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Semantic similarity-based program retrieval: a multi-relational graph perspective

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Conclusion

In this paper, we formulate the program retrieval problem as a graph similarity problem. This is achieved by first explicitly representing queries and program snippets as AMR and CPG, respectively. Then, through intra-level and inter-level attention mechanisms to infer fine-grained correspondence by propagating node correspondence along the graph edge. Moreover, such a design can learn correspondence of nodes at different levels, which were mostly ignored by previous works. Experiments have demonstrated the superiority of USRAE.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62192733 and 62192730).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Qianwen Gou, Yunwei Dong & YuJiao Wu

  2. School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129, China

    Qiao Ke

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  1. Qianwen Gou
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  2. Yunwei Dong
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Correspondence to Yunwei Dong.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

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Gou, Q., Dong, Y., Wu, Y. et al. Semantic similarity-based program retrieval: a multi-relational graph perspective. Front. Comput. Sci. 18, 183209 (2024). https://doi.org/10.1007/s11704-023-2678-8

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  • DOI: https://doi.org/10.1007/s11704-023-2678-8