Software Deployment Research Articles

Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts.

To develop deep learning-based networks for the diagnosis of diabetic retinopathy (DR) with cataracts based on infrared fundus images. Shanghai General Hospital, Shanghai Eye Disease Prevention & Treatment Center, Shanghai, China. Development and evaluation of an artificial intelligence (AI) diagnostic method. A total of 10 665 infrared fundus images from 4553 patients with diabetes were used to train and test the model. For image quality assessment, left and right eye classification, DR diagnosis and grading, and segmentation of 3 DR lesions, an end-to-end software using EfficientNet and UNet was developed. The accuracy and performance of the software in comparison to human experts was evaluated. The model achieved an accuracy of 75.31% for left and right eye classification, 100% for DR grading and diagnosis tasks, and 73.67% for internal test set, with corresponding areas under the curve (AUCs) of 0.88, 1.00, and 0.89, respectively. For DR lesion segmentation, the AUCs of hemorrhagic, microangioma, and exudative lesions were 0.86, 0.66, and 0.84, respectively. In addition, a contrast test of human-machine film reading confirmed the software's high sensitivity (96.3%) and specificity (90.0%) and consistency with the manual film reading group (κ = 0.869, P < .001). This easily deployable software generated reports quickly and promoted efficient DR screening with cataracts in clinical and community settings. AI-assisted software can perform automatic analysis of infrared fundus images and has substantial application value for the diagnosis of DR patients with cataracts.

Information technologies of application architecture`s IT services

The development of the System Architecture of the IT infrastructure focused on the usage of modern Data Centers is considered. The purpose of the research is to develop a strategy for the development of the System Architecture of the IT infrastructure based on the usage of advanced methodologies and concepts of leading manufacturer’s hardware and software. The following principle of IT infrastructure construction is formulated: IT infrastructure architectures define a set of services. IT services are provided to three groups of clients. IT services and clients are connected by 5 implementation scenarios. The integration of IT services is determined by 5 architectures. As IT services, we understand information technologies aimed at maintaining the following elements in technically good condition: network devices, computing equipment, data storage devices, automatic software deployment services, network services, perimeter protection services, directory services, file and print services, data management services, business application services, IT management services, archiving and recovery services, certificate management services, integration services. Architectures define the fundamental principles of building IT services and their relationship. One of the most important architectures is the architecture of software applications. The architecture of software applications is determined by the business needs of the corporation and the approaches, methods of creating applications by a specific developer. It defines the execution environment for applications, mechanisms of communication between applications and components, tools for controlling the application and managing its state, as well as storage for structured and unstructured data types. Tasks of the architecture: to provide an environment for the execution of application components; support dispatching mechanisms for communications between application components; implement tools for monitoring the level of services and diagnosing problems; provide storage of structured and unstructured information. Standardization of the application architecture allows you to minimize costs associated with the support of several types of architectures, each for a specific type of business application.

355. DEVELOPMENT OF A CUSTOM RELATIONAL DATABASE FOR INTEGRATING CLINICAL AND RESEARCH DATA FOR THE STUDY FOR ESOPHAGEAL ADENOCARCINOMA

Abstract Background With the advent of next-generation sequencing, digital pathology, and other high-dimensional data sources used in modern research, improved data management is required to maintain organization and relationship to the corresponding patient and their outcomes. The complexity of the data often limits interpretation by clinicians not highly versed in its analysis. Conversely, data scientists may not be able to interpret the data in the correct clinical context. We describe here a relational database and custom tools which coordinates clinical, organoid, and genomic data in the study of esophageal adenocarcinoma. Methods Database architecture was designed with pillars of clinical data and outcome, organoid culture data, and next-generation sequencing data. Data dictionaries were composed for all tables in the database architecture. Oracle Database was utilized and populated with datasets formatted to adhere to data dictionaries. Application Express was used to develop web applications for users to enter, query, and analyze data. Automatic nightly backups, robust login security, and tiers of access protect confidential data against system failures, human error, and data theft. Custom tools with graphical user interfaces were developed for survival curve generation and organoid drug-screening results, to facilitate clinician use. Results This database now encompasses over 21 data tables comprising clinical, surgical, recurrence, treatment response, tissue sample storage, organoid research, and genomic data. Data scientists can link omic data directly to patient clinical data and clinician researchers can rapidly obtain patient and organoid survival data for covariates of choice using a graphical user interface. Conclusion Proper data management requires a substantial initial investment in designing of database architecture, deployment of software and servers, formatting of data, and the creation of front-end user applications. The investment is well worth the cost, however, as a centralized database allows for rapid querying of data that would otherwise be scattered or require individualized analysis for each project. This also facilitates the interactions between data scientists and clinician researchers.

