construct and validate web security processes and standards.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.

Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide.This problem is addressed by the software development industry by software product line(SPL)practices that employ feature models.However,optimal feature selection based on user requirements is a challenging task.Thus,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited resources.In this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and clients.The proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and preferences.The system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management repository.Similarly,a list of features is suggested to the client according to their needs and preferences.The experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and clients.It provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction level.The results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system failure.Therefore,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated costs.Thus,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the modern era of technology.

Requirement Change Management in Global Software Environment Using Cloud Computing

The successful implementation of any software project depends upon the requirements. Change in requirements at any stage during the life cycle of software development is taken as a healthy process. However, making out this change in a co-located environment is somewhat easier than the distributed environment where stakeholders are scattered at more than one location. This raises many challenges?i.e.?coordination, communication & control, managing change effectively and efficiently and managing central repository. Thus, cloud computing can be applied to minimize these challenges among the stakeholders. We have used a case study to evaluate the framework using cloud computing.

An Improved Framework for Requirement Change Management in Global Software Development

In this research, an improved framework for requirement change management in global software development (RCM_GSD) has been presented. The objective is to manage the change in requirement specifically in global software development in an appropriate manner. The proposed frame-work RCM_GSD follows the required processes of RCM and reduces the concerns of GSD. Systematic Literature Review (SLR) was conducted for exploration of relevant research. During literature study, it is analyzed that the existing techniques of change management were not suitable for global software development (GSD). The change in requirements becomes more complicated in distributed environment due to the lack of communication and collaboration among globally dispersed stakeholders. The proposed model is compared with other models proposed in recent literature and analysis is made between them;feedback was obtained from the domain experts as well. The feedback and comparison results show that the proposed model provides an appropriate solution for requirement change management in GSD.

Towards Knowledge Management in RE Practices to Support Software Development

Requirement engineering in any software development is the most important phase to ensure the success or failure of software. Knowledge modeling and management are helping tools to learn the software organizations. The traditional Requirements engineering practices are based upon the interaction of stakeholders which causes iteratively changes in requirements and difficulties in communication and understanding problem domain etc. So, to resolve such issues we use knowledge based techniques to support the RE practices as well as software development process. Our technique is based on two prospective, theoretical and practical implementations. In this paper, we described the need of knowledge management in software engineering and then proposed a model based on knowledge management to support the software development process. To verify our results, we used controlled experiment approach. We have implemented our model, and verify results by using and without using proposed knowledge based RE process. Our resultant proposed model can save the overall cost and time of requirement engineering process as well as software development.

An Integration of UML Sequence Diagram with Formal Specification Methods—A Formal Solution Based on Z

UML Diagrams are considered as a main component in requirement engineering process and these become an industry standard in many organizations. UML diagrams are useful to show an interaction, behavior and structure of the system. Similarly, in requirement engineering, formal specification methods are also being used in crucial systems where precise information is required. It is necessary to integrate System Models with such formal methods to overcome the requirements errors i.e. contradiction, ambiguities, vagueness, incompleteness and mixed values of abstraction. Our objective is to integrate the Formal Specification Language (Z) with UML Sequence diagram, as sequence diagram is an interaction diagram which shows the interaction and proper sequence of components (Methods, procedures etc.) of the system. In this paper, we focus on components of UML Sequence diagram and then implement these components in formal specification language Z. And the results of this research papers are complete integrated components of Sequence diagram with Z schemas, which are verified by using tools and model based testing technique of Formal Specifications. Results can be more improved by integrating remaining components of Sequence and other UML diagrams into Formal Specification Language.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Towards Aspect Based Components Integration Framework for Cyber-Physical System

Cyber-Physical Systems(CPS)comprise interactive computation,networking,and physical processes.The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world.Smart systems,such as smart health care systems,smart homes,smart transportation,and smart cities,are made up of complex and dynamic CPS.The components integration development approach should be based on the divide and conquer theory.This way multiple interactive components can reduce the development complexity inCPS.As reusability enhances efficiency and consistency in CPS,encapsulation of component functionalities and a well-designed user interface is vital for the better end-user’s Quality of Experience(QoE).Thus,incorrect interaction of interfaces in the cyber-physical system causes system failures.Usually,interface failures occur due to false,and ambiguous requirements analysis and specification.Therefore,to resolve this issue semantic analysis is required for different stakeholders’viewpoint analysis during requirement specification and components analysis.This work proposes a framework to improve the CPS component integration process,starting from requirement specification to prioritization of components for configurable.For semantic analysis and assessing the reusability of specifications,the framework uses text mining and case-based reasoning techniques.The framework has been tested experimentally,and the results show a significant reduction in ambiguity,redundancy,and irrelevancy,as well as increasing accuracy of interface interactions,component selection,and higher user satisfaction.

Renin--angiotensin system inhibitor is associated with the reduced risk of all-cause mortality in COVID-19 among patients with/without hypertension

Consecutively hospitalized patients with confirmed coronavirus disease 2019(COVID-19)in Wuhan,China were retrospectively enrolled from January 2020 to March 2020 to investigate the association between the use of renin–angiotensin system inhibitor(RAS-I)and the outcome of this disease.Associations between the use of RAS-I(angiotensin-converting enzyme inhibitor(ACEI)or angiotensin receptor blocker(ARB)),ACEI,and ARB and in-hospital mortality were analyzed using multivariate Cox proportional hazards regression models in overall and subgroup of hypertension status.A total of 2771 patients with COVID-19 were included,with moderate and severe cases accounting for 45.0%and 36.5%,respectively.A total of 195(7.0%)patients died.RAS-I(hazard ratio(HR)=0.499,95%confidence interval(CI)0.325–0.767)and ARB(HR=0.410,95%CI 0.240–0.700)use was associated with a reduced risk of all-cause mortality among patients with COVID-19.For patients with hypertension,RAS-I and ARB applications were also associated with a reduced risk of mortality with HR of 0.352(95%CI 0.162–0.764)and 0.279(95%CI 0.115–0.677),respectively.RAS-I exhibited protective effects on the survival outcome of COVID-19.ARB use was associated with a reduced risk of all-cause mortality among patients with COVID-19.

A New Framework for Scholarship Predictor Using a Machine Learning Approach

Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.