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.

Context: Software Engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of software fault prediction metrics.

This paper emphasizes the role of contextual information and legal rules in publishing services, formulating contracts, discovering services, and their impact on ranking and adaptability. We use Configured Service concept, which is a package that bundles together service functionality, service contract, and service provision context. Service providers only publish Configured Services in a service registry. Service requesters query the registry to discover available services that can match their requirements. Often there is a semantic gap between the service query and the services in the registry. To deal with this, we discuss three query types. The discovery processes, employing different matching processes that are appropriate for the query types, will rank the services in order to enable the requester choose the most relevant service(s). Ranking is also essential when the number of matching's is large. We identify the different situations that call for rediscovery and re-ranking of service queries. We include a brief account of formalism, within which all these activities are precisely described.