Researching Cooperation and Communication in Continuous Software Engineering

Continuous Software Engineering (CSE) - -continuous development and deployment of software - -and DevOps - -the close cooperation or integration of operations and software development - -is about to change how software is developed. Together with the tighter integration of development and operations also with usage this will change coordination and collaboration both between IT professionals and between developers and users. In this short paper, we discuss the CHASE dimension of three core research themes that begin to crystallize in literature. This position paper is intended as a 'call to arms' for the CHASE community to study CSE

A Taxonomy of Software Delivery Performance Profiles: Investigating the Effects of DevOps Practices

This research develops a taxonomy of Software Delivery Performance Profiles for DevOps development settings. We base the underlying Software Delivery Performance measure on the application of the Economic Order Quantity (EOQ) model to software development. Consistent with the objectives of both, development and operations departments, the measure includes attributes for throughput (release frequency and lead-time to delivery) and for stability (mean time to restore). Using a sample of 7,522 DevOps professionals globally, we conduct a hierarchical cluster analysis and find that the throughput and stability measures move in tandem and form three distinct Software Delivery Performance Profiles. Further analysis will show how the use of individual DevOps practices impacts Performance Profiles of development settings. When completed, the study will support the utility of DevOps and the effectiveness of individual DevOps practices

Continuous Deployment Transitions at Scale

Predictable, rapid, and data-driven feature rollout; lightning-fast; and automated fix deployment are some of the benefits most large software organizations worldwide are striving for. In the process, they are transitioning toward the use of continuous deployment practices. Continuous deployment enables companies to make hundreds or thousands of software changes to live computing infrastructure every day while maintaining service to millions of customers. Such ultra-fast changes create a new reality in software development. Over the past four years, the Continuous Deployment Summit, hosted at Facebook, Netflix, Google, and Twitter has been held. Representatives from companies like Cisco, Facebook, Google, IBM, Microsoft, Netflix, and Twitter have shared the triumphs and struggles of their transition to continuous deployment practices—each year the companies press on, getting ever faster. In this chapter, the authors share the common strategies and practices used by continuous deployment pioneers and adopted by newcomers as they transition and use continuous deployment practices at scale

Configuration Smells in Continuous Delivery Pipelines: A Linter and a Six-Month Study on GitLab

An effective and efficient application of Continuous Integration (CI) and Delivery (CD) requires software projects to follow certain principles and good practices. Configuring such a CI/CD pipeline is challenging and error-prone. Therefore, automated linters have been proposed to detect errors in the pipeline. While existing linters identify syntactic errors, detect security vulnerabilities or misuse of the features provided by build servers, they do not support developers that want to prevent common misconfigurations of a CD pipeline that potentially violate CD principles (“CD smells”). To this end, we propose CD-Linter, a semantic linter that can automatically identify four different smells in pipeline configuration files. We have evaluated our approach through a large-scale and long-term study that consists of (i) monitoring 145 issues (opened in as many open-source projects) over a period of 6 months, (ii) manually validating the detection precision and recall on a representative sample of issues, and (iii) assessing the magnitude of the observed smells on 5,312 open-source projects on GitLab. Our results show that CD smells are accepted and fixed by most of the developers and our linter achieves a precision of 87% and a recall of 94%. Those smells can be frequently observed in the wild, as 31% of projects with long configurations are affected by at least one smell