Software Deployment Research Articles

Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy’s and St Thomas’ Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King’s College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists’ decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.

On Exploiting and Implementing Collaborative Virtual and Augmented Reality in a Cloud Continuum Scenario

This work explores the application of collaborative virtual and augmented reality in a cloud continuum context, focusing on designing, implementing, and verifying three reference architectures for five collaborative VR/AR software deployment. The architectures designed differ in their distribution of computational load: one handles everything in the cloud, one balances the load between the cloud and the edge, and the last concentrates the load entirely on the edge. The design of the architectures was initially outlined through sequence and component diagrams and then implemented using the most appropriate technologies and frameworks. For each architecture, a specific application was developed and deployed on the various components of that architecture to test its proper functioning. Finally, the scenarios were simulated to be stressed with a significant number of users, employing tools such as Cloud Analyst to analyze performance and present well-defined and implemented reference architectures.

Accelerating Time-to-Market in Life Insurance: The Power of AI-Driven Test Automation Frameworks

The Life Insurance sector grapples with distinct challenges, balancing multifaceted operational workflows, rigorous compliance standards, and the surging demand for swift software deployment. Conventional Quality Assurance (QA) paradigms often fall short in delivering the responsiveness required by today's digital transformation efforts. This paper examines how Artificial Intelligence (AI)-enhanced test automation frameworks can substantially truncate time-to-market while ensuring software excellence. By leveraging advanced predictive analytics, strategic test case prioritization, and integrated Continuous Integration/Continuous Delivery (CI/CD) processes, Life Insurance enterprises can accelerate software launch cycles with improved dependability, thereby meeting the ever-evolving expectations of the marketplace and regulatory entities.

DESIGN AND FABRICATION OF SMART ENVIRO GUARDIAN

This project tackles the challenge of maintaining a comfortable and healthy indoor environment in the face of unpredictable weather conditions. The primary objective is to develop an automated weather monitoring system integrated with a humidifier, leveraging Internet of Things (IoT) technology and a suite of sensors including the DHT11 for temperature and humidity, a rain sensor, an LDR sensor, and a BMP180 barometric pressure sensor. The system aims to monitor real-time weather data and adjust indoor conditions accordingly, enhancing comfort and reducing health risks such as respiratory issues, allergies, and mold growth. Additionally, it provides remote monitoring and real-time alerts through IoT connectivity, improving user convenience and energy efficiency. The methodology includes detailed requirement analysis, hardware and software development, testing, and deployment. This project promises to significantly improve indoor environmental quality, especially in regions with erratic weather patterns, by providing a robust, adaptable solution. Keywords: Automated Weather Monitoring, Climate Control, IoT Technology, Humidifier, DHT11 Sensor, Rain Sensor, LDR Sensor, Energy Efficiency

CI/CD model for optimizing software deployment in SMEs

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.

Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department.

Objectives: To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. Materials and Methods: A retrospective study was conducted in two phases-without AIS (Period 1: 1 January 2020-30 June 2020) and with AIS (Period 2: 1 January 2021-30 June 2021). Results: Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (-1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 min (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38). Conclusions: AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. Key Point: After AI software deployment, the rate of radiographic discrepancies decreased by 17% (p = 0.04) but this was not clinically relevant (-2%, p = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (p = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

ObjectivesTo identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as...

Assessing the transformative impact of cloud computing on software deployment and management

Cloud computing has fundamentally transformed the landscape of software deployment and management, offering significant benefits and reshaping traditional approaches. This review explores the transformative impact of cloud computing on these domains, highlighting key changes and advantages. Firstly, cloud computing has revolutionized software deployment by introducing scalable and flexible infrastructure solutions. Unlike traditional onpremises systems that require significant upfront investment and ongoing maintenance, cloud platforms offer ondemand resources and payasyougo models. This shift enables organizations to deploy software rapidly, adapt to changing needs, and scale resources efficiently without the constraints of physical hardware. Additionally, cloud computing enhances software management through centralized control and automation. Cloud environments provide integrated management tools that streamline the deployment, monitoring, and maintenance of applications. These tools facilitate automated updates, patch management, and system backups, reducing the burden on IT teams and minimizing downtime. Furthermore, cloudbased management systems offer realtime visibility and analytics, allowing for proactive performance monitoring and troubleshooting. The collaborative nature of cloud computing also fosters improved development and deployment practices. Cloud platforms support DevOps methodologies by enabling continuous integration and continuous delivery (CI/CD) pipelines. This integration accelerates software development cycles, enhances collaboration among distributed teams, and ensures consistent and reliable deployments. Moreover, the cloud's global reach and accessibility break down geographical barriers, allowing organizations to deploy software across multiple regions effortlessly. This geographic flexibility enhances the user experience and ensures high availability and performance, regardless of the user's location. Despite these advancements, the transition to cloud computing presents challenges, including data security and compliance concerns. Organizations must implement robust security measures and adhere to regulatory requirements to protect sensitive information and maintain trust. In conclusion, cloud computing has had a profound impact on software deployment and management, offering scalable, flexible, and efficient solutions. Its transformative effects include streamlined operations, improved collaboration, and global accessibility. As cloud technology continues to evolve, organizations must navigate associated challenges while leveraging its benefits to drive innovation and efficiency in software management. Keywords: Management, Impact, Cloud Computing, Software Deployment, Assessing

