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Reflective feature location: knowledge in mind meets information in system

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Abstract

Locating code entities relevant to a feature (i.e., feature location) is an important task in software maintenance. Feature location is challenging due to the information gap between the developer’s knowledge about a feature and the feature’s implementation in the system. In this paper, we present a reflective feature location approach ReFLex to bridge this gap. ReFLex automatically computes a reflexion model between the developer’s logical view of a feature and the feature’s implemented view reverse-engineered from the code. It provides interactive reflection support for the developer to refine the logical view and the feature location results such that the logical view and the implemented view converge gradually in an iterative feature location process. We have implemented our approach as an Eclipse plugin and investigated the benefits and challenges in reflective feature location through a user study.

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Acknowledgements

This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2016YFB1000801) and National Natural Science Foundation of China (Grant No. 61370079).

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Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, 201203, China

    Xin Peng, Sen Pan, Wenyi Qian & Wenyun Zhao

  2. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 201203, China

    Xin Peng, Sen Pan, Wenyi Qian & Wenyun Zhao

  3. Research School of Computer Science, Australian National University, Acton, ACT, 2601, Australia

    Zhengchang Xing

  4. Department of Computer Science, Wayne State University, Detroit, MI, 48202, USA

    Václav Rajlich

Authors
  1. Xin Peng
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  2. Zhengchang Xing
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  3. Sen Pan
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  4. Wenyi Qian
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  5. Václav Rajlich
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  6. Wenyun Zhao
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Correspondence to Xin Peng.

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Conflict of interest The authors declare that they have no conflict of interest.

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Peng, X., Xing, Z., Pan, S. et al. Reflective feature location: knowledge in mind meets information in system. Sci. China Inf. Sci. 60, 072102 (2017). https://doi.org/10.1007/s11432-015-0860-9

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