Entire Software Research Articles

Navigation methods using airlandscape data for small UAVs

The purpose of the research. UAVs are widely used in such fields as military, intelligence, and research. As a result, the issue of their use in electronic warfare conditions and the presence of various interference, both man-made and natural, is acute. Thus, the need for UAV navigation using visual data increases.The purpose is study of the main negative factors affecting the quality of aerial photography images and methods for eliminating them to construct a correct orthomosaic for UAV visual navigation.Methods. Mathematical methods are proposed to eliminate negative distortions in UAV camera images using various image transformation approaches. After applying these methods to the original images, corrected versions are obtained, which are used to construct the tile covering. Tiled coverage created from processed images provides continuous and uniform coverage of the area collected during the UAV’s flight. This allows you to obtain exact coordinates of images and objects on them.Results. An analysis of the main methods for eliminating negative factors that distort images during aerial photography for the visual navigation of UAVs, as well as a brief overview of the visual navigation methods themselves.Conclusion. To successfully implement visual navigation of a UAV, it is necessary to apply a number of methods for converting aerial photography images, as well as the use of certain algorithms for visual navigation. It is concluded that in addition to the use of mathematical and software algorithms, it will also be necessary to analyze and study the necessary computing power for the use of the entire hardware and software complex on board the UAV, taking into account its weight-dimensional properties.

The Trustworthiness Metric Model of Interface Based on Defects

The interface is a crucial element in component-based software, enabling the linkage of distinct components to facilitate interaction. Defects within the interface can significantly impact the overall trustworthiness of the system. Therefore, it is essential to assess the interface trustworthiness based on a defect-centric approach. This paper introduces a novel model for evaluating interface trustworthiness, anchored in defect analysis. First, the defect types are formalized based on interface specifications. Then, the comprehensive weight allocation method is established to characterize the importance degree of each interface defect type by combining the G1 and CRITIC methods. Subsequently, the attributes of the interface are evaluated by defect value analysis, and the trustworthiness measurement model of the interface is proposed based on these attributes. Furthermore, to evaluate the trustworthiness of the whole system, the trustworthiness measure models under different combination structure of components are established. Finally, the model’s’ applicability is demonstrated through an illustrative example. This trustworthiness evaluation from the interface view can guide interface designers to obtain high-quality interfaces and improve the trustworthiness of the entire software.

What you need to know about a learning robot: Identifying the enabling architecture of complex systems

Nowadays we deal with robots and AI more and more in our everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. This can lead to misconceptions about the behavior of the technologies being used. This in turn can lead to misuse and rejection by users. Explanation, for example through transparency, can address these misconceptions. However, explaining the entire software or hardware would be confusing and overwhelming for users. Therefore, this paper focuses on the ‘enabling’ architecture. It describes those aspects of a robotic system that may need to be explained to enable someone to use the technology effectively. Furthermore, this paper deals with the ‘explanandum’, i.e. the corresponding misunderstandings or missing concepts of the enabling architecture that need to be clarified. Thus, we have developed and are presenting an approach to determine the ‘enabling’ architecture and the resulting ‘explanandum’ of complex technologies.

MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the gap between benchmarking, experimentation, and optimization of software systems. It allows users to create one-click benchmarking and experimentation scenarios for multi-VM setups in the cloud with optional standard and custom metrics collection and data management of the results. MLOS provides a collection of pluggable optimizers (ML or otherwise) for efficiently exploring the configuration space and finding optimal values for parameters across the entire software stack, including VM, OS kernel, and userland applications. It has a convenient lightweight interface for data storage, access, and visualization for a user-friendly notebook experience. These features make it a useful platform for both systems developers and auto-tuning researchers. MLOS is an active open-source project and is being used within Azure Data. A video demonstrating MLOS is available at https://aka.ms/MLOS/VLDB-2024-demo-video.

