Abstract: Cyber-Physical Production Systems (CPPSs) are large-scale industrial systems in which hardware and software are deeply intertwined. CPPS software has to be highly variable to support frequently changing customer and hardware requirements. Managing the overall variability of such large-scale industrial software is challenging. Knowledge from diverse engineering disciplines, e.g., mechatronics, process, electrical, and control software engineering, is required to support product configuration. These disciplines use diverse artifacts and tools. It is infeasible to have one common variability model or one common configuration user interface representing variability knowledge from diverse disciplines in CPPSs. Therefore, in this paper, we propose the idea of a dynamic product configuration user interface, which decouples the product configuration process from the user interface based on an existing multidisciplinary variability management approach for CPPSs. We describe the next steps toward implementing and evaluating our idea.

Abstract: —Modern software development incorporates various technologies, such as containerization, CI/CD pipelines, and build tools, which have to be jointly configured to enable building, testing, deployment, and execution of software systems. The vast configuration space spans several different configuration artifacts with their own syntax and semantics, encoding hundreds of configuration options and their values. The interplay of these technologies requires some level of coordination, which is realized by matching configurations. That is, configuration options and their according values may depend on other options and values from entirely different technologies and artifacts. This creates non-obvious configuration dependencies that are hard to track. The missing awareness and overview of such configuration dependencies across diverse configuration artifacts, tools, and frameworks can lead to dependency conflicts and severe configuration errors. We propose C FG N ET , a framework that models the configuration landscape of a software project as a configuration network in an extensible and artifact-independent way. This way, we enable the early detection of possible dependency violations and proactively prevent misconfigurations during software development and maintenance. In a literature study, we found that the most common form of dependencies is the equality of values of different options. Based on this result, we developed an equality-based linker to determine dependent options across different artifacts. To demonstrate the extensibility of our framework, we also implemented nine plugins for popular technologies, such as Maven and Docker. To evaluate our approach, we injected and violated five real-world configuration dependencies extracted from Stack Overflow, which we support with our technology plugins, in five subject systems. C FG N ET found all injected dependency violations and four additional ones already present in these systems. Moreover, we applied C FG N ET to the commit history of 50 repositories selected from GitHub and found dependency conflicts in about two thirds of these repositories. We manually inspected 883 conflicts, with about 89% true positives, demonstrating the need to reliably track cross-technology configuration

Abstract: Configuration management plays a crucial role in software engineering, ensuring the effective management of software artifacts throughout the development lifecycle. This article explores the significance of configuration management, its key principles, and the various techniques and tools employed in the field. By maintaining version control, facilitating collaboration, and ensuring traceability, configuration management enables teams to achieve greater software quality, reliability, and maintainability. This abstract provides a concise overview of configuration management in software engineering, highlighting its importance and offering insights into its implementation and benefits.

Abstract: Efficient Configuration Tuning for Big-Data Software Systems via Configuration Space Reduction (Supplementary Material)

Abstract: Modern software systems are designed to be deployed in different configured environments (e.g., permissions, virtual resources, network connections), and adapted at run-time to different situations (e.g., memory limits, enabling/disabling features, database credentials). Such a configuration during the deployment and run-time of a software system is implemented via a set of configuration files, which together constitute what we refer to as a “configuration system”. Recent research efforts investigated the evolution and maintenance of configuration files. However, they merely focused on a limited part of the configuration system (e.g., specific infrastructure configuration files or Dockerfiles), and their results do not generalize to the whole configuration system. To cope with such a limitation, we aim by this paper to better capture and understand what files constitute a configuration system. To do so, we leverage an open Card Sort technique to qualitatively study 1,756 configuration files from OpenStack, a large and widely studied open-source software ecosystem. Our investigation reveals the existence of nine types of configuration files, which cover the creation of the infrastructure on top of which OpenStack will be deployed, along with other types of configuration files used to customize OpenStack after its deployment. These configuration files are interconnected while being used at different deployment stages. For instance, we observe specific configuration files used during the deployment stage to create other configuration files that are used in the run-time stage. We also observe that identifying and classifying these types of files is not straightforward, as five out of the nine types can be written in similar programming languages (e.g., python and bash) as regular source code files. We also found that the same file extensions (e.g., Yaml) can be used for different configuration types, making it difficult to identify and classify configuration files. Thus, we first leverage a machine learning model to identify configuration from non-configuration files, which achieved a median AUC of 0.91, a median Brier score of 0.12, a median precision of 0.86, and a median recall of 0.83. Thereafter, we leverage a multi-class classification model to classify configuration files based on the nine configuration types. Our multi-class classification model achieved a median weighted AUC of 0.92, a median Brier score of 0.04, a median weighted precision of 0.84, and a median weighted recall of 0.82. Our analysis also shows that with only 100 labeled configuration and non-configuration files, our model reached a median AUC higher than 0.69. Furthermore, our configuration model requires a minimum of 100 configuration files to reach a median weighted AUC higher than 0.75.

Abstract: <p><i>Configuration management plays a crucial role in software engineering, ensuring the effective management of software artifacts throughout the development lifecycle. This article explores the significance of configuration management, its key principles, and the various techniques and tools employed in the field. By maintaining version control, facilitating collaboration, and ensuring traceability, configuration management enables teams to achieve greater software quality, reliability, and maintainability. This abstract provides a concise overview of configuration management in software engineering, highlighting its importance and offering insights into its implementation and benefits.Configuration management plays a crucial role in software engineering, ensuring the effective management of software artifacts throughout the development lifecycle. This article explores the significance of configuration management, its key principles, and the various techniques and tools employed in the field. By maintaining version control, facilitating collaboration, and ensuring traceability, configuration management enables teams to achieve greater software quality, reliability, and maintainability. This abstract provides a concise overview of configuration management in software engineering, highlighting its importance and offering insights into its implementation and benefits.</i></p>

Abstract: Software configuration management (SCM) is an important activity in the software engineering life cycle. SCM by control of the evolution process of products leads to constancy and stability in software systems. Nowadays, use of software configuration management is essential during the process of software development as rules to control and manage the evolution of software systems. SCM effects different levels of abstraction included the architectural level. Configuration of software architecture causes improvement in the configuration of the lower abstraction levels. CM of software architecture is more significant in large scale software with longevity of life cycle. Traditional SCM approaches, at the architectural level, do not provided the necessary support to software configuration management, so systems that use these approaches are faced with problems. These problems arise because of the lack of a serious constant and repeated changes in the software process. To overcome this it is necessary to create an infrastructure. Hence, a service oriented approach for configuration management is presented in this paper. In this approach, the activities of configuration management are conducted from a service oriented viewpoint. This approach was also used to try and control the evolution and number of versions of different software systems in order to identify, organize and control change and reforms during the production process. This approach can compose services and create composite services for new undefined activities of configuration.