Due to the availability of several social media platforms and their use in sending text messages, it is necessary to provide an easy and safe way to protect messages from being hacked especially in the presence of intruders and data thieves, and taking into consideration that most of those messages are confidential and personal, it is necessary to provide an easy and safe way to protect messages from being hacked. In this research paper, a simple and easy method of message cryptography will be proposed. The method divides a message into blocks with fixed sizes. The block size ranges from 2 to 60. The method uses a secret color image to generate an array with a size equal to the number of resulted blocks. The array will then be used as a private key. Each element of the private key will be used to calculate the number of rotation digits for the associated block in order to apply block rotation left operation. The proposed method will be examined using the parameter’s Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and throughput. The proposed method will be compared with other standard methods of message cryptography, such as Data Encryption Standard (DES), Triple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF). Experimental results show that the proposed method is secured enough based on using secret image, block size, and calculated Rotation Left Digits (RLD) for each block.

Unified Modeling Language (UML) diagrams serve as essential tools for visualizing system structure and behavior in software design. With the emergence of Large Language Models (LLMs) that automate various phases of software development, there is growing interest in leveraging these models for UML diagram generation. This study presents a comprehensive empirical investigation into the effectiveness of GPT-4-turbo in generating four fundamental UML diagram types: Class, Deployment, Use Case, and Sequence diagrams. We developed a novel rule-based prompt-engineering framework that transforms domain scenarios into optimized prompts for LLM processing. The generated diagrams were then synthesized using PlantUML and evaluated through a rigorous survey involving 121 computer science and software engineering students across three U.S. universities. Participants assessed both the completeness and correctness of LLM-assisted and human-created diagrams by examining specific elements within each diagram type. Statistical analyses, including paired t-tests, Wilcoxon signed-rank tests, and effect size calculations, validate the significance of our findings. The results reveal that while LLM-assisted diagrams achieve meaningful levels of completeness and correctness (ranging from 61.1% to 67.7%), they consistently underperform compared to human-created diagrams. The performance gap varies by diagram type, with Sequence diagrams showing the closest alignment to human quality and Use Case diagrams exhibiting the largest discrepancy. This research contributes a validated framework for evaluating LLM-generated UML diagrams and provides empirically-grounded insights into the current capabilities and limitations of LLMs in software modeling education.

In the rapidly evolving field of information technology education,integrating artificial intelligence (AI) and machine learning (ML) techniques presents opportunities and challenges. This empirical study investigates the application of tree-based ML techniques, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for software defect prediction in the context of IT education. We analyze nine publicly available NASA software defect datasets to compare the performance of these algorithms across multiple metrics, including accuracy, precision, recall, and ROC area. Our findings demonstrate that XGBoost consistently outperforms Random Forest, achieving near-perfect accuracy across most datasets. The paper explores how these advanced techniques can be responsibly integrated into software engineering (SE) education to enhance student learning while addressing concerns about potential over-reliance on AI tools. We discuss the implications of our results for IT education, emphasizing the need to balance the use of sophisticated AI technologies with the development of fundamental software assurance skills. Furthermore, we examine the role of AI in augmenting SE education, particularly in areas such as software assurance explanations, feature identification, and data augmentation.

Colored digital images are one of the most important types of digital data to be used in many vital applications, which require a safe way to protect them from hacking operations and the danger of intruders and data thieves. This paper presents an effective and safe method for storing digital colored images (CASDC). A high level of protection is provided through a complex secret key agreed upon between the sender and the receiver. The secret key consists of nine decimal digits (and can be increased as needed). These digits are processed to extract three values for each color of the three color channels. A left rotation process is performed for the value of each color to produce three new values, where an exclusion process is performed between them to obtain the encrypted value for the color. CASDC is evaluated against a wide range of images to calculate its throughput to show the extent to which this method fulfills encryption and decryption requirements. The Mean Square Error (MSE) values, Peak Signal to Noise Ratio (PSNR), and Correlation Coefficient for the three primary channels of the RGB coloring system were analyzed. The practical results of the proposed method are compared with other standard methods such as Data Encryption Standard (DES), Tripple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF). According to the obtained results, CASDC outperforms all standard methods in terms of efficiency by reducing the time of encryption and decryption and increasing the throughput of the corresponding process. Besides, CASDC is robust against breaks, as the attempts to break the private key will require hundreds of years in the best case.

Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.