The use of agile methods for software development has grown to a large extent in the last few years. These methods ensure the quick delivery of software products with minimal cost and user satisfaction. Though these techniques were initially developed for small developmental teams, certain challenges have been observed when these methods are applied on large scale. However, we have conducted a systematic literature review (SLR) for the identification of motivators for adopting agile methods on a large scale from a management perspective. Thus, we have identified a total of 21 motivators for adopting agile methods on a large scale from a management perspective. Among these motivators, some were marked as critical motivators depending on variables, e.g., the factors critical in one variable might not be critical in another variable. The factors which were recorded as critical in all variables are strong executive support, agile development environment training and learning, agile development expertise, team competency, and briefing of top management on agile. Furthermore, we also found that the impact of different motivators was different depending on time and place for project manager guidance, i.e., some motivators were most critical in one region while less critical in another. Similarly, some of the motivators were more critical in previous decades but less critical in recent decades because of different improvements in software processes and technologies. These motivators are also analyzed from different angles, i.e., decade-wise and region wise for project managers guidance. The motivators are extracted from a sample of 58 research papers identified via an SLR process. Finally, we have analyzed the identified motivators based on various variables, such as continents and digital libraries. INDEX TERMS Large-scale agile, agile software development, systematic literature review, adopting agile methodology, success factors. I. INTRODUCTION Agile methods were meant for practice in single or small development teams and projects [1]. However, due to its usefulness, these methods can be applied in Large-Scale Agile Development (LSAD) teams and projects as well. Adopting Agile methods in larger projects and teams [2], is difficult as compared to smaller ones-which is the first choice-larger ones will need more coordination. LSAD teams The associate editor coordinating the review of this manuscript and approving it for publication was Bora Onat.

A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.

Software Outsourcing Partnership (SOP) is considered as a kind of risk and reward sharing client‐vendor relationship. Generally, a fruitful outsourcing association might be converted to an outsourcing partnership. The objective of this research is to identify and analyse barriers that are hurdles to vendors in renewing or promoting their ongoing client‐vendor relationship to outsourcing partnership. A questionnaire survey based on the findings of Systematic Literature Review (SLR) was performed with 50 experts. The study identifies five critical barriers such as “insufficient quality of technical capability,” “poor infrastructure,” “poor quality of service,” “communication gap and poor coordination,” and “relational risk.” The results indicate that barriers' insufficient quality of technical capability, poor infrastructure, and poor quality of service were common in four types of experts while insufficient quality of technical capability is common in three levels of experts. Furthermore, barriers were classified based on their criticality from client‐vendor perspective. The results of Spearman correlation test (rs = 0.714 and ρ = 0.000) confirmed that the participant strongly agrees with the outcomes of the SLR. The results suggest that for successful renewal or promotion of their existing outsourcing association, vendor organizations should address all the identified barriers in general and the most common barriers in particular.

To reduce costs and improve organizational efficiency, the adoption of innovative services such as Cloud services is the current trend in today’s highly competitive global business venture. The aim of the study is to guide the software development organization (SDO) for Cloud-based testing (CBT) adoption. To achieve the aim, this study first explores the determinants and predictors of Cloud adoption for software testing. Grounded on the collected data, this study designs a technology acceptance model using fuzzy multicriteria decision-making (FMCDM) approach. For the stated model development, this study identifies a list of predictors (main criteria) and factors (subcriteria) using systematic literature review (SLR). In the results of SLR, this study identifies seventy subcriteria also known as influential factors (IFs) from a sample of 136 papers. To provide a concise understanding of the facts, this study classifies the identified factors into ten predictors. To verify the SLR results and to rank the factors and predictors, an empirical survey was conducted with ninety-five experts from twenty different countries. The application value in the industrial field and academic achievement of the present study is the development of a general framework incorporating fuzzy set theory for improving MCDM models. The model can be applied to predict organizational Cloud adoption possibility taking various IFs and predictors as assessment criteria. The developed model can be divided into two main parts, ranking and rating. To measure the success or failure contribution of the individual IFs towards successful CBT adoption, the ranking part of the model will be used, while for a complete organizational assessment in order to identify the weak area for possible improvements, the assessment part of the model will be used. Collectively, it can be used as a decision support system to gauge SDO readiness towards successful CBT.

Software Outsourcing Partnership (SOP) is a type of cooperative client-vendor relationship. SOP is an emerging strategy and is different from ordinary software development outsourcing (SDO). Usually, a fruitful outsourcing association might be converted to an outsourcing partnership. Conversely, SOP is not a risk-free business, numerous barriers associated with SOP. The overarching target of this exploratory paper is to find and analyze a list of barriers that are considered obstacles for vendors in the conversion of their surviving contractual outsourcing relationship to a partnership. Firstly, twenty-six barriers to SOP formation were identified through systematic literature review (SLR) from a sample of 106 papers and then an empirical survey was conducted with fifty experts to analyze the significance and applicability of these barriers in the SOP context. The identified barriers were further analyzed based on five variables such as decades, company size, continents, location of analysis, and perspective of the study. Ten barriers were considered as critical barriers (CBs) via SLR. Industrial experts indicate they extremely agree with five CBs. Eight CBs were equally reported on all continents. We found ten CBs common in all types of organizations. Further, twelve CBs were shared in both decades while ten CBs were found common in both academia and industry. Furthermore, four CBs were specific to clients; five were specific to vendors while ten were common to both. The association of various barriers with SOP formation is found statistically significant for twenty-five barriers with effect size (0.41 < Ø < 0.90, p < 0.05). Stakeholders in SOP should address all the listed barriers especially the critical ones to attain a partner position. INDEX TERMS Systematic literature review, empirical survey, software outsourcing partnership, client-vendor relationship.