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Detecting coreferent entities in natural language requirements

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Abstract

Requirements are usually written in natural language and evolve continuously during the process of software development, which involves a large number of stakeholders. Stakeholders with diverse backgrounds and skills might refer to the same real-world entity with different linguistic expressions in the natural-language requirements, resulting in requirement inconsistency. We define this phenomenon as Entity Coreference (EC) in the Requirement Engineering (RE) area. It can lead to misconception about technical terminologies, and harm the readability and long-term maintainability of the requirements. In this paper, we propose a DEEP context-wise method for entity COREFerence detection, named DeepCoref. First, we truncate corresponding contexts surrounding entities. Then, we construct a deep context-wise neural network for coreference classification. The network consists of one fine-tuning BERT model for context representation, a Word2Vec-based network for entity representation, and a multi-layer perceptron in the end to fuse and make a trade-off between two representations. Finally, we cluster and normalize coreferent entities. We evaluate our method, respectively, on coreference classification and clustering with 1853 industry data on 21 projects. The former evaluation shows that DeepCoref outperforms three baselines with average precision and recall of 96.10% and 96.06%, respectively. The latter evaluation on six metrics shows that DeepCoref can cluster coreferent entities more accurately. We also conduct ablation experiments with three variants to demonstrate the performance enhancement brought by different components of neural network designed for coreference classification.

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Notes

  1. Note that entities in this research are ready-made, and entity extraction is out of the scope of this research.

  2. https://github.com/MeloFancy/DeepCoref.

  3. https://dumps.wikimedia.org.

  4. https://github.com/huggingface/transformers.

  5. https://pytorch.org.

  6. It is one of the world’s top five hundred commercial banks.

  7. https://github.com/cbaziotis/ekphrasis.

  8. https://github.com/doukremt/distance.

  9. https://scikit-learn.org.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China under grant No. 2018YFB1403400, the National Science Foundation of China under grant No. 61802374, No. 61432001, No. 61602450. This work is also supported by China Merchants Bank Intelligent Software Research and Development Effectiveness Research Project.

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Authors and Affiliations

  1. Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Yawen Wang, Lin Shi, Mingyang Li & Qing Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Yawen Wang, Lin Shi, Mingyang Li & Qing Wang

  3. State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Qing Wang

  4. Science & Technology on Integrated Infomation System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Qing Wang

  5. School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia

    Yun Yang

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Wang, Y., Shi, L., Li, M. et al. Detecting coreferent entities in natural language requirements. Requirements Eng 27, 351–373 (2022). https://doi.org/10.1007/s00766-022-00374-8

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