Journal of Advancement in Software Engineering and Testing
This is blind peer-reviewed journal that provides a platform which aspires to cover all the aspects of Software Engineering and its Integration into Recent Technologies in the field of Computer Engineering. Focus and Scope Formal Methods and Programming Languages Web Technologies Performance Evaluation and Measurements Tools and Environments for Software Development Visualization Parallelizing Compilers Internet Computing Component-based and OO Technology Optimization Parallel Processing Language, Compliers and Operating Systems
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Blended Learning to Overcome Inadequate Infrastructure
Blended learning is a combination of eLearning and traditional face-to-face learning. Since challenges have been identified related to blended learning that hinder effective teaching and learning, this study examined student opinions of this learning model. In resource-constrained settings, inadequate infrastructure severely limits access and quality. This paper explores blended learning as a possible sustainable solution. The strategic integration of cost-effective digital tools with in-person instruction allows blended models to bypass physical limitations. This means more students can join, learn better, while needing smaller upfront costs - so it grows easily without big spending.
System Activity Tracker is a tool that helps users and teams protect their computers. It is a place where you can see exactly what is happening with your files, apps, and connected devices at any time. System Activity Tracker makes it so you do not have to use many different security programs. It has file system monitoring, productivity tracking, and a dashboard where all system logs are shown together. System Activity Tracker is built with modern technology like React, Electron, and Python. It uses SQLite and Supabase to make sure everything is in sync across the cloud. This report is about how System Activity Tracker is put together, how it records every file change, and how it is designed to keep your system safe. System Activity Tracker is a tool that can help teams keep their data secure and be more aware of their work habits. It is really good for people who want to protect their hardware. Additionally, System Activity Tracker integrates cloud-powered security alerts, custom folder tracking, and external platforms like Resend for email reports. These features enhance security by automatically sending alerts for suspicious deletions and enabling deep monitoring of any folder on the device.
Efficient perception and predictive intelligence are critical components of modern autonomous driving systems. DriveIntent: AI-Powered Trajectory Prediction System is designed to enhance road safety by accurately forecasting the future movements of vulnerable road users using advanced artificial intelligence techniques. The system employs transformer- based trajectory prediction combined with multi-sensor fusion to generate context-aware and multimodal future paths, overcoming the limitations of traditional reactive approaches. It further improves prediction reliability by modelling social interactions between agents and leveraging kinematic motion features. To ensure robustness, data from cameras, LiDAR, and radar are integrated, enabling consistent performance under occlusion and challenging environmental conditions. A real-time backend supports efficient inference, while an interactive dashboard provides intuitive visualization of predicted trajectories. Developed using modern deep learning frameworks and scalable system architecture, DriveIntent demonstrates the potential of AI-driven prediction systems to transform safety and decision- making in autonomous mobility.
DEEPVISON: AN INTELLIGENT FRAMEWORK FOR LIVESTOCK
Deep Vision: An Intelligent Framework for Livestock Monitoring is a smart, low-cost monitoring system designed to enhance livestock health, welfare, and farm productivity, particularly in rural and underserved areas. The system leverages existing mobile camera footage, eliminating the need for expensive IoT devices, and uses the YOLOv8 object detection model to accurately identify and track animals in real time. For behavior analysis, the system employs Convolutional Neural Networks (CNNs), specifically using architectures like EfficientNet and ResNet, to classify key livestock behaviors such as feeding, resting, movement, aggression, and isolation—crucial indicators of animal health and well-being. To ensure robust performance across varying environmental conditions such as lighting, weather, and farm layout, the system uses synthetic data augmentation through Generative Adversarial Networks (GANs), addressing class imbalance and improving generalization. The model also incorporates explainable AI through Grad-CAM, allowing visualization of the decisionmaking process by highlighting the regions in an image that influenced the CNN’s predictions—this builds trust, transparency for farmers and experts interpreting system outputs. Furthermore, to make the system accessible to farmers in diverse linguistic regions, it integrates Google Translate API to provide multilingual dashboard support, alerts, and insights in local languages, thereby enhancing usability and inclusiveness. An interactive dashboard offers live animal monitoring, behavior tagging, automated health alerts, and historical behavior analytics, enabling timely and informed decisions. Overall, this system combines deep learning, explainability, and language accessibility to deliver a practical solution.
