Accuracy and rigor are vital indicators of the specification document, especially for the ARINC653 aviation industry standard. A high-quality standard or specification should clearly depict the system behaviors yet leave no fatal vulnerability. Formal verification could definitely help achieve this goal, but it requires intensive professional domain knowledge and overwhelming manpower. Recently, fast-growing natural language processing (NLP) techniques do well in harvesting knowledge extraction for the downstream tasks. However, since knowledge about an entity is scattered over heterogeneous contents (plain text, pseudocode, XML, etc.) for almost all such standard documents, a single content or not all contents cannot account for the entire knowledge. To this end, we propose a novel and practical approach to construct the Ontology of ARINC653 and extract the logical guards. Technically, we combine the NLP techniques with domainspecific naming and lexical rules for entity recognition in Ontology and then apply information extraction and relation formalization for relation extraction (in terms of guards). We evaluate the quality of our Ontology against that induced by the domain professor. We further apply this approach to the historical ARINC653 standards and evaluate the performance. Results show that our approach indeed helps construct knowledge integration and aid for specification understanding.

Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With the recent popularity of NLP technique, the state-of-the-art approach NoRBERT utilizes the pre-trained language model BERT and achieves a satisfactory performance. However, the dataset PROMISE used by the existing approaches for this problem consists of only hundreds of requirements that are outdated according to today’s technology and market trends. Besides, the NLP technique applied in these approaches might be obsolete. In this paper, we propose an approach of prompt learning for requirement classification using BERT-based pretrained language models (PRCBERT), which applies flexible prompt templates to achieve accurate requirements classification. Experiments conducted on two existing small-size requirement datasets (PROMISE and NFR-Review) and our collected large-scale requirement dataset NFR-SO prove that PRCBERT exhibits moderately better classification performance than NoRBERT and MLM-BERT (BERT with the standard prompt template). On the de-labeled NFR-Review and NFR-SO datasets, Trans_PRCBERT (the version of PRCBERT which is fine-tuned on PROMISE) is able to have a satisfactory zero-shot performance with 53.27% and 72.96% F1-score when enabling a self-learning strategy.