Journal of Computer Science Engineering and Software Testing

Object-Oriented Metrics and Machine Learning–based Early Prediction of Software Reliability: An Analytical Framework

Arpita Tewari — 2026-07-08

Achieving high reliability is essential for ensuring that software systems operate successfully in real-world environments, particularly in high-assurance domains. As modern software systems become larger and more complex, predicting reliability at an early stage of development has become essential for reducing maintenance costs, improving testing strategies, and ensuring dependable software delivery. This paper explores the early prediction of software reliability by leveraging object-oriented metrics with machine learning techniques. It presents methodologies for assessing software reliability at early stages of the software development lifecycle, using metrics such as coupling, cohesion, inheritance, and complexity. The framework utilizes historical software datasets containing design metrics, defect information, and reliability indicators to develop predictive models. Machine learning algorithms such as Random Forest, Support Vector Machine, and Artificial Neural Networks are applied to identify complex relationships between software characteristics and potential failures. The proposed framework supports proactive software quality management by helping developers prioritize testing, optimize resource allocation, and improve design decisions before deployment. This approach contributes toward developing reliable, cost-effective, and high-quality software systems while reducing long-term maintenance efforts.

The paper reviews key machine learning algorithms that have been employed to model software reliability prediction and analyzes case studies that demonstrate the effectiveness of these approaches. The findings suggest that certain OO metrics can serve as significant indicators of software reliability, facilitating proactive management of software quality.

SentinelMind: A Four-channel Deep Learning Platform for Behavioral Depression Screening through Integrated Text, Speech, Facial, and Video Signal Processing

Nagnath Biradar — 2026-07-01

Mental health disorders, particularly clinical depression, represent a major global public health crisis that is often underdiagnosed due to the subjective nature of traditional diagnostic practices. While automated screening tools have emerged, they commonly rely on a single behavioral modality, such as written text or speech prosody, failing to capture the full spectrum of observable patient indicators. This paper presents SentinelMind, an integrated, multi-modal screening platform that simultaneously processes four distinct behavioral channels: written text, spoken audio, static facial images, and temporal video signals. The platform utilizes specialized deep learning encoders, incorporating a fine-tuned RoBERTa transformer for semantic language analysis, a Wav2Vec2 model for speech emotion mapping, and a DeepFace visual engine paired with an OpenCV detection backend to extract static and dynamic facial expression distributions. Individual modality predictions are dynamically combined through an adaptive Cross-Modal Gated Attention Fusion mechanism that adjusts weighting based on signal confidence. Deployed as a lightweight Flask web application, SentinelMind operates strictly on commodity hardware under a 3.2 GB peak RAM footprint. Experimental results demonstrate that the four-channel fused configuration achieves an overall binary classification F1-score of 92.0 %, representing a significant performance gain over bimodal (88.3 %) and individual channel baselines. The system delivers an explainable and responsive screening utility suitable for clinical workflows.

Artificial Intelligence for Real-Time Predictive Analytics in Smart Systems

Nalla Sravani — 2026-07-02

Artificial Intelligence (AI) has become a foundational technology for enabling real-time predictive analytics in smart systems, including smart cities, healthcare infrastructures, industrial IoT environments, intelligent transportation networks, and energy grids. Real-time predictive analytics integrates machine learning, streaming data processing, edge computing, and distributed architectures to analyze continuous data flows and generate actionable insights with minimal latency. This paper reviews the architectures, algorithms, and system-level integrations that enable AI-driven real-time prediction in smart environments. It discusses advanced learning models such as deep neural networks, reinforcement learning, and ensemble methods for dynamic decision-making. The review further examines sensor fusion, streaming frameworks, uncertainty modeling, and scalability challenges. Ethical, legal, and security considerations—such as data privacy, fairness, robustness against adversarial attacks, and governance—are analyzed in the context of high-stakes automated systems. Finally, technical challenges including computational constraints, explainability, validation, and human oversight are explored alongside emerging research directions such as federated learning, neuromorphic computing, and edge intelligence. The study provides a comprehensive synthesis of current technologies and future pathways for deploying trustworthy, scalable, and adaptive AI-powered predictive analytics in smart systems.