QUALITY ASSESSMENT OF THE SOFTWARE DEVELOPMENT PROCESS OF AN IT COMPANY BASED ON THE USE OF THE UTILITY FUNCTION

The paper considers the software development process as an object of research, which is a poorly structured system. A description of such systems is given in the form of general characteristics, which include: difficulties in building an analytical model; incompleteness, inaccuracy, unreliability and uncertainty of information; benchmarks required for assessing weakly structured systems are often absent; uniqueness of the decision-making process; dynamic nature of models of poorly structured systems, etc. In this paper, the quality assessment of the software development process is considered based on maturity model standards, which can have continuous and discrete variants. The continuous version assesses the quality of the individual focus areas and processes of the maturity models. For this purpose, a discrete point scale of the first type is used, when the assessment is carried out according to an objective criterion. The quality assessment of individual focus areas and processes characterizes the local criteria for assessing the quality of the entire software development process. Therefore, the task is to form some kind of integral quality assessment on their basis. One of the options for solving this problem is a discrete maturity model, where the scale for assessing the entire software development process has five gradations called maturity levels. Starting from the second level, each gradation is characterized by a set of focus areas with corresponding levels of capability. The availability of such a scale allows not only assessing the quality of the entire software development process, but also solving the task of planning to improve its quality. But first, it is necessary to analyse such a scale from the point of view of its balance, namely, that the distances on the scale between the gradations are approximately equal. Therefore, the paper analyses the existing scales that can be proposed for expert assessment for the quality of the software development process. Their construction can be realized on the basis of a utility function using the local criteria of maturity models formalized in this paper. For this purpose, a fundamental property of systems is used. Namely, the dependence of the utility (efficiency) of a complex system on the invested resources over the life cycle interval, which usually takes the form of a logistic curve. Further research will be devoted to using this fact to build a balanced scale for assessing the entire software development process based on maturity models.

Measuring and improving software testability at the design level

ContextThe quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels. ObjectiveThe objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (e.g., fan-in and fan-out) and inheritance (e.g., depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates. MethodThe methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed. ResultsThe proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R2 score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %. ConclusionThe proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.

Machine Learning Systems are Bloated and Vulnerable

Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applications to containers. Containers are lightweight virtualization technologies used to package code and dependencies, providing portable, reproducible and isolated environments. For their ease of use, data scientists often utilize machine learning containers to simplify their workflow. However, this convenience comes at a cost: containers are often bloated with unnecessary code and dependencies, resulting in very large sizes. In this paper, we analyze and quantify bloat in machine learning containers. We develop MMLB, a framework for analyzing bloat in software systems, focusing on machine learning containers. MMLB measures the amount of bloat at both the container and package levels, quantifying the sources of bloat. In addition, MMLB integrates with vulnerability analysis tools and performs package dependency analysis to evaluate the impact of bloat on container vulnerabilities. Through experimentation with 15 machine learning containers from TensorFlow, PyTorch, and Nvidia, we show that bloat accounts for up to 80% of machine learning container sizes, increasing container provisioning times by up to 370% and exacerbating vulnerabilities by up to 99%. For more detail, see the full paper, ~\citezhang2024machine.

Detection of Fake Currency Using Machine Learning

Counterfeiting of currency is a growing problem due to advancements in color printing technology, impacting economies worldwide. Fake money is often used for criminal activities, including terrorism. Research indicates that developing countries, like India, are particularly affected. One solution proposed involves analyzing the image of counterfeit currency through pre-processing steps such as converting from RGB to grayscale. This approach aims to tackle the issue of counterfeit currency circulation effectively. After converting the text, the image is divided into sections, its characteristics are assessed, relationships are identified, and a decision is made to determine if the image is authentic or counterfeit. Counterfeit currency continues to be a notable problem. Financial institutions and other establishments have the necessary resources to confirm the legitimacy of money. However, the average person does not have access to such equipment, making it essential to have counterfeit money detection software that can be used by anyone. This study achieves a maximum accuracy of 81% when analyzing 50 Indian currency notes with a denomination of 2000 rupees. This initiative offers a comprehensive overview of a counterfeit detection system that is accessible to the general public. The proposed system uses image processing to distinguish real currency from counterfeit money. The entire software has been developed using the Python programming language. Key Words: Fake Currency, Counterfeit, Denomination, Python, Machine Learning,

Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.

Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience. Open-source frameworks dedicated to Machine Learning, such as Tensorflow and Keras have produced significant changes in the development of technologies that we currently use. This work contributes by comprehensively investigating and describing the application of RNNs for temporal processing through a study of a 3-bit Flip Flop memory implementation. We delve into the entire modeling process, encompassing equations, task parametrization, and software development. The obtained networks are meticulously analyzed to elucidate dynamics, aided by an array of visualization and analysis tools. Moreover, the provided code is versatile enough to facilitate the modeling of diverse tasks and systems. Furthermore, we present how memory states can be efficiently stored in the vertices of a cube in the dimensionally reduced space, supplementing previous results with a distinct approach.

