DevOps is a combination of collaborative and multidisciplinary efforts of an organization to control continuous delivery and updates of new software while guaranteeing their reliability and correctness. In the software industry, the implementation of DevOps (development and operations units) faces many challenges that are specifically associated with the security. This study aims to develop a prioritization based taxonomy of DevOps security challenges using PROMETHEE-II approach. The total of eighteen DevOps security challenges were extracted from the literature and were further evaluated with experts using questionnaire survey study. In the third stage, multi criteria decision making PROMETHEE-II approach was used to prioritize and develop the taxonomy of identified factors and their categories. The implications of PROMETHEE-II approach are novel in this research domain as it has been used successfully in various other domains e.g. medical, banking, internet techniques and management etc. The contribution of this study is not limited to develop the taxonomy based structure of DevOps security challenges, but also the proper prioritization of these challenges by introducing PROMETHEE-II approach in the research field of DevOps. The study results will assist the practitioners to remove the uncertainty and vagueness in the opinion of DevOps experts to secure DevOps implementation for better and continuous software development process.

In current era, DevOps gain much interaction in software industry as it provides the flexible development environment. To meet the continuous development and operations, DevOps mainly focus, to integrate the data from heterogeneous source. While DevOps adoption, the quality assessment of data integrated from heterogeneous environment, is important and challenging at the same time. This study aims to identify the critical factors that could negatively impact the data quality assessment process in DevOps. We have used the systematic literature review (SLR) approach and identify a total of 13 critical challenging factors. The finding of SLR are further validated with industry experts via questionnaire survey. Finally, we have applied the Fuzzy TOPSIS approach to prioritize the investigated challenging factors with respect to their significance of DevOps data quality assessment process. The results show that analyzing data in real time, visualization of data and missing information and other invalid data are the highest ranked challenging factors which need to be addressed on priority basis, to successfully measure the quality of heterogeneous data in DevOps. We believe that the finding of this study will assist the practitioner to consider the most significant factors for measuring the quality of heterogeneous data in DevOps.

Testing is a complex phase in DevOps process due to need of an automated process that provides feedback at different strategies of continuous development and operations pipeline. Software organization face several challenges during the testing phase due to lack of understanding on testing best practices for the DevOps paradigm. The objective of this study is to prioritize DevOps best testing practices, which can facilitate the selection of testing practices during DevOps process. To perform this research, we have extended the work done by Hornbeek, using the 15 DevOps testing practices discussed in his study. First, we categorize the test practices against culture, automation, lean, measurement, and sharing (CALMS) pillars of DevOps adoption principles. Next, a questionnaire‐based survey was conducted to collect feedback from industry practitioners on the DevOps test practices and their categorization against CALMS criteria. Finally, we applied Interpretive Structure Modeling (ISM) to find the interrelationship between CALMS criteria, and fuzzy TOPSIS was used to prioritize the DevOps test practices that will assist practitioners to better manage the testing activities during DevOps process.

The quantum computing (QC) field is rapidly moving beyond the realm of pure science to become a commercially viable technology that may be able to overcome the drawbacks of traditional computing. Major technology tycoons have spent in building coding frameworks and hardware to create applications specifically designed for quantum computing over the last few years. The development of QC hardware is accelerating, however, the requirement for software-intensive methodology, approaches, procedures, instruments, roles and responsibilities for creating industrial-focused quantum software applications arises from operationalizing the QC. This paper outlines the concept of quantum software engineering (QSE) life cycle, which entails the engineering of quantum requirements, design, implementation, testing and maintenance of quantum software. This paper notably advocates for collaborative efforts between the industrial community and software engineering research to propose practical solutions to support the complete activities for the development of quantum software. The proposed vision makes it easier for researchers and practitioners to suggest new procedures, reference designs, cutting-edge equipment, and methods for utilizing quantum computers and creating the newest and most advanced quantum software.