Automation of error detection in code using machine learning

The objective of this study was to develop approaches for the automated detection of coding errors through machine learning algorithms. The research examined five primary approaches: classification using decision trees, sequence analysis with recurrent neural networks, anomaly detection through clustering algorithms, a generative approach with transformers, and deep learning using convolutional neural networks. Each approach was evaluated on a five-point scale based on a systematic analysis of advantages and disadvantages, considering performance metrics. The results included examples of the implementation of these approaches, an analysis of their strengths and weaknesses, and assessments of their effectiveness. Transformers demonstrated high accuracy in complex cases, effectively processing large volumes of data and identifying errors in intricate code structures. This approach received a rating of 5 due to its high accuracy and efficiency in handling large and complex datasets. Decision tree algorithms, despite their speed and simplicity, had limited effectiveness in largescale tasks, particularly for complex software structures. Meanwhile, clustering algorithms proved versatile in anomaly detection, though their accuracy depended on the correct selection of clustering parameters. These algorithms received a rating of 3 due to their limited effectiveness in complex tasks and scalability issues. The approach based on recurrent neural networks showed good results in sequence analysis but was sensitive to long sequences and the vanishing gradient effect, which reduced its accuracy. Convolutional neural networks efficiently handled visual representations of code but had limited capability in considering sequence context. Neural network-based approaches received a rating of 4, as they are effective in specific tasks but have limitations related to resource consumption and contextual analysis. Thus, the results confirmed that for automated error detection in large and complex programs, the most effective approach is the use of generative models, such as transformers, which can process substantial data volumes with high accuracy