220 citations · 1.2k across the 49 of their papers we have counts for
7 papers · 2 filters
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
An Empirical Study of AI Techniques in Mobile Applications
Yinghua Li, Xueqi Dang, Haoye Tian +5
The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized servic…
A Comparative Study of Smartphone and Smart TV Apps
Yonghui Liu, Xiao Chen, Yue Liu +6
Context: Smart TVs have become one of the most popular television types. Many app developers and service providers have designed TV versions for their smartphone applications. Desp…
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Haoye Tian, Xunzhu Tang, Andrew Habib +5
In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.…
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Weiguo Pian, Hanyu Peng, Xunzhu Tang +5
Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code datase…
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Haoye Tian, Kui Liu, Yinghua Li +7
A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Bec…