MT-SOTA: A Merkle-Tree-Based Approach for Secure Software Updates over the Air in Automotive Systems

The automotive industry has seen a dynamic transformation from traditional hardware-defined to software-defined architecture enabling higher levels of autonomy and connectivity, better safety and security features, as well as new in-vehicle experiences and richer functions through software and ongoing updates of both functional and safety-critical features. Service-oriented architecture plays a pivotal role in realizing software-defined vehicles and fostering new business models for OEMs. Such architecture evolution demands new development paradigms to address the increasing complexity of software. This is crucial to guarantee seamless software development, integration, and deployment—all the way from cloud or backend repositories to the vehicle. Additionally, it calls for enhanced collaboration between car manufacturers and suppliers. Simultaneously, it introduces challenges associated with the necessity for ongoing updates and support ensuring vehicles remain safe and up to date. Current approaches to software updates have primarily been implemented for traditional vehicle architectures, which mostly comprise specialized electronic control units (ECUs) designed for specific functions. These ECUs are programmed with a single comprehensive executable that is then flashed onto the ECU all at once. Different approaches should be considered for new software-based vehicle architectures and specifically for ECUs with multiple independent software packages. These packages should be updated independently and selectively for each ECU. Thus, we propose a new scheme for software updates based on a Merkle tree approach to cope with the complexity of the new software architecture while addressing safety and security requirements of real-time and resource-constrained embedded systems in the vehicle. The Merkle-tree-based software updates over the air (MT-SOTA) proposal enables secure updates for individual software clusters. These clusters are developed and integrated by diverse entities with varying release timelines. Our study demonstrates that the MT-SOTA scheme can enhance the speed of software update execution without significantly increasing the process overhead. Additionally, it offers necessary defense against potential cyberthreats. The results of the performed technical analysis and experiments of the MT-SOTA implementation are presented in this paper.

Azure DevOps The Next Era of Application Lifecycle Management

The software development, testing, and deployment process is a series of actions that are carried out in order to achieve a particular result. highly manual process, resulting in a significant number of code errors and bugs. Development and operations (Support) teams can often be out of sync, slowing software delivery and disappointing business stakeholders. DevOps creates efficiency across all tasks involved in software development, deployment, and maintenance. Connecting development with operations leads to increased visibility, more accurate requirements, improved communication, and faster time to Industry. Azure DevOps supports a joint culture and set of multiple steps and processes that bring together the development team, managers, and contributors to build software quickly. It allows the software industry to improve software at a faster pace than they can with traditional software development approaches. Compared with ADO Server, ADO Services offers the following added benefits: • Easy for server management. • Fastest access to greatest features. • Improved faster connectivity for remote users. • A transition from capital expenditures (like servers) to operational expenditures (subscriptions). DevOps is becoming a software industry standard for multiple businesses. According to a report issued by Capgemini, 61% of businesses either adopted DevOps or planned to do so during 2018. Statistics like this demonstrate that DevOps is a necessary part of your business plan if you expect to respond quickly to the market's demands, improve your business’s time-to-market, and keep your software solutions updated regularly. DevOps creates efficiency across all tasks involved in software development, deployment, and maintenance. Connecting development with operations leads to increased visibility, more accurate requirements, improved communication, and faster time to Industry.

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.

Unlocking the Power of AI/ML in DevSecOps: Strategies and Best Practices

In today's rapidly evolving technological landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices has emerged as a critical enabler for enhancing security, efficiency, and innovation in software development and deployment processes. This paper explores the strategies and best practices for harnessing the full potential of AI/ML within the DevSecOps framework. Beginning with an overview of DevSecOps principles and the role of AI/ML, the paper delves into specific strategies such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Furthermore, it examines key challenges and considerations associated with implementing AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through case studies and real-world examples, the paper illustrates how organizations can leverage AI/ML technologies to optimize their DevSecOps pipelines, mitigate security risks, and foster a culture of continuous improvement. By embracing these strategies and best practices, organizations can unlock the full potential of AI/ML to drive innovation, resilience, and agility in their DevSecOps initiatives.

