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

Learning-based techniques, especially advanced Large Language Models (LLMs)for code, have gained considerable popularity in various software engineering(SE) tasks. However, most existing works focus on designing betterlearning-based models and pay less attention to the properties of datasets.Learning-based models, including popular LLMs for code, heavily rely on data,and the data's properties (e.g., data distribution) could significantly affecttheir behavior. We conducted an exploratory study on the distribution of SEdata and found that such data usually follows a skewed distribution (i.e.,long-tailed distribution) where a small number of classes have an extensivecollection of samples, while a large number of classes have very few samples.We investigate three distinct SE tasks and analyze the impacts of long-taileddistribution on the performance of LLMs for code. Our experimental resultsreveal that the long-tailed distribution has a substantial impact on theeffectiveness of LLMs for code. Specifically, LLMs for code perform between30.0\% and 254.0\% worse on data samples associated with infrequent labelscompared to data samples of frequent labels. Our study provides a betterunderstanding of the effects of long-tailed distributions on popular LLMs forcode and insights for the future development of SE automation.

The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models