ReqGen: Keywords-Driven Software Requirements Generation

Ziyan Zhao, Li Zhang, Xiaoli Lian (), Xiaoyun Gao, Heyang Lv and Lin Shi
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Ziyan Zhao: The State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing 100191, China
Li Zhang: The State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing 100191, China
Xiaoli Lian: The State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing 100191, China
Xiaoyun Gao: The State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing 100191, China
Heyang Lv: The State Key Laboratory of Software Development Environment (SKLSDE), Beihang University, Beijing 100191, China
Lin Shi: Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Mathematics, 2023, vol. 11, issue 2, 1-22

Abstract: Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen , which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords’ (phrases’) inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.

Keywords: software requirements generation; knowledge injection; requirements syntax (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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