DevOps is a widely adopted practice to consistently develop and upgrade a system that is already in use. Between software development and operations, DevOps presupposes cross-functional cooperation and automation. The adoption and execution of DevOps in businesses are complicated since it necessitates adjustments to organizational, technical, and cultural factors. The implementation of DevOps in practice is thoroughly described in this systemic literature review (SLR). The study focuses on the identification of the manager's challenges in the DevOps environment and also intends to find the mitigation practices. In this article, SLR has been performed to identify the manager's challenges and the state-of-the-art mitigation strategies. This study identifies twenty challenges from the manager's perspective and the applied mitigation strategies to overcome the challenges. The findings of the current work would be beneficial in comprehending the DevOps idea, methods, and perceived impacts, particularly among managers while adopting DevOps in the organization.

DevOps is a software development approach that emphasize collaboration, communication and integration between development and operation teams to improve the speed and efficiency of software delivery. DevOps aims to automate and streamline the software development and deployment process. Nevertheless, when a software organization adopts DevOps, several challenges on infrastructure management, limited agility, scalability, increased cost, inconsistent environment, and security risks are faced. A solution is to adopt DevOps and Cloud together, but the integration requires advice because implementing new approaches for development and operations at the same time is also a challenge. The aim of this study is to identify and categorize success factors that positively influence the adoption of DevOps and Cloud in software organization and propose an integrated framework for factors of both dimensions. A systematic literature review (SLR) was conducted to collect the primary studies related to both fields for analysis. After the SLR, 40 success factors related to DevOps and Cloud are collected. These identified factors are further categorized into Technical, Organizational, and Social & Culture areas. The proposed framework can help practitioners and researchers to concentrate on the crucial areas that are essential for the successful adoption of DevOps and Cloud.

This is an Innovative Practice Full Paper. Our department has three computing programs seeking accreditations from ABET CAC and EAC. With new ABET criteria, we adopted performance indicators (PIs) for our two levels of outcomes, devised to map to the general and program specific criteria of the two commissions. The Blackboard is our university's mandated learning management system (LMS). In order to automatically create outcomes achievement reports for students in each major, we developed a new process and an ABET tool to integrate with Blackboard. The new process utilizes Blackboard to create PI rubrics and directly assess student work with rubrics. However, direct download of assessed rubrics data is not available in Blackboard, so that a third-party tool is used to export rubrics data. Achievement reports for students in a single major are desired for ABET outcomes and our two-level outcomes. Since student major information is not available in Blackboard, the ABET tool integrates data from Blackboard and another university proprietary system to distinguish students in the same major and creates a variety of short-term and long-term reports for our ABET continuous quality improvements. The introduced process and ABET tool is applicable to other departments using the Blackboard LMS.

This is an Innovative Practice Full Paper. This paper introduces a multi-program assessment system for a department offering multiple majors to seek accreditation from ABET CAC and EAC. The Canvas LMS is our mandated tool for faculty to set up course rubrics, assign grades, and communicate with students. However, it falls short on solving several of our data issues, including data retrieval, data separation, data sharing, data integration, and data security. The Canvas LMS provides no data download for individual rubric criteria of an instrument, hindering analysis to a more granular level. There is also no information to identify a student's major, making data separation for different academic programs difficult. Faculty are reluctant to share student data on Canvas for integration, worrying about a violation to FERPA. Moreover, data sharing and data integration can increase the risk of data security. Our assessment system is built atop of Canvas and is a service oriented web application for faculty to assess student performance and generate instrument reports. The reports reveal only computed statistics for data sharing, integration and security purposes. The provided web services can interact with another system to distinguish student majors and consolidate instruments whose rubric criteria cover the same performance indicators (PIs). The system is a safe and sound methodology to conquer our data problems and can facilitate both formative and summative analyses for the program outcomes evaluation.

We develop a moving average non-homogeneous Poisson process (MA NHPP) reliability model which includes the benefits of both time domain, and structure based approaches. This method overcomes the deficiency of existing NHPP techniques that fall short of addressing repair, and internal system structures simultaneously. Our solution adopts a MA approach to cover both methods, and is expected to improve reliability prediction. This paradigm allows software components to vary in nature, and can account for system structures due to its ability to integrate individual component reliabilities on an execution path. Component-level modeling supports sensitivity analysis to guide future upgrades, and updates. Moreover, the integration capability is a benefit for incremental software development, meaning only the affected portion needs to be re-evaluated instead of the entire package, facilitating software evolution to a higher extent than with other methods. Several experiments on different system scenarios and circumstances are discussed, indicating the usefulness of our approach.

We develop a user-oriented reliability model, a simple yet effective approach, to address huge amounts of Web contents, and more importantly tackle users' dynamic navigation behaviors for a Web system. The users' intrinsic capabilities and behaviors, unlike programs' common recurring behaviors, call for a new approach for modeling the reliability of an organization's Web system. Our approach differs in the modeling scope of the Web system and the means of modeling user behaviors. We exploit the idea of cyclomatic complexity to cover the maximum number of independent paths, which overcome the less likely repetitive user navigations, and still maintain the dynamic nature of Web surfing. The modeling process takes Web senders' access, error, and referrer information as inputs, and translates them into a flow graph. A transition tree is then derived from the flow graph, based on depth-first traversal and dynamic programming. Accordingly, the reliability of the Web system can be computed by excluding those unreliable paths. This model is further applied to prioritize the error removal sequence to assist in allocating resources and effort.

A number of Markov-based software reliability models have been developed for measuring software reliability. However, the application of these models is strictly limited to software that satisfies the Markov properties. The objective of our work is to expand the application domain of the Markov-based models, so that most software can be modeled and software reliability can be measured at the architecture level. To overcome the limitations of Markov properties, our model takes execution history into account and addresses both deterministic and probabilistic software behaviors. Each state represents the executions of one or more components depending on the architectural styles. In addition, the executions of one component are depicted by using distinctive states, when such executions are influenced by past states. Furthermore, we construct loops to eliminate the likelihood of unlimited state expansion and utilize a binomial tree structure to account for all the different execution paths. We show that Markov models are applicable even to software that does not fully satisfy the Markov properties. Therefore, we significantly improve the state of the art in architecture-based software reliability modeling.

Presents a case study that evaluates two software quality attributes: performance and availability. We use three programs based on two architectural styles: pipe-filter and batch-sequential. The objective of this study is to identify the crucial factors that might have an influence on these quality attributes from the software architecture perspective. The benefit of this study is that early quality prediction can be facilitated by an analysis of the software architecture. The results from this study show that it is feasible to select a better architectural style based on variations in the execution environment to attain higher availability and/or better performance. Moreover, we demonstrate the effects of these variations on the quality measurements.

We present an analytical model for estimating architecture-based software reliability, according to the reliability of each component, the operational profile, and the architecture of software. Our approach is based on Markov chain properties and architecture view to state view transformations to perform reliability analysis on heterogeneous software architectures. We demonstrate how this analytical model can be utilized to estimate the reliability of a heterogeneous architecture consisting of batch-sequential/pipeline, call-and-return, parallel/pipe-filters, and fault tolerance styles. In addition, we conduct an experiment on a system embedded with three architectural styles to validate this heterogeneous software reliability model.