Analysis of Embedded System's Functional Requirement using BERT-based Name Entity Recognition for Extracting IO Entities

抄録

The functional testing specification is usually designed based on the IO entities' recognition from the embedded system's functional requirement sentences. However, it is hard for the software testing engineers to ably recognize the appropriate IO entities from the functional requirement sentences without clearly indicated entities and much experience with the domain knowledge. The conventional rule-based methods of extracting IO entities are inapplicable when the requirement sentences drafted by humans become too semantically complex. Even though all the sentences keep aligned with the structure, it is still infeasible to manually hard-code each rule when those rules change from time to time without any explicit writing standard. With the successful application of artificial intelligent techniques in natural language processing (NLP), we propose a method that intelligently solves the issue of the entities recognition by using BERT (Bidirectional Encoder Representations from Transformers) based named entity recognition (NER) which is the technique of NLP to recognize the phrases having similar attributes in semantics. In this paper, we specifically focus on the issue of IO entities' recognition in the embedded systems that implement the inverter control function such as elevator and hybrid hydraulic excavator systems. Our evaluation result demonstrates that the best model variant fine-tuned on 829 sentences achieves more than 80% F-measure in recognizing the IO entities, and the model can provide applicable information for the improvement of industrial productivity in the target industries. Our contribution of this paper is to provide insight into the case whether the IO entities in the target system manages to be interpreted well by exploiting the BERT model with the sole reliance on the small size of exemplary IO entities data and three existing model variants pre-trained on large corpus open datasets with general language knowledge.