The enhancement of software system reliability and quality through software testing is a crucial aspect of the software development lifecycle. However, traditional software testing methods often entail significant investments in time, labor, and cost. In recent times, search-based test data generation has emerged as an operational methodology for achieving this efficiency. Various approaches have been developed to generate test cases for branch coverage using meta-heuristic algorithms. Despite their effectiveness, there exists room for improvement in existing methodologies. In this research, we propose a novel search-based test data generation method for branch coverage software testing, leveraging the capabilities of Particle Swarm Optimization (PSO). To validate our approach, we conducted experiments on seven well-known software programs. Our results demonstrate that the proposed PSO-based method outperforms existing test data generation methods such as Simulated Annealing (SA), Genetic Algorithm (GA), Harmony Search (HS), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC). Specifically, our method consistently produces superior test data in significantly fewer iterations, effectively covering a greater number of branches. This research contributes to the ongoing efforts in optimizing software testing processes, emphasizing the potential of PSO in enhancing the efficiency of automated test data generation for branch coverage.

Estimating effort early in the software development life cycle is essential for proper planning. It enables better allocation of resources, time, and budget, helping to avoid project delays and cost overruns. Inaccurate estimation often leads to project failures, which is a pervasive issue nowadays for software project managers. Machine learning approaches have generally shown significant success in addressing estimation challenges across various engineering domains. This study introduces a novel method, combining a Dense neural network (DNN) with a metaheuristic adaptive anti-predatory (AP) Algorithm known as AP-DNN. This method is effectively used to address the challenges of estimating software effort. The adaptive anti-predatory (AP) algorithm is utilized to optimize the parameters of the DNN, improving its capacity to explore the parameter space thoroughly and avoid getting trapped in local optima. The proposed anti-predatory dense neural network (AP-DNN) method was tested on several benchmark SEE datasets, and its performance was compared with various contemporary algorithms from existing literature. The comparative results indicate that AP-DNN outperforms other methods across most evaluation metrics and datasets.

The Flying Ad Hoc Network (FANET) is a novel technology that enables unmanned aerial vehicles (UAVs) to create self-governing wireless connections. However, UAVs deal with several obstacles, including limited power, frequent connection failures, high mobility, and short communication ranges. This entails developing an efficient and secure routing technique to ensure consistent message delivery from source to destination. This paper uses a new innovative approach called 3D Zone-based adaptive rectangle filtering (3D-ARF) combined with the responsive routing algorithm to enhance the routing efficiency and security in flying ad-hoc networks. The initial stage method confines the distribution of RRIQ (route request Inquiry) and RCF (route confirmation) packets within a designated 3D rectangular zone. 3D ARF includes a rectangle filtering process that supervises a relay node using metrics like speed and position to minimize the unwanted broadcast, reduce routing overhead and ensure stable route selection. Secondly, it safely transmits data while keeping a method as simple as possible. The results demonstrate that 3D-ARF significantly improves packet delivery rates, energy efficiency, drops packet and latency compared to AODV, making it particularly suitable for FANETs. Moreover, performance analysis reveals that 3D-ARF achieves a 96% accuracy rate in networks of varying sizes, outperforming DSR (85%) and DSDV (80%).

Reconnaissance attacks serve as the initial phase of Advanced Persistent Threats (APTs). The study proposes CluSHAPify, an approach that integrates SHAP-based traffic metadata selection with hierarchical clustering interpretations to determine the most relevant features for attack detection across different attack flow classes. Unlike most studies that select the top-k features, the proposed study uses hierarchical clustering to justify the selection of features identified with the highest SHAP values ensuring the most relevant features are chosen for effective attack detection across different attack flow classes. Additionally, CluSHAPify leverages multiple learners, making it a cross-model approach that also overcomes the limitations of SHAP-based feature selection, which is inherently model-dependent. The proposed approach uses multiple learners to improve feature selection robustness by capturing diverse perspectives, combining XAI for enhanced accuracy and explainability, a novel approach in existing research. This study uses performance metrics designed for unbalanced datasets, demonstrating its effectiveness with various learners, including XGBoost, Random Forest, Decision Tree, and Extra Trees. This makes CluSHAPify a reliable and adaptable solution for detecting reconnaissance attacks in IIoT environments.

Software Fault Prediction has a critical role in improving effectiveness and reliability of software systems by identifying potential faults early in the development cycle. A hybrid PSO optimized CNN-RNN model leverages the strengths of both RNN and CNN in capturing temporal and spatial data features, while PSO optimizes hyperparameters to enhance model performance in this research. The proposed PSO optimized CNN-RNN model is compared against existing hybrid machine learning models, where PSO optimized Genetic Algorithm (GA) was used for hyperparameter tuning and feature selection of Support Vector Machine (SVM). Our experiments are performed on publicly available software fault’s datasets, providing a comprehensive comparison of model performance that is evaluated on the basis of various matrices of performance like F-measures, accuracy, recall, F1-score, SD and precision. The results demonstrate that while optimized Machine Learning algorithms perform well in some cases, the CNN-RNN-PSO model consistently outperforms them, offering superior fault prediction capabilities. The NASA MDP repository’s benchmark datasets are used for the comparative analysis and the results demonstrated that the optimized hybrid machine learning model achieves competitive performance. The proposed PSO optimized CNN-RNN model demonstrates superior accuracy and robustness due to its deep learning architecture and optimization capabilities. This research focus on the potential of a hybrid DL approach which improves the software reliability and suggests future directions for integrating intelligent models in SFP.

Health maintenance organizations, doctors, hospitals, and various medical institutions provide large amounts of medical data to each of us. All these important data should be preserved to be viewed anytime and anywhere via Electronic Health Records (EHRs). Electronic Health Records mainly contain the patient's medical information, such as the patient's medical history, appointments, diagnosis, medicines, prescriptions, and current treatments. EHRs are often administered by a single vendor, which implies that all personal information is saved in data sets controlled by the vendor in charge of the archives. The requirement for a strategy and the vulnerability in security frameworks, EHR theft is rapidly becoming common. The novel framework built upon blockchain offers unparalleled security, transparency, and efficiency in handling sensitive medical information. By leveraging blockchain's decentralized architecture, patient records are securely stored across a distributed network, ensuring tamper-proof data integrity and protection against unauthorized access. The Healthcare Record Chain Management (H_RCM) framework introduces a paradigm shift, enabling seamless interoperability among healthcare providers while maintaining patient privacy through cryptographic techniques. Moreover, smart contracts are embedded within the blockchain streamline administrative processes, automating tasks such as insurance claims and billing. As a result, the adoption of this innovative approach not only enhances data security and interoperability but also fosters trust among stakeholders, ultimately improving the quality and accessibility of healthcare services. The findings of the system were validated using real-life scenarios of various use cases and compared to the conventional health record system.