Navigating ethical considerations in software development and deployment in technological giants

Navigating ethical considerations in software development and deployment in technological giants

On the Impact of Input Models on the Fault Detection Capabilities of Combinatorial Testing

Testing is an important activity to detect faults before software deployment. We focus on black-box combinatorial testing, where fault detection is one of the main objectives. In this paper, we argue that input model abstraction notably impacts the fault detection capability of a combinatorial test suite. First, we present experiments from previous work that support this argument. We then perform new experiments on a more diverse set of programs. These experiments use mutation testing to estimate fault detection capability, but we also include structural coverage measures in the new experiments. Finally, we elaborate on two possible improvements to obtain an optimal input abstraction strategy for not just continuous but all input domains. Both experiments suggest that input abstraction affects the fault detection capability. We claim that the improvements will produce a better input abstraction with which we can achieve better fault detection capability without increasing the test suite size.

NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design.

Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple "button-clicking" functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of "white-box" genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists.

A new dataset for video-based cow behavior recognition

A new video based multi behavior dataset for cows, CBVD-5, is introduced in this paper. The dataset includes five cow behaviors: standing, lying down, foraging,rumination and drinking. The dataset comprises 107 cows from the entire barn, maintaining an 80% stocking density. Monitoring occurred over 96 h for these 20-month-old cows, considering varying light conditions and nighttime data to ensure standardization and inclusivity.The dataset consists of ranch monitoring footage collected by seven cameras, including 687 video segment samples and 206,100 image samples, covering five daily behaviors of cows. The data collection process entailed the deployment of cameras, hard drives, software, and servers for storage. Data annotation was conducted using the VIA web tool, leveraging the video expertise of pertinent professionals. The annotation coordinates and category labels of each individual cow in the image, as well as the generated configuration file, are also saved in the dataset. With this dataset,we propose a slowfast cow multi behavior recognition model based on video sequences as the baseline evaluation model. The experimental results show that the model can effectively learn corresponding category labels from the behavior type data of the dataset, with an error rate of 21.28% on the test set. In addition to cow behavior recognition, the dataset can also be used for cow target detection, and so on.The CBVD-5 dataset significantly influences dairy cow behavior recognition, advancing research, enriching data resources, standardizing datasets, enhancing dairy cow health and welfare monitoring, and fostering agricultural intelligence development. Additionally, it serves educational and training needs, supporting research and practical applications in related fields. The dataset will be made freely available to researchers world-wide.

Migration aspects from monolith to distributed systems using software code build and deployment time and latency perspective

Migration aspects from monolith to distributed systems using software code build and deployment time and latency perspective

InvesTEE: A TEE-supported Framework for Lawful Remote Forensic Investigations

Remote forensic investigations, i.e., the covert lawful infiltration of computing devices, are a generic method to acquire evidence in the presence of strong defensive security. A precondition for such investigations is the ability to execute software with sufficient privileges on target devices. The standard way to achieve such remote access is by exploiting yet unpatched software vulnerabilities. This in turn puts other users at risk, resulting in a dilemma for state authorities that aim to protect the general public (by patching such vulnerabilities) and those that need remote access in criminal investigations. As a partial solution, we present a framework that enables privileged remote forensic access without using privileged exploits. The idea is to separate the remote forensic software into two parts: a Forensic Software, designed by law enforcement agencies to execute investigative actions, and a (privileged) Control Software, provided by the device vendor to selectively grant privileges to the Forensic Software based on a court warrant within the rules of criminal procedure. By leveraging trusted execution environments for running the Control Software in a tamper-proof manner, we enable trustful deployment and operation of remote forensic software. We provide a proof-of-concept implementation of InvesTEE that is based on ARMv8-A TrustZone.

