  Despite the continuous efforts in improving both the effectiveness and
efficiency of code search, two issues remained unsolved. First, programming
languages have inherent strong structural linkages, and feature mining of code
as text form would omit the structural information contained inside it. Second,
there is a potential semantic relationship between code and query, it is
challenging to align code and text across sequences so that vectors are
spatially consistent during similarity matching. To tackle both issues, in this
paper, a code search model named CSSAM (Code Semantics and Structures Attention
Matching) is proposed. By introducing semantic and structural matching
mechanisms, CSSAM effectively extracts and fuses multidimensional code
features. Specifically, the cross and residual layer was developed to
facilitate high-latitude spatial alignment of code and query at the token
level. By leveraging the residual interaction, a matching module is designed to
preserve more code semantics and descriptive features, that enhances the
adhesion between the code and its corresponding query text. Besides, to improve
the model's comprehension of the code's inherent structure, a code
representation structure named CSRG (Code Semantic Representation Graph) is
proposed for jointly representing abstract syntax tree nodes and the data flow
of the codes. According to the experimental results on two publicly available
datasets containing 540k and 330k code segments, CSSAM significantly
outperforms the baselines in terms of achieving the highest SR@1/5/10, MRR, and
NDCG@50 on both datasets respectively. Moreover, the ablation study is
conducted to quantitatively measure the impact of each key component of CSSAM
on the efficiency and effectiveness of code search, which offers the insights
into the improvement of advanced code search solutions.
