Automated Extraction of Requirement Entities by Leveraging LSTM-CRF and Transfer Learning

Abstract

Requirement entities, "explicit specification of concepts that define the primary function objects", play an important role in requirement analysis for software development and maintenance. It is a labor-intensive activity to extract requirement entities from textual requirements, which is typically done manually. A few existing studies propose automated methods to support key requirement concept extraction. However, they face two main challenges: lack of domain-specific natural language processing techniques and expensive labeling effort. To address the challenges, this study presents a novel approach named RENE, which employs LSTM-CRF model for requirement entity extraction and introduces the general knowledge to reduce the demands for labeled data. It consists of four phases: 1) Model construction, where RENE builds LSTM-CRF model and an isomorphic LSTM language model for transfer learning; 2) LSTM language model training, where RENE captures general knowledge and adapt to requirement context; 3) LSTM-CRF training, where RENE trains the LSTM-CRF model with the transferred layers; 4) Requirement entity extraction, where RENE applies the trained LSTM-CRF model to a new-coming requirement, and automatically extracts its requirement entities. RENE is evaluated using two methods: evaluation on historical dataset and user study. The evaluation on the historical dataset shows that RENE could achieve 79% precision, 81% recall, and 80% F1. The evaluation results from the user study also suggest that RENE could produce more accurate and comprehensive requirement entities, compared with those produced by engineers.