Review on Software Technology

Abstract: Software technology is the term employed to describe the assortment of instruments, approaches, and procedures used in the creation, upkeep, and deployment of software systems. It covers a wide range of topics, including testing methods, software engineering principles, databases, frameworks, libraries, and programming languages. Programming languages are one of the major components of software technology. Different programming languages provide varying degrees of functionality and abstraction, enabling programmers to construct code for certain goals. In addition to many others, popular programming languages include Java, Python, C++, and JavaScript. These languages are always changing to accommodate brand-new features and improvements as well as adapt to shifting requirements

Achieving Workplace Equality With Cloud HR Software: Best Practices For Implementing A Successful Diversity and Inclusion Programs

The pursuit of Diversity and Inclusion (D&amp;I) in organizational contexts has burgeoned into a pivotal business strategy, albeit with varying degrees of success. A myriad of D&amp;I initiatives across industries has been met with challenges, manifesting in suboptimal outcomes and, in certain instances, outright failure. This paper seeks to explore the potential factors contributing to the lackluster performance of numerous D&amp;I programs and endeavors to identify prospective avenues to circumvent these issues. Addressing workplace equality stands paramount in the evolution of a thriving, inclusive, and innovative organizational culture. The role of Human Resources (HR) is pivotal in orchestrating an environment that not only embraces diversity but also fosters an ethos of inclusivity and equality. Amidst the modern technological landscape, Cloud HR Software surfaces as a groundbreaking tool that amalgamates the virtues of cloud computing with HR functionalities, offering scalable, accessible, and robust solutions to mitigate disparities and anchor equal opportunities across all tiers of an organization. This paper elucidates the imperative of achieving workplace equality and how leveraging Cloud HR Software can potentially streamline this pursuit through comprehensive data analytics, real-time feedback, and developing equitable talent management strategies. I delve deep into various use-cases and empirically backed scenarios wherein organizations have successfully harnessed Cloud HR solutions to not only comprehend but also rectify structural and cultural inequalities, thereby propelling themselves toward a more egalitarian working environment. The exploration aims to shed light on practical strategies, inherent challenges, and ethical aspects interwoven with the deployment of Cloud HR Software while maintaining a keen eye on ensuring that technological advancements and organizational equality progress hand in hand. This white paper delves into the multifaceted challenges that permeate the implementation of D&amp;I programs and elucidates pathways towards fruitful execution. Central to these challenges are superficial commitment, misaligned strategies, resistance to change, and the failure to intertwine inclusivity within organizational fabric, often culminating in suboptimal outcomes and disintegrated team dynamics. Through an exploratory lens, this paper concurrently sheds light on the critical success factors that underpin triumphant D&amp;I initiatives.

A Practical Study on Automatizing the Software Deployment Process

Yazılım dağıtımı, geliştirilen bir uygulamanın çalıştırılacak dijital ortamlara yüklenmesidir ve yazılım geliştirme yaşam döngüsünde önemli bir aşamadır. Herhangi bir hataya veya kesintiye yol açmayacak şekilde titizlikle uygulanmalıdır. Küçük organizasyon ve sistemlerde bu sürecin işleyişinin manuel olarak işletilebilmesi mümkündür. Ancak orta ve büyük organizasyonlarda dağıtım yapılacak ortamların çeşitliliği, büyüklüğü, karmaşıklığı buna engeldir. Ayrıca iş sürekliliği, güvenlik ve uyum gibi gereksinimler dikkate alındığında manuel dağıtımdan kaynaklı birçok risk söz konusudur. Bu tür risk ve sorunların aşımında otomatik yazılım dağıtım araçlarına ihtiyaç vardır. Bu çalışmada yazılım yaşam ve dağıtım süreci, manuel dağıtımdan kaynaklı sorunlar, sorunların çözümünde kullanılan otomatik dağıtım araçları incelenmiştir. Yazılım dağıtım sürecinin otomatikleştirilmesi uygulamalı olarak çalışılmıştır. Bu çalışma daha önce yapılan çalışmalardan farklı olarak, konusu ve uygulamalı bir sunum olması sebebiyle ilgili kurum çalışanları, öğrenci ve araştırmacılar için teknik bir rehber niteliğindedir. Yazılım dağıtım süreci ve bu sürecin otomatikleştirilmesine yönelik uygulamalı çalışma literatüre kazandırılmıştır.

AI-driven container security approaches for 5G and beyond: A survey

The rising use of microservice-based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers, as well as the container environment itself, are critical for infrastructure in cloud settings and 5G. To address security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule-based approaches and machine learning that can respond to novel threats. In this study, we survey the container security literature focusing on approaches that leverage machine learning to address security challenges.