Machine Learning Systems are Bloated and Vulnerable

Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applications to containers. Containers are lightweight virtualization technologies used to package code and dependencies, providing portable, reproducible and isolated environments. For their ease of use, data scientists often utilize machine learning containers to simplify their workflow. However, this convenience comes at a cost: containers are often bloated with unnecessary code and dependencies, resulting in very large sizes. In this paper, we analyze and quantify bloat in machine learning containers. We develop MMLB, a framework for analyzing bloat in software systems, focusing on machine learning containers. MMLB measures the amount of bloat at both the container and package levels, quantifying the sources of bloat. In addition, MMLB integrates with vulnerability analysis tools and performs package dependency analysis to evaluate the impact of bloat on container vulnerabilities. Through experimentation with 15 machine learning containers from TensorFlow, PyTorch, and Nvidia, we show that bloat accounts for up to 80% of machine learning container sizes, increasing container provisioning times by up to 370% and exacerbating vulnerabilities by up to 99%.

Software Component Selection: An Optimized Selection Criterion for Component-based Software Engineering (CBSE)

Component-Based Software Engineering (CBSE) is gaining popularity in software development due to time and cost limitations. As software applications have become integral to people's lives, developing high-quality and user-friendly applications within a reasonable timeframe and budget has become increasingly challenging. Software development firms frequently use Commercial-Off-The-Shelf (COTS) components to address this challenge and reduce development costs and time. However, selecting appropriate components that meet customer requirements and integrate seamlessly with the target system is a complex task requiring considering the entire software system's quality. This study investigates the critical factors that software industry practitioners and experts must consider when selecting software components. First, the author asked practitioners to identify the most important quality criteria for an online bookstore from a list using subjective judgment and evaluation grades. Then, the study employed the Evidential Reasoning (ER) approach to tackle the multi-level evaluations and information uncertainty associated with software component selection issues. The ER approach's primary features, such as weight normalization, probability assessment, uncertainty management, and utility intervals, offer several benefits for COTS selection problems, including cost and time reduction, improved software reliability, effectiveness, and efficiency. This study assessed the quality criteria for an online bookstore using the ER approach and provided analysis results based on the approach's computational steps. Finally, the study ranked the four components according to their weights, evaluation grades, and belief degrees for selection

An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users.

ADVANCED AUTOMATIC SYSTEM FOR REMOTE CONTROL IN THE OIL AND GAS INDUSTRY

The purpose of this work is to design and implement a cascade control system of the level with the flow rate, with remote supervision, in an industrial type installation, located in the Laboratory of Measurement Techniques, Department of Automation, Computers and Electronics, within the Faculty of Mechanical and Electrical Engineering. The operating parameters of the installation are as follows: suction pressure: 0 – 2 bar; discharge pressure: 0 -10 bar; water flow: 0-30 m3/h; transported liquid: water. The automation system is structured on three hierarchical levels: level 1: the instrumentation mounted on the installation (transducers, signaling devices, etc.); level 2: includes the TGA System; level 3: constitutes the upper hierarchical level and includes the HMI (Human Machine Interface). The TGA system is a computerized system that ensures the monitoring, command and control functions of all the stand's component elements. It contains a programmable logic controller PLC 1214 – Siemens, which communicates via an Ethernet IP network with an operator interface and with the network of the Faculty of Mechanical and Electrical Engineering, which allows remote control of the installation. It also includes the protection system in case of failure, which involves stopping the rotating equipment, isolating the pipes by closing the control valve and alarming the operators. The HMI (Human Machine Interface) interface presents 3 options on the start page, from where the operator can select which application he wants to test: flow control loop application; level adjustment loop application; cascade adjustment loop application. The HMI equipment integrates SCADA functions, database update, evolution curves of analog quantities, sound and monitor alarm system, keyboard command, periodic records, event records, alarm records and technological schemes at the human-machine interface. The entire software is made in an open architecture, recommended by ISO norms. The PLC communicates in the network by adopting the protocol TCP/IP. An executable program used for the real-time application program was developed.

DevOps, Continuous Integration and Continuous Deployment Methods for Software Deployment Automation

In the fast-paced landscape of software development, the need for efficient, reliable, and rapid deployment processes has become paramount. Manual deployment processes often lead to inefficiencies, errors, and delays, impacting the overall agility and reliability of software delivery. DevOps, as a cultural and collaborative approach, plays a central role in orchestrating the synergy between development and operations teams, fostering a shared responsibility for the entire software delivery lifecycle. Continuous Integration is a fundamental DevOps practice that involves regularly integrating code changes into a shared repository, triggering automated builds and tests. Continuous Deployment complements Continuous Integration by automating the release and deployment of validated code changes into production environments. The purpose of this research is to create a software deployment automation system to make it easier and reliable for organizations to deploy software. In conclusion, the results of this research show that by adopting DevOps, Continuous Integration, and Continuous Deployment, organizations can achieve enhanced collaboration, shortened release cycles, increased deployment frequency, consistent deployment, and improved overall software quality.

Software Development Life Cycle (SDLC)

Abstract: The software development life cycle (SDLC) process plays an important role in the development and delivery of a software product. This research article provides an in-depth analysis of various SDLC approaches and their unique capabilities, benefits, and challenges. By reviewing traditional and modern SDLC models,this article aims to provide insight to software developers, project managers, and project partners to help them make decisions. Information and development into the entire software development process.

Automatic Transfer Switch (ATS) Using Omron CP1L PLC and Human Machine Interface

Airports in aviation services are required to use facilities with the latest technology. This requires a stable and uninterrupted electricity supply to operate correctly. The Automatic Transfer Switch design based on the CX-Designer Human Machine Interface moves the electrical power supply source from PLN to the Generator Set or vice versa. There are two operating modes, namely manual mode and auto mode. In operation, it is also equipped with an interlock function between the PLN contactor and the generator and a PLN phase loss sensor for the R, S and T phases. For auto mode, it uses a timer with a setting for PLN of 12 seconds and for the generator of 15 seconds. The control system uses an Omron CP1L-L20DR-D PLC with CX-Programmer programming for Human Machine Interface with CX-Designer. The Automatic Transfer Switch test was successful in system testing, starting from manual and auto mode, PLN phase loss testing, and timer testing; 100% of the entire hardware and software system ran well.

Design of foreign object debris detecting and monitoring using two sets of sensor system

<span>Foreign object debris (FOD) is a foreign object that should not be on the runway which might cause the risk of aircraft crashes and put the lives of humans at risk. In this research, we designed a system to detect FOD using two sets of a sensor system for a wider, area of detection. This research was commenced by designed the entire hardware and software and tested the system. The object used as the testing object were rock, bolts, plastic bottles as well as iron pieces with varied distances and position angles. Based on the testing result, both sets of sensor system could detect objects with distance accuracy on the sensor system set of FOD 1 and FOD 2 consecutively were 98% and 94% for rock, 97% and 96% for bolts, 96% and 94% for plastic bottles, 96% and 94% for the iron pieces. The accuracy of angle measurement on the sets of sensor system FOD 1 and FOD 2 consecutively were 96% for rock, 98% for bolts, 97% and 95% for plastic bottles, 97% and 94% for the iron pieces. Monitoring system testing was carried out using black box testing and website response speed test towards the variation of database number.</span>

Qualification and optimisation of a gas mixing apparatus for complex trace gas mixtures

Abstract A gas mixing apparatus (GMA) can provide well-defined gas mixtures, which are necessary to calibrate gas sensors for quantitative measurements. This article presents a novel modular GMA and especially the representative measurements carried out to qualify and quantify its performance. New methods were developed to optimise the GMA’s settling speed and to enable self-monitoring. The GMA is able to provide up to 14 individual test gases, each in a large concentration range from sub-parts-per-billion to hundreds of parts-per-million. Additionally, the relative humidity can be set. The system is able to provide stable gas mixtures in approximately 6 min. The entire control software is realised in Python, which determines the control setpoints of all relevant system components based on the concentration input of the user. A major focus was placed on minimising the time of a gas mixture exchange to confirm design and additional software optimisation. Also, a number of techniques are shown for the analytical quantification of the system, including analysis of thermal desorption tubes by an accredited laboratory, showing maximal contaminations of e.g. only 4.18 ppb ethanol. Finally, self-monitoring of the system using photoionization detectors (PID) is demonstrated as an addition to flow readback for internal leakage detection.

Performance Engineering: The Crucial Step for Optimized System

Performance engineering refers to the systematic discipline of engineering systems and software to meet performance requirements. The goal is to build systems that provide excellent speed, scalability, reliability, and resource usage to end users under current and projected workloads. Performance engineering applies scientific principles and methods throughout the entire software development lifecycle to proactively address performance issues before they negatively impact end users. It seeks to build peak performance capabilities into systems by design right from the start.

An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments

Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design.