DOML: A new modeling approach to Infrastructure-as-Code

One of the main DevOps practices is the automation of resource provisioning and deployment of complex software. This automation is enabled by the explicit definition of Infrastructure-as-Code (IaC), i.e., a set of scripts, often written in different modeling languages, which defines the infrastructure to be provisioned and applications to be deployed.We introduce the DevOps Modeling Language (DOML), a new Cloud modeling language for infrastructure deployments. DOML is a modeling approach that can be mapped into multiple IaC languages, addressing infrastructure provisioning, application deployment and configuration.The idea behind DOML is to use a single modeling paradigm which can help to reduce the need of deep technical expertise in using different specialized IaC languages.We present the DOML’s principles and discuss the related work on IaC languages. Furthermore, the advantages of the DOML for the end-user are demonstrated in comparison with some state-of-the-art IaC languages such as Ansible, Terraform, and Cloudify, and an evaluation of its effectiveness through several examples and a case study is provided.

SPACE TECHNOLOGIES AND DEVOPS: INTEGRATING POSITIONING AND TIMING SYSTEMS FOR RELIABLE SOFTWARE DEPLOYMENT IN AEROSPACE APPLICATIONS

The specific topic of investigation in this paper is the integration of PNT technologies into the DevOps paradigm for the aerospace domain with an emphasis on improved robustness and accuracy of software releases. The paper's goal is to show how PNT systems, especially GNSS and machine learning applied to time-series data, can solve several critical issues faced in software deployment in aerospace, whether using simulation reports and real-world case study examples, areas and ways in which the PNT-enabled DevOps pipeline may be employed may be highlighted. The fact that the timing and positioning investigations improve the accuracy of the deployment also reduces synchronization errors, as evidenced by the research. Through the preceding technologies, aerospace organizations will be able to elevate the reliability of software installations, hence positively contributing to the operational and safety aspects of aerospace and space missions. This integration indicates a significant advancement in what must be done to handle DevOps processes for the critical requirements of aerospace applications.

Empowering DevOps with Infrastructure as Code :Trends, Tools and Techniques

DevOps has arisen as a pillar of modern software engineering, emphasising the integration of development and operations to ensure effective product delivery. Infrastructure as Code (IaC) is an important DevOps technique that involves defining and managing infrastructure requirements using code, enabling for automated provisioning and maintenance. This technique enhances traceability, reuse, and consistency across development and production environments. The introduction of microservices architectures has increased project teams’ infras- tructure responsibilities, making IaC essential for delivering reliable and efficient deployments. IaC enables developers to describe infrastructure in code, simplifying the deployment process. In both large and small businesses, IaC is essen- tial for supporting efficient DevOps processes.The most recent breakthroughs, tools, and techniques in IaC demonstrate a revolutionary impact on software development and deployment workflows. As more businesses adopt cloud-native designs and containerisation technologies, the requirement for automated infrastructure provisioning grows, leading in the growth of IaC tools and methodologies. Organisations that combine IaC with continuous integration and delivery (CI/CD) pipelines can re- duce time-to-market and improve operational efficiency. IaC not only automates infrastructure management, but it also includes software engineering principles like version control and testing into infrastructure provisioning, which improves consistency and reliability. This democratisation of infrastructure management encourages increased collaboration across cross-functional teams, hence improving accountability and innovation. Implementing IaC is therefore crucial for achieving agility, scalability, and resilience in the digital age. Index Terms—Iac, DevOps, CI/CD, Automation, Cloud-native, Infrastructure Provisioning ,Terraform, Ansible, AWS CloudFor- mation

MakeSBML: a tool for converting between Antimony and SBML.

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

The Effects of Global Market Changes on Automotive Manufacturing and Embedded Software

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is the management of production facilities by integrating software production lines, continuous integration, and continuous delivery/continuous deployment (CI/CD). All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as has the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research examines numerous datasets from the United States of America and Washington State, based on which we estimate possible future changes in the automotive industry’s sales.

Unlocking Agile Software Deployment: The Power of Canary Deployments

Unlocking Agile Software Deployment: The Power of Canary Deployments