English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To obtain code snippets for reuse, programmers prefer to search for relateddocuments, e.g., blogs or Q&A, instead of code itself. The major reason is dueto the semantic diversity and mismatch between queries and code snippets. Deeplearning models have been proposed to address this challenge. Compared withapproaches using information retrieval techniques, deep learning models do notsuffer from the information loss caused by refining user intention intokeywords. However, the performance of previous works is not satisfactorybecause they ignore the importance of code structure. When the semantics ofcode (e.g., identifier names, APIs) are ambiguous, code structure may be theonly feature for the model to utilize. In that case, previous works relearn thestructural information from lexical tokens of code, which is extremelydifficult for a model without any domain knowledge. In this work, we proposePSCS, a path-based neural model for semantic code search. Our model encodesboth the semantics and structures of code represented by AST paths. We trainand evaluate our model over 330k-19k query-function pairs, respectively. Theevaluation results demonstrate that PSCS achieves a SuccessRate of 47.6% and aMean Reciprocal Rank (MRR) of 30.4% when considering the top-10 results with amatch. The proposed approach significantly outperforms both DeepCS, the firstapproach that applies deep learning to code search task, and CARLCS, astate-of-the-art approach that introduces a co-attentive representationlearning model on the basis of DeepCS. The importance of code structure isdemonstrated with an ablation study on code features, which enlightens modeldesign for further studies.

PSCS: A Path-based Neural Model for Semantic Code Search