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User story clustering in agile development: a framework and an empirical study

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Abstract

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development process. User stories are the primary means of requirements collection and elicitation in the agile development. A project can involve a large amount of user stories, which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis, effective mapping to developed features, and efficient maintenance. Nevertheless, the current user story clustering is mainly conducted in a manual manner, which is time-consuming and subjective to human bias. In this paper, we propose a novel approach for clustering the user stories automatically on the basis of natural language processing. Specifically, the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story meta-model. The similarity of user stories is calculated, which can be used to generate the connected graph as the basis of automatic user story clustering. We evaluate the approach based on thirteen datasets, compared against ten baseline techniques. Experimental results show that our clustering approach has higher accuracy, recall rate and F1-score than these baselines. It is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile development. The study also highlights promising research directions for more accurate requirements elicitation.

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Acknowledgements

We thank anonymous reviewers for their thoughtful comments. This work was sponsored by the National Natural Science Foundation of China (Grant Nos. 62192731, 62192730, 62162051), the Australian Research Council Discovery Project (DP210102447), and the Fundamental Research Funds for the Central Universities (BLX202003).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China

    Bo Yang

  2. Engineering Research Center for Forestry Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, 100083, China

    Bo Yang

  3. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Bo Yang, Xiuyin Ma & Haoran Guo

  4. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot, 010020, China

    Chunhui Wang

  5. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Huai Liu

  6. Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, 100871, China

    Zhi Jin

  7. Institute of Software, School of Computer Science, Peking University, Beijing, 100871, China

    Zhi Jin

Authors
  1. Bo Yang
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  2. Xiuyin Ma
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  3. Chunhui Wang
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  4. Haoran Guo
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  5. Huai Liu
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  6. Zhi Jin
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Corresponding authors

Correspondence to Bo Yang or Zhi Jin.

Additional information

Bo Yang received the PhD degree in computer software and theory from the Beihang University, China. He is an associate professor at the School of Information Science and Technology, Beijing Forestry University, China. His research interests include deep learning, software testing, software fault localization, and software requirements analysis. He is a member of CCF.

Xiuyin Ma received the BEng degree from the North China University of Technology, China. Her research interests include software requirements analysis and software testing.

Chunhui Wang received her PhD degree in computer science from School of Electronics Engineering and Computer Science, Peking University, China in 2020. Currently, she is an associate professor at the School of Computer Science, Inner Mongolia Normal University, China. Her research interests include requirements engineering and collective intelligence based software engineering. She is a member of CCF.

Haoran Guo received the BEng degree from the North China University of Technology, China. His research interests include software fault localization and software testing.

Huai Liu received the PhD degree in software engineering from the Swinburne University of Technology, Australia. He is a senior lecturer in the Department of Computing Technologies, Swinburne University of Technology, Australia. He has worked as a lecturer at Victoria University and a research fellow at RMIT University Australia. His current research interests include software testing, cloud computing, and end-user software engineering.

Zhi Jin obtained her BSc from Zhejiang University, China in 1984, and PhD from National University of Defense Technology, China in 1992, respectively. She is a professor in School of Computer Science, Peking University (PKU), China and serves as the Deputy Director of High-Confidence Software Technologies (PKU), Ministry of Education, China since 2009. Her research interests include requirements engineering, knowledge engineering, and knowledge-based software engineering.

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Yang, B., Ma, X., Wang, C. et al. User story clustering in agile development: a framework and an empirical study. Front. Comput. Sci. 17, 176213 (2023). https://doi.org/10.1007/s11704-022-8262-9

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