Analysis of deployment dependencies in software components

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.

Automotive applications can be described as distributed embedded software which perform real-time computation on top of a heterogeneous hardware platform. One key phase in designing distributed software systems is software deployment. Therein it is decided how software components are deployed over the hardware platform, and how the communication between software components is performed. These decisions significantly determine the system performance. This chapter tackles the software deployment problem, tailored to the needs of the automotive domain. Thereby, the focus is on two issues: the configuration of the communication infrastructure and how to handle design constraints. It is shown, how state-of-the-art approaches have to be extended in order to tackle these issues, and how the overall process can be performed efficiently, by utilizing search methodologies.

LHC experiments are depending on a rich palette of software components to build their specific applications. These underlying software components include the ROOT analysis framework, the Geant4 simulation toolkit, monte carlo generators, grid middle-ware, graphics libraries, scripting languages, databases, tools, etc. which are provided centrally in up to date versions on multiple platforms (Linux, Mac, Windows). Until recently this set of packages has been tested and released in a tree like structure as a consistent set of versions across operating systems, architectures and compilers for LHC experiments only. Because of the tree like deployment these releases were only usable in connection with a configuration management tool which provided the proper build and run-time environments and was hindering other parties outside LHC from easily using this palette of packages.In a new approach the releases will be grouped in "flat structure" such that interested parties can start using it without configuration management, retaining all the above mentioned advantages. In addition to an increased usability the software shall also be distributed via system provided package deployment systems (rpm, apt, etc.). The approach of software deployment is following the ideas of providing a wide range of HEP specific software packages and tools in a coherent, up to date and modular way on multiple platforms. The target audience for such software deployments are individual developers or smaller development groups / experiments who don't have the resources to maintain this kind of infrastructure. This new software deployment strategy has already been successfully implemented for groups at CERN.

OTS selection plays a crucial role in the deployment of software systems. One of its main current problems is how to deal with the vast amount of unstructured, incomplete, evolvable and widespread information that highly increases the risks of taking a wrong decision. The goal of our research is to tackle these information quality problems for facilitating the collection, storage, retrieval, analysis and reuse of OTS related information. An essential issue in this endeavor is to assess what OTS selectors mean by Information Quality and their needs to perform an informed selection. Therefore, we are putting forward an on-line survey to get empirical data supporting our approach.

Large-scale reuse of software architectures and components is an important element for improving software productivity and reducing the time needed to design, develop, and deploy software systems. For maximum benefit, the application of reuse technologies must be coupled with effective process changes. In this paper, we focus on AT&T's approach to software reuse, particularly the use of the AT&T BaseWorX™ applications platform to achieve reuse. We also discuss the process changes associated with software reuse, and the results that have been achieved from applying reuse to a wide range of software products.

Configuring and deploying a large software system is complicated when the system is composed of components and when there are numerous possible configurations for the system. In such a scenario, it is difficult for end users to specify and install an appropriate configuration for their specific environment. Defining all valid configurations of a software system is challenging, and can be addressed through a concise specification that can generate all of the possible configurations. The Deployable Software Description (DSD) part of the University of Colorado Software Dock project, is one such specification format. But using the DSD runs the risk that the set of generated configurations includes some that are invalid with respect to constraints defined independently of the DSD. The paper describes a framework to support the analysis of DSD specifications to help developers detect potentially invalid configurations. This analysis assumes that the system components are annotated with properties, and an analysis tool is provided that takes a specific configuration and analyzes it for conflicts with respect to some set of constraints. Using the DSD, we can enumerate and analyze configurations to verify their validity. The results can be used to modify the DSD to avoid future generation of invalid configurations.