AI-POWERED EXAM QUESTION PAPER PREDICTOR
The rapid growth of digital education has created a need for intelligent systems that can assist in academic evaluation and exam preparation. Traditional methods of question paper setting are time-consuming, repetitive, and often lack adaptability to changing syllabi and student needs. To address these challenges, this project presents an AI-powered Question Paper Predictor, a system designed to automatically generate structured and relevant question papers using past examination data and syllabus inputs. The proposed system integrates Natural Language Processing (NLP) and Large Language Models (LLMs) to analyze syllabus content and previously asked questions. It identifies important topics, extracts key concepts, and generates new questions that align with academic standards. The system follows principles such as Bloom’s Taxonomy to ensure a balanced distribution of difficulty levels, including knowledge-based, analytical, and application-oriented questions. The workflow of the system involves extracting text from uploaded PDF documents, cleaning and segmenting the data, and processing it using AI models to generate meaningful and contextually accurate questions. A user-friendly interface allows users to upload syllabus or past papers and receive a complete question paper as output, which can also be downloaded for further use. By automating the question paper generation process, the system reduces manual effort, improves efficiency, and provides a smart tool for both educators and students. It can be used for exam preparation, practice tests, and academic planning. The project demonstrates how artificial intelligence can enhance educational tools by making them more adaptive, scalable, and intelligent.
AgentMesh: Multi-Agent Code Analysis Framework for Autonomous Quality Assurance via Distributed Agent Orchestration
As software systems continue to grow in complexity, ensuring their reliability and quality has become increasingly challenging. Modern development practices involve frequent updates, distributed architectures, and rapid deployment cycles, which place significant pressure on traditional testing methods. Conventional approaches, which depend on manual testing and static automation scripts, often fail to keep pace with dynamic requirements, resulting in incomplete test coverage, delayed defect detection, and increased development overhead. This project introduces an Agent Mesh-based intelligent testing framework designed to improve the efficiency and effectiveness of software testing. The proposed system is built on a multi-agent architecture, where each agent is assigned a specific role such as planning test strategies, generating test cases, executing tests, detecting anomalies, and validating outcomes. These agents work collaboratively, enabling the system to handle complex testing workflows in a coordinated and adaptive manner. The framework analyzes application inputs, system behavior, and code changes to automatically generate relevant test scenarios and execute them in real time. It further evaluates the results, identifies potential issues, and prioritizes them based on severity. By incorporating a feedback mechanism, the system continuously refines its testing process, making it more robust and adaptive over time. By reducing dependence on manual intervention and enabling intelligent automation, the proposed approach enhances test coverage, accelerates the testing cycle, and improves overall software quality. The system is scalable and can be extended to integrate with real-time development pipelines, making it suitable for modern agile and continuous integration environments.
Disaster Preparedness and Response Education System for Schools and Colleges
This work proposes a smart Disaster Preparedness and Response Education System (DPRES) designed for schools and colleges. The system integrates virtual simulation-based learning, real-time emergency response guidance, and automated safety assessment modules to improve disaster awareness among students and teachers. DPRES incorporates multi-hazard sim- ulation models, AI-driven adaptive training, and mobile-based emergency communication. To validate the efficiency of the proposed system, a pilot deployment was conducted in two educational institutions for six months. Evaluation results showed a 63% improvement in disaster protocol knowledge and a 47% reduction in emergency evacuation time during mock drills. The findings demonstrate that the system significantly enhances safety preparedness in the education sector.