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. INDEX TERMS DevOps data quality assessment, fuzzy TOPSIS, empirical investigation. SAIMA RAFI received the M.Sc. degree in computer science from the University of Agriculture Faisalabad, Faisalabad, Pakistan, and the M.S. degree in computer science from Government College University at Faisalabad, Faisalabad. She is currently pursuing the Ph.D. degree with the

DevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.

No abstract

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.

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.