Software and symmetric testing methodologies are primarily used in detecting software defects, but these testing methodologies need to be optimized to mitigate the wasting of resources. As mobile applications are becoming more prevalent in recent times, the need to have mobile applications that satisfy software quality through testing cannot be overemphasized. Testing suites and software quality assurance techniques have also become prevalent, which underscores the need to evaluate the efficacy of these tools in the testing of the applications. Mutation testing is one such technique, which is the process of injecting small changes into the software under test (SUT), thereby creating mutants. These mutants are then tested using mutation testing techniques alongside the SUT to determine the effectiveness of test suites through mutation scoring. Although mutation testing is effective, the cost of implementing it, due to the problem of equivalent mutants, is very high. Many research works gave varying solutions to this problem, but none used a standardized dataset. In this research work, we employed a standard mutant dataset tool called MutantBench to generate our data. Subsequently, an Abstract Syntax Tree (AST) was used in conjunction with a tree-based convolutional neural network (TBCNN) as our deep learning model to automate the classification of the equivalent mutants to reduce the cost of mutation testing in software testing of android applications. The result shows that the proposed model produces a good accuracy rate of 94%, as well as other performance metrics such as recall (96%), precision (89%), F1-score (92%), and Matthew’s correlation coefficients (88%) with fewer False Negatives and False Positives during testing, which is significant as it implies that there is a decrease in the risk of misclassification.

Mobile applications are becoming increasingly used to achieve various computing needs. Hence, it is essential to guarantee quality of the applications. Software testing has been the main activity for ensuring the quality of software, however, it is a critical and costly activity. Furthermore, code coverage is commonly used as metric for verifying the effectiveness of testing technique. However, numerous researchers and experts claimed that considering code coverage only to ascertain the testing quality is not enough to ensure quality of apps. In view of this, mutation testing has been introduced to complement the procedure by assuring that apps behave as expected and are released free of faults. Several techniques/tools were proposed based on different syntax and mutation operators. This study presents a comparative study of six (6) mutation techniques/tools for Android applications to highlight their strengths and weakness which will give an insight to researchers on the directions for further research.

The increasing demand for efficient energy management and privacy concerns poses challenges for smart grids in developing regions. This study introduces a Federated AIoT Framework integrating Federated Learning (FL) with IoT, leveraging a Federated Graph Attention Network (FeGAN) to enhance Nigeria's smart grid. The framework decentralizes model training, improving grid efficiency while ensuring data privacy. Results show a 92% accuracy in grid optimization, 89% accuracy in energy theft detection, and a 15% reduction in energy losses. Additionally, it demonstrated superior scalability and privacy-preserving mechanisms, achieving an F1 score of 0.91 for grid efficiency and 0.86 for theft detection. The Matthews Correlation Coefficient (MCC) for privacy reached 0.95, underscoring robust security. Federated Learning creates an opportunity for conducting secure smart grid management with scalability together with privacy compliance in resource-limited settings. Research expansion will target wider implementation within various power systems to integrate renewable power generation.

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is transforming precision agriculture by enabling accurate, real-time detection of crop diseases while ensuring data sovereignty. The Federated AIoT framework, combined with Graph Attention Networks (GATs), processes decentralized sensor and image data locally, addressing privacy concerns and supporting sustainable practices. Using federated learning, the model aggregates local updates from each farm to build a global model that benefits from diverse environments without compromising individual data ownership. Evaluated on the PlantVillage dataset and synthetic data, the framework achieved 90% accuracy, 90% precision, 75% recall, an F1-score of 81%, and a Matthews Correlation Coefficient (MCC) of 67%, indicating strong early disease detection performance. This approach empowers farmers with data control and offers scalability across varied agricultural contexts, with future research focused on improving real-time scalability in resource-limited settings.

Crop diseases are incessant and significant challenges to global food security. Conventional disease control means are still utilized as the primary mitigation model, but they fall short at providing quick and precision-based responses that are required for quick outbreak containment, resulting in significant yield losses. New advances in Artificial Intelligence of Things (AIoT) technologies currently enabled novel capabilities to predict activities and initiate early-stage interventions in disease progression. This longitudinal scoping review, organized according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol, examined 100 peer-reviewed articles from IEEE Xplore, PubMed, Springer, Elsevier, and MDPI to investigate AIoT applications in the prediction and control of crop diseases and assess the quality of articles published between 2019 and 2024. The literature reviewed indicated a variety of predictive models in farming that fused AI and IoT. Notably, the deployment of federated learning was suggested as a solution to minimize the risk of privacy breaches by training models using locally stored data whose heterogeneity allows them to avoid sharing sensitive on-farm data. The availability of large and standardized datasets is limited. Currently, the cost of deploying the systems, especially in smallholder agriculture, is a problem, although empirical evidence has shown that AIoT systems can result in significant gains in prediction accuracy (85%–98%). Scalable, explainable, and interpretable AIoT systems; robust benchmark datasets; and detailed system architectures are relevant to disentangle system impacts. The key to large-scale adoption and lasting impact will be the reduction of the deployment costs and the integration of more cutting-edge technologies and programs to offer training to farmers. Received: 12 September 2024 | Revised: 31 December 2024 | Accepted: 13 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Muhammad Bello Kusharki: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing. Muhammad Muktar Liman: Methodology, Validation, Formal analysis, Investigation, Resources, Supervision. Bilkisu Muhammad-Bello: Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization, Funding acquisition. Nachamada Blamah: Validation, Resources, Supervision. Moses Timothy: Validation, Resources.