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The trend toward global software development (GSD) has grown tremendously in recent years because of the rapid acceleration in information and communication technologies (ICTs). The reason for the changing trend toward GSD is to develop high‐quality software with minimum cost and time with round the clock. Despite the benefits gained from GSD, there are certain challenges associated with it. Literature reveals that software integration is an integral challenge in the GSD domain and vendors face numerous difficulties in integrating the software components to build the final product. The main aim of this research study is to develop a software integration model (SIM) that will assist GSD vendors to address the factors linked with various stages of software integration. This model is basically designed for GSD vendor organizations to assess and improve their software integration‐related activities. However, it is also beneficial for GSD client organizations in getting knowledge about the status of the GSD vendor organization's software integration capabilities. The research design is composed of well‐known research strategies including systematic literature review (SLR), questionnaire survey, and case study approach. This research study yielded the SIM. We identified nine critical success factors (CSFs) and 10 critical barriers (CBs) in software integration through SLR‐1 by extracting the data from a sample of 105 papers. Similarly, we also identified a total of 132 practices/solutions, for the identified CSFs and CBs in software integration, through SLR‐2 from a sample of 40 papers. These identified factors and practices were validated from 96 global software industry experts/practitioners through online survey. The identified CSFs, CBs, and their associated practices have been arranged adequately across the six software integration levels of SIM. We have adapted SIM structure from capability maturity model integration (CMMI), implementation maturity model (IMM), and software outsourcing vendors readiness model (SOVRM). We also conducted six case studies in the software industry for SIM applicability and validity. The participants' feedback from the six case studies in GSD vendor's organizations portray that the SIM is beneficial for GSD vendors to assess their software integration capability. [ABSTRACT FROM AUTHOR]

Cloud computing is characterized as a shared computing and communication infrastructure. It encourages the efficient and effective developmental processes that are carried out in various organizations. Cloud computing offers both possibilities and solutions of problems for outsourcing and management of software developmental operations across distinct geography. Cloud computing is adopted by organizations and application developers for developing quality software. The cloud has the significant impact on utilizing the artificial complexity required in developing and designing quality software. Software developmental organization prefers cloud computing for outsourcing tasks because of its available and scalable nature. Cloud computing is the ideal choice utilized for development modern software as they have provided a completely new way of developing real‐time cost‐effective, efficient, and quality software. Tenants (providers, developers, and consumers) are provided with platforms, software services, and infrastructure based on pay per use phenomenon. Cloud‐based software services are becoming increasingly popular, as observed by their widespread use. Cloud computing approach has drawn the interest of researchers and business because of its ability to provide a flexible and resourceful platform for development and deployment. To determine a cohesive understanding of the analyzed problems and solutions to improve the quality of software, the existing literature resources on cloud‐based software development should be analyzed and synthesized systematically. Keyword strings were formulated for analyzing relevant research articles from journals, book chapters, and conference papers. The research articles published in (2011–2021) various scientific databases were extracted and analyzed for retrieval of relevant research articles. A total of 97 research publications are examined in this SLR and are evaluated to be appropriate studies in explaining and discussing the proposed topic. The major emphasis of the presented systematic literature review (SLR) is to identify the participating entities of cloud‐based software development, challenges associated with adopting cloud for software developmental processes, and its significance to software industries and developers. This SLR will assist organizations, designers, and developers to develop and deploy user‐friendly, efficient, effective, and real time software applications. [ABSTRACT FROM AUTHOR]

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions. [ABSTRACT FROM AUTHOR]