Flexible and scalable ML-based diagnosis module for optical networks: a security use case [Invited

To support the pervasive digital evolution, optical network infrastructures must be able to quickly and effectively adapt to changes arising from traffic dynamicity or external factors such as faults and attacks. Network automation is crucial for enabling dynamic, scalable, resource-efficient, and trustworthy network operations. Novel telemetry solutions enable optical network management systems to obtain fine-grained monitoring data from devices and channels as the first step toward the near-real-time diagnosis of anomalies such as security threats and soft failures. However, the collection of large amounts of data creates a scalability challenge related to processing the data within the desired monitoring cycle regardless of the number of optical services being analyzed. This paper proposes a module that leverages the cloud native software deployment approach to achieve near-real-time machine learning (ML)-assisted diagnosis of optical channels. The results obtained over an emulated physical-layer security scenario demonstrate that the architecture successfully scales the necessary components according to the computational load and consistently achieves the desired monitoring cycle duration over a varying number of monitored optical channels.

SAP BusinessObjects in Medical Informatics.

Electronic health record (EHR) generates a large amount of data filled with opportunities to enhance documentation compliance, quality improvement, and other metrics. Various software tools exist, but many clinicians are unaware of them. Our institution switched from a hybrid of paper and multiple small EHRs to one all-inclusive EHR system. We faced significant challenges beyond the typical new software deployment phase that affected our departmental regulatory compliance, quality measures, and research initiatives. We aimed to navigate these issues through the use of medical informatics. We used a multidimensional database software analysis tool called SAP BusinessObjects® (SAP SE. Released 2020. SAP BusinessObjects, Version 14.2.8.3671. Waldorf, Germany) to design automated queries for the patient database to generate various reports for our department. As a result, We improved our anesthesia documentation non-compliance from 13-17% of all cases to 4% within months. We have also used this tool to automatically generate various reports such as preoperative beta-blocker administrations, caseloads, case complications, procedure logs, and medication records. Even today many departments rely on manual checks for even the most basic documentation and quality metric compliance, which can be time consuming and costly. Using medical informatics tools is a highly efficient alternative. Fortunately, many software tools exist within most modern EHR packages, and most people can learn to use these tools productively.

Containerization and its Architectures: A Study

Containerization is a technique for lightweight virtualization of programs in cloud computing, which leads to the widespread use of cloud computing. It has a positive impact on both the development and deployment of software. Containers can be divided into two groups based on their setup. The Application Container and the System Container are two types of containers. A container is a user-space that is contained within another container, while a system container is a user-space that is contained within another container. This study compares and contrasts several container architectures and their organization in micro-hosting environments for containers.

Toward an Analysis Method of Software Evolution Triggered by Place Change in Mobile Computing

Software dynamic evolution is a hot topic in software engineering. Traditional evolution is caused by the change in user requirements and running environments. However, in mobile computing systems, there is a new problem that place change leads to the change in software configuration, deployment, and function, which is called the problem of software evolution triggered by place change. Facing this new problem, traditional evolution methods fail to formally describe their evolution process and analyze their evolution performance. Therefore, a new method needs to be developed to ensure the reliability of software evolution triggered by place change. We first address various situations regarding the change of the software function caused by place change in physical space, which are described by a Bigraph model. Then, the triggering conditions for software evolution are defined, and a reaction system corresponding to software evolution is discussed. A theory about software function change caused by place change is developed, including a novel algorithm for identifying software evolution rules that involve place information and the control process for the whole software evolution (CPSE) in a mobile computing environment. Finally, some real case studies illustrate the effectiveness and correctness of our proposed method. Extensive experiments show that our algorithm CPSE outperforms Quiescence and version consistency (VC)-concurrent versions (CV) algorithms in timeliness and average disruption.

Security Misconfigurations in Open Source Kubernetes Manifests: An Empirical Study

Context: Kubernetes has emerged as the de-facto tool for automated container orchestration. Business and government organizations are increasingly adopting Kubernetes for automated software deployments. Kubernetes is being used to provision applications in a wide range of domains, such as time series forecasting, edge computing, and high-performance computing. Due to such a pervasive presence, Kubernetes-related security misconfigurations can cause large-scale security breaches. Thus, a systematic analysis of security misconfigurations in Kubernetes manifests, i.e., configuration files used for Kubernetes, can help practitioners secure their Kubernetes clusters. Objective: The goal of this paper is to help practitioners secure their Kubernetes clusters by identifying security misconfigurations that occur in Kubernetes manifests . Methodology: We conduct an empirical study with 2,039 Kubernetes manifests mined from 92 open-source software repositories to systematically characterize security misconfigurations in Kubernetes manifests. We also construct a static analysis tool called Security Linter for Kubernetes Manifests ( SLI-KUBE ) to quantify the frequency of the identified security misconfigurations. Results: In all, we identify 11 categories of security misconfigurations, such as absent resource limit, absent securityContext , and activation of hostIPC . Specifically, we identify 1,051 security misconfigurations in 2,039 manifests. We also observe the identified security misconfigurations affect entities that perform mesh-related load balancing, as well as provision pods and stateful applications. Furthermore, practitioners agreed to fix 60% of 10 misconfigurations reported by us. Conclusion: Our empirical study shows Kubernetes manifests to include security misconfigurations, which necessitates security-focused code reviews and application of static analysis when Kubernetes manifests are developed.