Cloud computing recent emanation has drastically diversified everyone's perception of software delivery, infrastructure architectures, application development and deployment models. It is a new touchstone preceding the enterprise application development that can effectively expedite and facilitate the execution of various workflows in business process management system. Workflow is an automation of a business entity that can manage the business processes efficiently satisfying the requirements of prevailing enterprises. Dynamic workflows in a cloud computing environment empower the utilization of various cloud services to facilitate workflow execution. Scheduling multiple workflows in cloud environment is always a challenge. Workflow scheduling is a critical issue in the management of workflow execution. In this paper, we have compared and analyzed the performance of different heuristic workflow scheduling algorithms with various QoS parameters and scheduling factors.

: The purpose of IDA Memorandum Report M-552, Software Technology Development and Deployment Plan for the DOD Technology Base, is to document the findings of three workshops sponsored by the Institute for Defense Analyses (IDA). The participants were asked to place primary focus on actions that could enhance DOD's ability to deploy software intensive weapon systems, to capitalize on the findings, conclusions, and recommendations of previous DOD software studies, and to draw from their own personal experiences. The IDA study took a different approach than the previous studies by addressing those common user problems directly related to the deployment of operational software intensive weapon systems. The results were five key findings and recommendations for the DDDRE(R&AT) that address specific technology base efforts as well as DOD policy/ management infrastructure changes that should be considered for immediate action.

The computing infrastructure on which engineers develop and deploy software has evolved significantly in recent years. The rapid growth of cloud computing services mean that infrastructure and platform components are becoming more decentralized (owned by others, often far away from the development or operating organization) and more elastic (with the ability to provision and de-provision them at will). Infrastructure-as-a-Service (IaaS) capabilities provide the raw resources needed to deploy software—computing, storage, and networking. Platform-as-a-Service (PaaS) offerings provide important software components as commodity services—databases, identity and access management, security, analytics, various kinds of middleware, and much more. New virtualization and packaging techniques for software that can take advantage of cloud computing, such as containers, provide new opportunities for rapid and automated testing, deployment, and scaling of software systems. This enables new software delivery models, such as continuous deployment of new software to production environments and frequent, transparent A/B testing of new features. These changes are having an impact on software development environments, as well, with more development tasks and workflow steps moving to the cloud. This chapter will briefly explore these technologies and their relationships to one another, and explore their impacts on the practice of software engineering.

Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic systems in Internet of Robotic Things (IoRT) environments. Our solution integrates Model-Driven Engineering (MDE) with containerization technologies to improve scalability, reproducibility, and maintainability. A dedicated metamodel introduces high-level abstractions for describing deployment architectures, repositories, and container configurations. A web-based tool enables collaborative model editing, while an external deployment automator generates validated Docker and Compose artifacts to support seamless multi-host orchestration. We validated the approach through real-world experiments, which show that the method effectively automates deployment workflows, ensures consistency across development and production environments, and significantly reduces configuration effort. These results demonstrate that model-driven automation can bridge the gap between Software Engineering (SE) and robotics, enabling Software-Defined Robotics (SDR) and supporting scalable IoRT applications.

The Linux operating system has become the platform of choice in the HEP community. However, the migration process from another operating system to Linux can be a tremendous effort for developers and system administrators. The ultimate goal of such a transition is to maximize agreement between the nal results of identical calculations on the different platforms. Apart from the ne tuning of the existing software, the following issues need to be resolved: choice of Linux distribution, development tools (compiler, debugger, prolers etc.), compatibility with third-party software, and deployment strategy. It would be ideal to develop, run and test software using ofce desktops, local farm systems, or personal laptops regardless of the Linux distribution choosen. To accomplish this task, you need to have a e xible package management system that is able to install/upgrade/verify/uninstall the necessary software components without particular knowledge of remote system conguration and user privileges. We discuss how Linux became the third ofcial computing platform of the CLEO collaboration, outlining the details of the transition from Tru64 and Solaris operating systems to Linux, and the software model and deployment strategy employed.

