most citedNatural Attack for Pre-trained Models of Code

163 citations · 258 across the 12 of their papers we have counts for

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cs.SE20225 cited

SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries

Hong Jin Kang, Pattarakrit Rattanukul, Stefanus Agus Haryono +4

Many modern software systems are enabled by deep learning libraries such as TensorFlow and PyTorch. As deep learning is now prevalent, the security of deep learning libraries is a…

cs.SE2022

Answer Summarization for Technical Queries: Benchmark and New Approach

Yang Chengran, Bowen Xu, Ferdian Thung +8

Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that exis…

cs.SE202220 cited

AutoPruner: Transformer-Based Call Graph Pruning

Thanh Le-Cong, Hong Jin Kang, Truong Giang Nguyen +4

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise.…

cs.SE2022

Compressing Pre-trained Models of Code into 3 MB

Jieke Shi, Zhou Yang, Bowen Xu +2

Although large pre-trained models of code have delivered significant advancements in various code processing tasks, there is an impediment to the wide and fluent adoption of these…

cs.SE20222 cited

An Exploratory Study on Code Attention in BERT

Rishab Sharma, Fuxiang Chen, Fatemeh Fard +1

Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer-based Pre-trained Language Models (PLM) trained on…

cs.SE202210 cited

On the Effectiveness of Pretrained Models for API Learning

Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Ferdian Thung +4

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit f…