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

Code embedding is a keystone in the application of machine learning onseveral Software Engineering (SE) tasks. To effectively support a plethora ofSE tasks, the embedding needs to capture program syntax and semantics in a waythat is generic. To this end, we propose the first self-supervised pre-trainingapproach (called GraphCode2Vec) which produces task-agnostic embedding oflexical and program dependence features. GraphCode2Vec achieves this via asynergistic combination of code analysis and Graph Neural Networks.GraphCode2Vec is generic, it allows pre-training, and it is applicable toseveral SE downstream tasks. We evaluate the effectiveness of GraphCode2Vec onfour (4) tasks (method name prediction, solution classification, mutationtesting and overfitted patch classification), and compare it with four (4)similarly generic code embedding baselines (Code2Seq, Code2Vec, CodeBERT,GraphCodeBERT) and 7 task-specific, learning-based methods. In particular,GraphCode2Vec is more effective than both generic and task-specificlearning-based baselines. It is also complementary and comparable toGraphCodeBERT (a larger and more complex model). We also demonstrate through aprobing and ablation study that GraphCode2Vec learns lexical and programdependence features and that self-supervised pre-training improveseffectiveness.

GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses