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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Most of the JavaScript code deployed in the wild has been minified, a processin which identifier names are replaced with short, arbitrary and meaninglessnames. Minified code occupies less space, but also makes the code extremelydifficult to manually inspect and understand. This paper presents Context2Name,a deep learningbased technique that partially reverses the effect ofminification by predicting natural identifier names for minified names. Thecore idea is to predict from the usage context of a variable a name thatcaptures the meaning of the variable. The approach combines a lightweight,token-based static analysis with an auto-encoder neural network that summarizesusage contexts and a recurrent neural network that predict natural names for agiven usage context. We evaluate Context2Name with a large corpus of real-worldJavaScript code and show that it successfully predicts 47.5% of all minifiedidentifiers while taking only 2.9 milliseconds on average to predict a name. Acomparison with the state-of-the-art tools JSNice and JSNaughty shows that ourapproach performs comparably in terms of accuracy while improving in terms ofefficiency. Moreover, Context2Name complements the state-of-the-art bypredicting 5.3% additional identifiers that are missed by both existing tools.

Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts