Integrated Development Environment Research Articles

AI Pair Programming Tool

Abstract: In the ever-evolving landscape of software development, the need for tools that augment developer productivity is paramount. This paper introduces a novel integration of IntelliJ IDEA, a widely used integrated development environment (IDE), with OpenAI's advanced deep learning models. The synergy between these two technologies aims to elevate the coding experience by providing intelligent code assistance, allowing developers to write code more efficiently and with greater accuracy. Our approach leverages OpenAI's natural language processing capabilities to enhance code suggestions, auto-completions, and error detection within IntelliJ IDEA. By analyzing vast code repositories and learning from diverse coding patterns, our system empowers developers with context-aware recommendations, significantly reducing the cognitive load associated with code creation. This paper presents the architecture of our solution, detailing the seamless integration of OpenAI's models into the IntelliJ IDEA IDE. We discuss the methodology behind training the models, emphasizing their adaptability to various programming languages and project structures. Furthermore, we present empirical results demonstrating the effectiveness of our approach in real-world coding scenarios.

Study On the Path To Improve the Leadership Of Sports Counselors And Students' Employability

Graduates "difficult employment" has become a prominent problem in the general college education, the so-called "graduation = unemployment" concept seriously affects the realization of the goal of talent training in colleges and universities. However, the employment channels of sports major itself are relatively narrow, and many students are engaged in jobs irrelevant to their major after graduation. Therefore, this paper aims to promote the improvement of college counselors' quality and employment rate by discussing the promotion path of sports counselors' leadership and students' employment ability. To sum up, in the face of increasingly complex computer application environment, the development of computer software has put forward more stringent and diversified requirements. Therefore, the introduction of layered technology to computer software development, constantly improve the computer software development environment, improve the development efficiency, shorten the software development cycle, save the cost of software development, so as to promote the sustainable and healthy development of computer software industry.

Food Quality Monitoring System

Food poisoning affects around 600 million people each year, with 4,20,000 people dying as a result. People frequently ingest damaged meals since there are no obvious signs of deterioration. Foods release gases like Methane, Ethylene and Ammonia when they decompose. This paper introduces Food Spoilage Detection by using sensor and displayed on character 16x2 LCD. We are living in the world, where one third food is wasted every year. It is more than enough to feed millions of people in the world. Most considerable reason for food wastage is food spoilage. Food spoilage can be detected in many ways. Our project mainly focuses food spoilage detection using Sensor. In components used in this project is LCD display, sensors and Arduino Uno. Sensors, LCD display is interfaced to Arduino Uno. By using sensors we can detect the gases coming from food. The sensor sends the data to LCD display where we can view whether food is spoiler. Software required to program Arduino Uno is Arduino Integrated development environment (IDE). Hardware required for this project is Arduino board, MQ3 sensor, character 16x2 LCD, USB cable, power supply and connecting wires

IDE vs. pen-and-paper showdown - Performance and perceptions of testing first-year IT students’ programming skills

This study analysed first-year IT students' performance and perceptions of using Integrated Development Environment (IDE) versus pen and paper (P&P) based programming testing methods. 68 participants completed two programming tests using both testing methods, and the results were evaluated on complexity, quality and other criteria. No significant difference was observed in the average scores between the two test methods. However, IDE testing showed fewer syntax errors and lower cyclomatic complexity, indicating the potential advantages of IDE's error-checking. IDE tests exhibited higher variability in student performance, suggesting varying comfort levels with the test environment. Additionally, a weaker correlation existed between actual and perceived performance in IDE tests, highlighting challenges in self-evaluation within a digital framework. Most students preferred IDE-based testing, citing efficiency, user-friendliness, and real-time feedback. Nonetheless, a minority recognised the value of P&P tests for demonstrating deep syntax understanding. This research enhances our understanding of programming pedagogy and assessment.

Adapting Jupyter for C++ Programming Education: An Empirical Study on Lab Instruction Strategies and Student Perspectives

This paper explores the use of the Jupyter platform in teaching C++ programming at the higher education level. It includes a comparative analysis between traditional integrated development environments and online judge systems. The study discusses a teaching approach that leverages JupyterHub and Xeus-Cling to create an interactive learning setting. This approach was pilot-tested during a C++ lab session at Shenzhen Technology University, and student feedback was subsequently gathered and analyzed. A substantial number of students found value in Jupyter's interactive capabilities and the seamless integration of code execution and report writing. However, others experienced difficulties with the platform due to adaptation hurdles, code compatibility issues, and the lack of functionality compared to traditional integrated development environments. The paper further explores the potential of integrating Jupyter with the Visual Studio Code environment to mitigate these limitations and amplify its benefits. The paper concludes by recommending ongoing research and evaluation to refine and adapt this innovative teaching strategy.

AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities

Many Machine Learning(ML)-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-based vulnerability analysis tools, few have been integrated into modern Integrated Development Environments (IDEs), hindering practical adoption. To bridge this critical gap, we propose in this article AIBugHunter, a novel Machine Learning-based software vulnerability analysis tool for C/C++ languages that is integrated into the Visual Studio Code (VS Code) IDE. AIBugHunter helps software developers to achieve real-time vulnerability detection, explanation, and repairs during programming. In particular, AIBugHunter scans through developers’ source code to (1) locate vulnerabilities, (2) identify vulnerability types, (3) estimate vulnerability severity, and (4) suggest vulnerability repairs. We integrate our previous works (i.e., LineVul and VulRepair) to achieve vulnerability localization and repairs. In this article, we propose a novel multi-objective optimization (MOO)-based vulnerability classification approach and a transformer-based estimation approach to help AIBugHunter accurately identify vulnerability types and estimate severity. Our empirical experiments on a large dataset consisting of 188K+ C/C++ functions confirm that our proposed approaches are more accurate than other state-of-the-art baseline methods for vulnerability classification and estimation. Furthermore, we conduct qualitative evaluations including a survey study and a user study to obtain software practitioners’ perceptions of our AIBugHunter tool and assess the impact that AIBugHunter may have on developers’ productivity in security aspects. Our survey study shows that our AIBugHunter is perceived as useful where 90% of the participants consider adopting our AIBugHunter during their software development. Last but not least, our user study shows that our AIBugHunter can enhance developers’ productivity in combating cybersecurity issues during software development. AIBugHunter is now publicly available in the Visual Studio Code marketplace.

Deployment of Model-Based-Design-Adaptive Controllers for Monitoring and Control Mechatronic Devices

The modeling and control of complex, non-linear, and time-changing mechatronic systems requires complex software development. They are carried out in the prototyping phase with engineering software development tools, generating models, and control algorithms that are not always easily exportable to control hardware. The following work offers an alternative that considers Model-Based Design using graphics-based languages, which facilitates programming tasks for modeling and controlling mechatronic devices and their transition from prototype to control hardware. Model-Based Design with high abstraction programming level capabilities provides the user with a fast coding and testing environment, suitable for laboratory prototyping and subsequent transfer to commercial embedded controllers. The proposed solution combines control hardware based on an STM32H7 microcontroller and a software development environment using graphics-based languages developed for MATLAB. The result is a solution that integrates control hardware and software in a hardware-in-the-loop paradigm. This solution provides robust and energy-saving controllers and demonstrates that an advanced control algorithm can be set up in a critical safety-compliant low-cost embedded controller via a custom model-based design. Algorithms and tools are transferred to the controller without losing the advantages gained in the prototyping phase. Finally, experimental results implementing an adaptive controller in a DC motor and in a pneumatic system are shown to validate the system.

Multicriteria Optimization of Software Development Platform Selection

The problem of multicriteria optimization of tech stack selection in a non-linear software development environment is formalized. Approaches to solving the problem based on the formation of a compromise area of local quality criteria, additive convolution of vector criteria and their scalarization are considered. An aggregate quality criterion of the software development platform is proposed. It allows taking into account the nonlinearities of the external environment in the framework of solving the problem of extremal control. The coordinates of the vector criteria are piecewise continuous functions A comparative analysis of various approaches to changing the software development tech stack in the face of significant disturbances in the development tools market has been carried out. The possibilities of boxed solutions, custom development and Open Source software development in ensuring information security of the software development environment are shown. The information stability of a software development project using Less Code tools is established and the advantages of technological solutions are shown. The analysis of the adequacy of the obtained solutions was carried out using the Open Source Less Code of the Jmix platform. The data of a comparative analysis of the budgets of the Less Code platform and custom software from vendors are given, which showed the benefits of using Open Source software development tools for long-term operation of the development platform.

Evolvable Hardware Based Optimal Position Control of Quadcopter

Trading off performance metrics in control design for position tracking is unavoidable. This has severe consequences in mission-critical systems such as quadcopter applications. The controller area and propulsion energy are conflicting design parameters, whereas the reliability and tracking speed are related metrics to be optimized. In this research, a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Programmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed, propulsion energy, and reliability optimization under similar error performance. To improve the computation power and controller area, the dynamic partial reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE), where a ranking-based approach brings into action either proportional derivative, sliding mode, or model predictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed, reliability, controller area, and propulsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions. Concerning the implementation results of the controller with the switching-based controller, there is 6 % computation power and 30 % resource savings due to DPR .

Research Paper on Analysis Load Balancing Algorithm using Cloud Computing

Cloud load balancing is nothing but distributing the work load among many servers & computing property. It can manage work load demand as well as application demand. This process is done with the help of distributing resources among numerous computers. Cloud load balancing consist of circulation of workload traffic & on time demand of internet. Various techniques were represented in literature to improve performance, quality of services, cargo distribution etc. The issue of cargo balancing is same as cloud computing which is data over loading & under loading. We have to apply proper cargo balancer for cloud terrain. The study represents view of problem faced by current cargo balancing ways. The researchers make more effective algorithm. Cloud computing refers to a services & application that can accessible throughout the world from data center.all services are accessible through online. There are several issues in cloud computing such as scheduling, load balancing, quality of services & storage resources. Here we study load balancing. When many clients send request to server, server has already n number of task. In that case server is over loaded for this condition we have to apply load balancing algorithm. Cloud computing is a internet based network technology that share rapid growth in communication technology, by providing services to customer of various requirement with the help of online computing resources. It has both hardware & software application along with software development platform. Cloud computing becomes a popular word in the area of high performance distributed computing. It provide on demand access to share pool of resources over internet. Cloud computing is still much research is required for broad array of topic. One of the important research issue which need to be focus for effective performance isload balancing. Cloud computing aim is to gives computation & resources over the internet via dynamic provision of services. There are many challenges and issues associated with the implementation of cloud computing. This research paper deliberates one of cloud computing main problem that is load balancing. The main aim of load balancing is equally distribute the load on cloud server such that no host is under load & over load. Several load balancing algorithm have been implemented in literature to satisfied customer request for appropriate cloud node.

An internet of things-based touchless parking system using ESP32-CAM

<span>As technology continues to advance, governments around the world have implemented health protocols to minimize direct contact between individuals and objects, in response to the ongoing COVID-19 outbreak. To address this need, a touchless parking portal was designed using a microcontroller-based and internet of things (IoT) -based system, with the Arduino UNO microcontroller device serving as the core component. The system employs an ultrasonic sensor HC-SR04 and passive infrared (PIR) to detect vehicles as they arrive at the portal area, in addition to requiring an ESP32-CAM camera, servo motor, light-emitting diode (LED), I2C 16x2 liquid crystal display (LCD), push button, universal serial bus (USB) to transistor-transistor logic (TTL) converter, power supply, and portal bar. The system builder software was developed using Arduino integrated development environment (IDE), Android, and Blynk. The authors conducted thorough testing and analysis of the system, concluding that its overall performance reaches 100%. Nevertheless, despite the extensive experimentation conducted, there remains a possibility that certain factors could still affect the results. Therefore, caution is advised when interpreting the outcomes of this experiment.</span>

PREDICTING CREDIT WORTHINESS IN BANK LOAN APPROVAL PROCESS

Loans make up significant part in bank profits. In a bank, amongst large number of loan applications, it can be lengthy and challenging to choose genuine and eligible applications to approve the loan, if the process is done manually. The system was developed using Python 3.7 & its libraries and Jupyter Notebook cross-platform Integrated Development Environment (IDE). The developed system uses the machine learning algorithms - Naïve Bayes, Support Vector Machine (SVM) and XGBoost (Extreme Gradient Boosting) individually to classify the loan applications to automatically choose genuine and eligible applications. Based on average bank balance of the period and financial background, the system approves the loan amount to the predicted applicants with good credit history. The system’s performance in predicting the credit worthiness of applicants was evaluated. Naive Bayes algorithm predicted the credit worthiness with 79% accuracy, SVM algorithm with 81% and XGBoost algorithm with 84%.

Application of The Speed-Up Robust Features Method To Identify Signature Image Patterns On Single Board Computer

Through the development of a signature pattern recognition program on SBC Beagle-bone Black, this research seeks to determine how to differentiate between real and false signatures. Three techniques of gathering data were employed in this study: interviews, observations, and a review of the literature. The quick application development method is the approach that is applied. The rapid, efficient, and brief development cycle (RAD) is emphasized. This study uses a use-case diagram to illustrate the application's logic and data flow. In this study, OpenCV is used as a digital image processing library along with the C++ programming language and QT creator as an integrated development environment (IDE). This application was subjected to both accuracy and functional testing. The following conclusions are drawn from the findings of the investigation and testing that was done: Using the fast library approach for approximate nearest neighbors (FLANN) and the speeded-up robust features (SURF) feature extraction method, the signature pattern recognition program on the Beagle-bone black SBC can differentiate between real and fraudulent signatures. Through the processes of generating image scale space, feature localization, and feature description, the SURF approach extracts feature from signature images. This signature pattern recognition application is one of the digital image processing apps that can be run on the Beagle-bone Black single board computer. This indicates that the specifications of the SBC Beagle-bone Black for digital image processing are good.

A Graph-Based Approach for Modelling Quantum Circuits

A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.

Development of a Device for Assessing the Emotional State of Companion Dogs Based on the Analysis of Vocalizations Associated with Anxiety and Barking

Introduction. Canine behaviour, socialization and interaction of dogs with humans are the key factors to be assessed among the risks inducing the increase of incidents related with development of anxiety in dogs. First of all, the lack of knowledge about the reasons of problem behaviour in animals and the high individual variability of behavioural patterns explain the relevance of this work. Moreover, the different methods used by the practitioners and absence of traceability of the owneranimal interaction record lead to the incorrect accumulation of diverse information about the identification and correction of companion dogs’ problem behaviour. The authors of this article aimed to develop a device for registering anxious and aggressive behaviour in dogs by analysing the intervalograms and spectrograms of vocalizations.Materials and Methods. The recordings of canine vocalizations (250 recordings) were obtained as a result of authors' observations carried out in Rostov-on-Don in the period from spring to autumn 2021. The growth of signal intensity was detected by a noise sensor or microphone. The amplitude and duration of the signal were determined by a microcontroller. The Arduino Integrated Development Environment (IDE) was used for writing a programme and flashing the microcontroller. The timers calculating the number of milliseconds from the beginning to the end of an event were implemented in the programme, also, the counters measuring the number of the events within a certain period of time were used.Results. A flowchart of the device for registering and classifying canine vocalizations, which are considered to be the behavioural anxiety markers, has been developed and described. The algorithm for assessing the type of animal activity during vocalization has been suggested. The device prototype has been developed, which enables determination and sending to a Telegram chat bot the data on dog’s anxiety and activity, as well as on the ambient temperature, based on the calculation and analysis of the vocalizations intervalograms.Discussion and Conclusion. The significance of the present development lies in creation of the hardware and software complex prototype that provides the unbiased analysis of the information about changes in canine behaviour in the real time. The implementation of this complex makes it possible to assess the canine behaviour and provides the opportunity to obtain the new data on probability of behavioural disorders in animals caused by a high level of anxiety. The complex can serve as a prototype for creating the systems for tracking and identifying other animal species’ behaviour (including in the urban settings).

A review on statistical language and neural network based code completion

Code completion, also referred to as intellisense, is a prevalent feature of Integrated Development Environments (IDEs) and code editors. It aids developers by automatically recommending and inserting code segments, variable names, and method names, among other things. With the accelerated growth of the software industry and the process of digitalization in recent years, the demand for software engineers has reached a record-high level. Thus, the advancement of code completion is encouraged and has become a popular topic in software engineering. This paper examines and summarizes the development of a statistical language and neural network-based code completion system. The main contents consist of introducing the concepts of code completion system, summarizing the general process of code completion and the evaluation metrics used for performance benchmarking, reviewing and summarizing the existing work conducted on statistical language approach and neural network approach respectively, as well as the limitations and challenges of existing code completion method, and finally forecasting the future development of code completion techniques.

Flood Image Classification using Convolutional Neural Networks

Flood disaster is a natural disaster that leads to loss of lives, properties damage, devastating effects on the economy and environment; therefore, there should be effective predictive measures to curb this problem. Between the years 2002- 2023, flood has caused death of over 200,000 people globally and occurred majorly in resource poor countries and communities. Different machine learning approaches have been developed for the prediction of floods. This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. Important parameters such as standard deviation and variance were incorporated in the parameters tuned CNN model that performed flood images feature extraction and classification for better predictive performance. The enhanced model was assessed with accuracy and loss measurement and compared with the existing model. The model leverage on the unique features of region of Interest aligns to resolve the issues of misalignments caused by the use of region of Interest pooling engaged in the traditional Faster-RCNN. The techniques and the developed system were implemented using a Python-based integrated development environment called “Anaconda Navigator” on Intel Core i5 with 8G Ram hardware of Window 10 operating system. The developed model achieved optimal accuracy at 200 epochs with 99.80% and corresponding loss of 0.0890. The results confirmed that predictive performance of a model can be improved by incorporating standard deviation and variance on model, coupled with its parameters tunning approach before classification.

Design and implementation of low-power, high-speed, reliable and secured Hardware Accelerator using 28 nm technology for biomedical devices

This paper implements a Hardware Accelerator (HA) for Wireless Body Sensor Networks (WBSNs) using 28 nm technology on Zynq system-on-a-chip (SOC) using Vivado and VITIS high-level synthesis tool. The Vitis integrated development environment (IDE) is a software-only development environment that Integrates with other Xilinx tools. The Zynq® SoC combines a state-of-the-art processor system (PS) and programmable logic (PL) into a single cohesive device. WBSNs contain different processing techniques like predictor, compressor, encryption encoder, and Error Control Coding (ECC) before transmission. These techniques are implemented and validated using the Xilinx® Software Development Kit (SDK) tool along with the PYNQ Z2 board having Zynq SOC respectively. Similarly, the processing techniques are implemented on PS block as HA. The results of the work show the data collection and processing through the dual-core ARM Cortex A9 processor of the board. The purpose of the work is to calculate the time required to process the bio-signals through the controller solely and using HA. The proposed work, mainly WBSN using HA shows an improvement of 81% in the speed compared with existing methods. Further, results of the synthesis and implementation strategy show improvement in power and area of HA with WBSN.

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.

USE OF PYTHON AND COMPLETE BLOOD COUNT PARAMETERS FOR COST-EFFECTIVE THALASSEMIA SCREENING IN RESOURCE-LIMITED SETTINGS: DEVELOPMENT AND VALIDATION OF A SCREENING PROGRAM.

Thalassemia screening is typically done using High-performance liquid chromatography (HPLC), which is an accurate but expensive method that is not widely available. To overcome this issue, researchers have looked into alternative screening methods, such as using erythrocytic indices obtained from a complete blood count (CBC) test. This approach has proven to be highly sensitive and specific, making it an attractive and cost-effective solution for excluding normal populations from thalassemia screening programs. Consequently, it has the potential to improve the efficiency of screening programs, particularly in settings with limited resources.
 A Python program, using the study by Samanta et al. (2021) as a basis, to screen for thalassemia using CBC parameters was created. The program was developed in Python 3.8 using Spyder IDE (Integrated development environment), and it takes in CBC parameters, such as hemoglobin, RBC (Red blood Corpuscles), MCV (mean corpuscular volume), MCH (Mean corpuscular hemoglobin), and HCT (Hematocrit), to determine an individual's thalassemia status. We validated the program using a dataset of 3,000 students who had undergone CBC testing at a local clinic. The dataset was anonymized to ensure privacy protection
 Our study showed that the Python program for thalassemia screening based on CBC parameters accurately identified individuals with thalassemia. We validated its performance on a large dataset of students and found that it has the potential to improve screening efficiency and accuracy, particularly in resource-limited settings. However, additional validation studies are necessary to confirm its generalizability and usefulness in diverse populations.