A BERT and Topic Model Based Approach to reviews Requirements Analysis

Yang, Jun; Dou, Yajie; Xu, Xiangqian; Ma, Yong; Yue-jin, Tan

doi:10.1109/iscid52796.2021.00094

Cited by 2 publications

(1 citation statement)

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“…[19] utilized the technique of NER with both machine learning and deep learning method to recognize simple software-related entities in software requirement specification (SRS). [20], [21] classifies the application related reviews requirement into given categories with the BERT model for apps performance analysis. The result from [19] further shows that BERT outperformed the rest of machine learning-based approaches.…”

Section: Related Workmentioning

confidence: 99%

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

Chow

2023

Journal of Information Processing

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The functional testing specification is usually designed based on the IO entitiesʼ recognition from the embedded systemʼs 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 entitiesʼ 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.

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