International Journal of Software Computing and Testing
About the Journal
International Journal of Software Computing and Testingwelcomes research papers and editorial reviews concerning the development in software computing and testing. Fuzzy computing, hybrid methods, immunological computing and morphic computing are a few topics that are included in the scope and focus of the journal.
All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance.
Journal at a Glance
Latest Articles
A Secured Check-In and Check-Out System Using Private Key Authentication and QR Code Verification Techniques for Daycare Management
Abstract: The safety and security of children in daycare centers remain a critical concern for parents and caregivers. Child daycare centers play a fundamental role in providing a safe and nurturing environment for children while their parents or guardians engage in work and other responsibilities. However, ensuring the safety and security of children in these facilities…
The Role of Artificial Intelligence in Mental Health: New Approaches for Diagnosis and Treatment
Abstract: Artificial intelligence (AI) is increasingly reshaping mental health care by supporting early identification of mental health conditions, improving diagnostic accuracy, and enabling personalized intervention strategies for disorders such as depression, anxiety, and post-traumatic stress disorder. Advanced AI technologies, including machine learning algorithms and natural language processing techniques, allow analysis of large-scale behavioral, textual, and speech-based…
Hybrid Evolutionary Algorithm for Automated Test Case Generation in Large-Scale Software Systems
Abstract: Automated test case generation is a crucial activity for ensuring the reliability, robustness, and quality of large-scale software systems. Conventional test generation techniques often face significant challenges in handling scalability, achieving high code coverage, and adapting to complex and heterogeneous software architectures. Evolutionary algorithms have emerged as effective solutions, capable of exploring large search spaces…
A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction
Abstract: The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a…
AI-Assisted Code Generation: Integrating Natural Language Models with Compiler Frameworks for Enhanced Software Development
Abstract: The rapid evolution of Artificial Intelligence (AI) has profoundly transformed contemporary software engineering practices, particularly through the emergence of AI-assisted code generation driven by large-scale natural language models (NLMs). These models, trained on extensive repositories of source code and technical documentation, enable developers to generate, modify, refactor, and optimize source code directly from natural language…
The Role of Artificial Intelligence in Mental Health: New Approaches for Diagnosis and Treatment
Abstract: Artificial intelligence (AI) is increasingly reshaping mental health care by supporting early identification of mental health conditions, improving diagnostic accuracy, and enabling personalized intervention strategies for disorders such as depression, anxiety, and post-traumatic stress disorder. Advanced AI technologies, including machine learning algorithms and natural language processing techniques, allow analysis of large-scale behavioral, textual, and speech-based…