A Study on Development Method for BERT-based False Alarm Classification Model in Weapon System Software Static Test

Hyoju Nam, Insub Lee, Namhoon Jung, Seongyun Jeong, Kyutae Cho, Sungkyu Noh

http://doi.org/10.5626/JOK.2024.51.7.620

Recently, as the size and complexity of software in weapon systems have increased, securing the reliability and stability is required. To achieve this, developers perform static and dynamic reliability testing during development. However, a lot of false alarms occur in static testing progress that cause wasting resources such as time and cost for reconsider them. Recent studies have tried to solve this problem by using models such as SVM and LSTM. However, they have a critical limitation in that these models do not reflect correlation between defect code line and other lines since they use Word2Vec-based code embedding or only code information. The BERT-based model learns the front-to-back relationship between sentences through the application of a bidirectional transformer. Therefore, it can be used to classify false alarms by analyzing the relationship between code. In this paper, we proposed a method for developing a false alarm classification model using a BERT-based model to efficiently analyze static test results. We demonstrated the ability of the proposed method to generate a dataset in a development environment and showed the superiority of our model.