Preface. 1. Introduction. 1.1 What is COP? 1.2 Why is COP important? 1.3 What is a component? 1.4 Hardware components and software components. 1.5 From OOP to COP. 1.6 Component-based software engineering. 1.7 Summary. 1.8 Self-review questions. 1.9 Exercises. 2. A Theory of Components. 2.1 Principles of COP. 2.2 Infrastructures of COP. 2.3 Component models. 2.4 Connection models. 2.5 Deployment models. 2.6 Unifying component technologies. 2.7 Summary. 2.8 Self-review questions. 2.9 Exercises. 3. COP with JavaBeans. 3.1 Overview of JavaBeans technology. 3.2 The component model of JavaBeans. 3.3 The connection model of JavaBeans. 3.4 The deployment model of JavaBeans. 3.5 Examples and lab practice. 3.6 Summary. 3.7 Self-review questions. 3.8 Exercises. 4. Enterprise JavaBeans Components. 4.1 The EJB a rchitecture. 4.2 The component model of EJB. 4.3 The connection model of EJB. 4.4 The deployment model of EJB. 4.5 Examples and lab practice. 4.6 Summary. 4.7 Self-review questions. 4.8 Exercises. 4.9 Programming exercises. 5. CORBA Components. 5.1 The COBRA component infrastructure. 5.2 The CORBA component model (CCM). 5.3 The connection model of CORBA and CCM. 5.4 The deployment model of CORBA and CCM. 5.5 Examples and lab practice. 5.6 Summary. 5.7 Self-review questions. 5.8 Exercises. 5.9 Programming exercises. 6. .NET Components. 6.1 The .NET framework. 6.2 The component model of .NET. 6.3 The connection model of .NET. 6.4 .NET component deployments. 6.5 Visual Studio .NET. 6.6 Examples and lab practice. 6.7 Summary. 6.8 Self-review questions. 6.9 Exercises. 6.10 Programming exercises. 7. COP with OSGi Components. 7.1 Overview of OSGi technology. 7.2 The component model of OSGi. 7.3 The connection model of OSGi. 7.4 The deployment model of OSGi. 7.5 Examples and lab practice. 7.6 Summary. 7.7 Self-review questions. 7.8 Exercises. 8. Web Services Components. 8.1 The Web Services framework. 8.2 The component model of Web Services. 8.3 The connection model of Web Services. 8.4 Web Services component deployment. 8.5 Examples and lab practice. 8.6 Summary. 8.7 Self-review questions. 8.8 Exercises. 8.9 Programming exercises. Appendix.

The 7th International Workshop on Economics-Driven Software Engineering Research (EDSER-7) continues to be the leading forum for the discussion of emerging research ideas in software economics. The focus of the workshop is on the use of economic models for reasoning about technical issues and decisions in the definition, design, development, deployment, and evolution of software and software-intensive systems.

From mid-2004 onwards, the major nuclear reaction databases (EXFOR, CINDA and ENDF) and services (Web and CD-ROMs retrieval systems and specialized applications) have been functioning within a modern computing environment as multiplatform software, working under several operating systems with relational databases. Subsequent work at the IAEA has focused on three areas of development: revision and extension of the contents of the databases; extension and improvement of the functionality and integrity of the retrieval systems; development of software for database maintenance and system deployment.

Minimizing the computing infrastructure (such as processors) in a distributed real-time embedded (DRE) system deployment helps reduce system size, weight, power consumption, and cost. To support software components and applications on the computing infrastructure, the hardware must provide enough processors to ensure that all applications can be scheduled without missing real-time deadlines. In addition to ensuring scheduling constraints, sufficient resources (such as memory) must be available to the software. It is hard to identify the best way(s) of deploying software components on hardware processors to minimize computing infrastructure and meet complex DRE constraints. This paper provides the following contributions to the study of the deployment of software components to hardware in DRE systems: (1) we present an algorithmic deployment technique that minimizes the required number of processors, while adhering to real-time scheduling, resource, and co-location constraints, (2) we show how this technique can be augmented with a harmonic period heuristic to further reduce the number of required processors, and (3) we present empirical data from applying three different deployment algorithms for processor minimization to a flight avionics DRE system.