DeltaDroid : Dynamic Delivery Testing in Android

Android is a highly fragmented platform with a diverse set of devices and users. To support the deployment of apps in such a heterogeneous setting, Android has introduced dynamic delivery —a new model of software deployment in which optional, device- or user-specific functionalities of an app, called Dynamic Feature Modules (DFMs) , can be installed, as needed, after the app’s initial installation. This model of app deployment, however, has exacerbated the challenges of properly testing Android apps. In this article, we first describe the results of an extensive study in which we formalized a defect model representing the various conditions under which DFM installations may fail. We then present DeltaDroid —a tool aimed at assisting the developers with validating dynamic delivery behavior in their apps by augmenting their existing test suite. Our experimental evaluation using real-world apps corroborates DeltaDroid ’s ability to detect many crashes and unexpected behaviors that the existing automated testing tools cannot reveal.

Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study

Continuous Integration and Delivery (CI/CD) practices have shown several benefits for software development and operations, such as faster release cycles and early discovery of defects. For Cyber-Physical System (CPS) development, CI/CD can help achieving required goals, such as high dependability, yet it may be challenging to apply. This article empirically investigates challenges, barriers, and their mitigation occurring when applying CI/CD practices to develop CPSs in 10 organizations working in eight different domains. The study has been conducted through semi-structured interviews, by applying an open card sorting procedure together with a member-checking survey within the same organizations, and by validating the results through a further survey involving 55 professional developers. The study reveals several peculiarities in the application of CI/CD to CPSs. These include the need for (i) combining continuous and periodic builds while balancing the use of Hardware-in-the-Loop and simulators, (ii) coping with difficulties in software deployment (iii) accounting for simulators and Hardware-in-the-Loop differing in their behavior, and (vi) combining hardware/software expertise in the development team. Our findings open the road toward recommenders aimed at supporting the setting and evolution of CI/CD pipelines, as well as university curricula requiring interdisciplinarity, such as knowledge about hardware, software, and their interplay.

Tanana River Test Site Model Verification Using the Marine and Hydrokinetic Toolkit (MHKiT)

The marine energy (ME) industry historically lacked a standardized data processing toolkit for common tasks such as data ingestion, quality control, and visualization. The marine and hydrokinetic toolkit (MHKiT) solved this issue by providing a public software deployment (open-source and free) toolkit for the ME industry to store and maintain commonly used functionality for wave, tidal, and river energy. This paper demonstrates an initial model verification study in MHKiT. Using Delft3D, a numerical model of the Tanana River Test Site (TRTS) at Nenana, Alaska was created. Field data from the site was collected using an Acoustic Doppler Current Profiler (ADCP) at the proposed Current Energy Converter (CEC) locations. MHKiT is used to process model simulations from Delft3D and compare them to the transect data from the ADCP measurements at TRTS. The ability to use a single tool to process simulation and field data demonstrates the ease at which the ME industry can obtain results and collaborate across specialties, reducing errors and increasing efficiency.

Field test of Model Predictive Control in residential buildings for utility cost savings

Air conditioners in residential buildings often rely on local feedback control to regulate the indoor temperature, where the unit is cycled on and off by comparing the actual temperature with the temperature set-point. Model Predictive Control (MPC) is a technique that makes use of building models, disturbance forecasts, and optimization algorithms to enhance building performance. MPC has demonstrated its potential in reducing building energy consumption and operating costs; however, it has seldom been applied in residential buildings because of the high implementation costs, complicated hardware configuration, and insufficient data. In this study, we developed and tested a MPC controller in a real apartment aimed at reducing the operating costs based on the local Time-of-Use (ToU) utility structure. We developed a feasible and easy-to-use hardware configuration as well as a cloud-based software deployment. The MPC controllers were implemented and evaluated through one-month field testing in a bedroom and living room of an apartment in a residential building. The field test showed that the MPC controller saved 22.1% for the bedroom and 26.8% cooling costs for the living room, compared to conventional rule-based control (RBC). We further confirmed the robustness of the MPC controller as the field test showed that the MPC controller is able to outperform the conventional RBC controller, even if the model is not accurate. Our work provides a practical MPC solution that can unlock the load flexibility potential of residential buildings, which creates a win–win solution for the households and the grid: saving utility costs for households and helping the grid to manage the balance between supply and demand.