## 2 Background and Literature Review  
In the source code, a single line may have reliance on the preceding lines, making it difficult to evaluate complex source code by any conventional language model. The LSTM based language model is a promising method for source code error assessment and classification. Information and communication technology has become an influential economic catalyst. A huge amount of source code is written and compiled globally. AI can be applied to assess source code errors. AI-based language models are often used for source code assessment and classification to obtain human-like responses. Many researchers have used AI-based models to detect source code errors in software engineering and programming education. [PERSON] et al. [10] proposed a source code correction method based on LSTM using code segment similarities. The study leveraged the sequence-to-sequence (seq2 seq) neural network model with natural language processing tasks for the code correction process. Another study [12] proposed a deep software language model based on RNNs. The experimental results showed that the model outperforms traditional language models such as n-gram and cache-based n-gram in a Java corpus. The software language model shows great promise in the field of software engineering. [PERSON] et al. [14] proposed an LSTM-based model for programming education where the model predicts the next word by analyzing incomplete source code. Novice programmers often struggle to write a complete program from scratch. To help them, the model predicts the next word to complete a program. The LSTM-based model achieved a high degree of prediction accuracy. Fault detection in source code has become an important research topic [1]. In one study [15], source code defect prediction was performed based on churn metrics combined with source code dependencies. In another [16], an extensive analysis of metrics and static code attributes was conducted for error prediction. [PERSON] et al. [17] selected suitable features by employing a naive Bayes classifier. [PERSON] et al. [18] introduced a vocabulary learning model that calculates the incorrect classification cost for the prediction of source code defects. Various ML approaches [19; 20; 21] have been proposed for classification, recommendation, and estimation problems. [PERSON] et al. [22] presented an ML-based language model for classifying source code snippets based on the programming language. In their work, a multinomial naive Bayes classifier was applied and code snippets from the website Stack Overflow were used as experimental data. [PERSON] and [PERSON] [23] proposed a hierarchical model that uses convolutional neural networks (CNNs) and LSTM for sentiment analysis in software engineering. This analysis model outperforms reference state-of-the-art models. [PERSON] et al. [24] classified archived source code by type of programming language using an LSTM network. Empirical results showed that the LSTM network outperformed the naive Bayes classifier and linguist classifier. [PERSON] and [PERSON] [25] presented a method for the automatic generation of fill-in-the-blank problems for novice programmers using k-means clustering and the bidirectional LSTM model. The k-means clustering method is used to select ideal source code from an online judge system and the code to be made blank (to be filled with appropriate words using the bidirectional LSTM model). [PERSON] et al. [26] presented a model called Tree-LSTM where an LSTM network works like a tree. The model evaluates the tasks of prediction of semantic relatedness based on sentence pairs and sentiment classification. [PERSON] and [PERSON] [27] presented a survey-based analysis that focused on what type of compiler message helps novice programmers identify errors and what actions should be taken regarding source code errors. They experimentally showed which type of message helps most. [PERSON] and [PERSON] [28] proposed a learning path recommendation system for novice students based on their desired learning ability chart.