Context: The volume of log files is massive as they contain vital information about the application’s behavior; they map out broad parts of the application, allowing us to understand how every component behaves, whether normally or abnormally. As a result, it is critical to examine the log files to see if the system is deviating from its usual path. Because they are so large, it is difficult for the developer to identify each and every error. So, to overcome this problem we developed a machine-learning model to detect types of errors in log files with minimal manual effort. 

Objectives: The main objective is to discover errors in log files throughout the testing and production phases so that the application behaves properly. We intend to detect errors by training the module with relevant datasets and teaching the model to differentiate between the types of errors like error, debug, info, fail, etc. caused when the application is tested or operated during the production phase. 

Methods: We employ machine learning techniques like SVM and multinomial naive Bayes as well as long-short-term memory (LSTM) networks, which are a sort of re-current neural network capable of learning order dependency in the prediction of sequences, which is appropriate for our use case. These techniques are used to de- termine whether errors such as assert, fail, error, and warning were generated. Then we used verdict generation machine learning techniques to generate the verdict from the error log messages. 

Results: The results indicated that, instead of manually detecting errors, we can easily discover and fix them by integrating machine learning and classification methods, making it easier to move the application to production. 

Conclusion: The results will assist developers in identifying the errors without having to manually examine the log file row by row. This approach has the potential to reduce the need for additional human efforts to examine log files for errors and can determine the type of error that occurred in the specific row that caused the application to diverge from its typical flow.