Li Li (0000-0003-2990-1614)

Personal information

Biography

Dr. Li Li is a Senior lecturer (a.k.a., Associate Professor) and a PhD supervisor at Monash University, Australia. He received his PhD degree in computer science from the University of Luxembourg in 2016. He has published over 60 research papers at prestigious conferences such as ICSE, ESEC/FSE, ASE, ISSTA, POPL, PLDI, etc. and prestigious journals such as ACM TOSEM and IEEE TSE, TIFS, TDSC, etc.

Li was named as one of the Top-5 most impactful earlier career software engineering researchers in the world. His research has received various awards, including the prestigious ARC Discovery Early Career Researcher Award (DECRA in short), an ACM Distinguished Paper Award at PLDI 2021, a Best Student Paper Award at WWW 2020, an ACM Distinguished Paper Award at ASE 2018, a FOSS Impact Paper Award at MSR 2018, and a Best Paper Award at the ERA track of IEEE SANER 2016.

Li is an active member of the software engineering and security community serving as a reviewer for many top-tier conferences and journals such as ASE, ICSME, SANER, TSE, TOSEM, TIFS, TDSC, TOPS, EMSE, JSS, IST, etc.

Activities

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Beihang University: Beijing, CN

2023 to present

Employment

Li Li

https://orcid.org/0000-0003-2990-1614

Personal information

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Biography

Activities

Employment (3)

Beihang University: Beijing, CN

Monash University: Melbourne, Victoria, AU

University of Luxembourg: Luxembourg, LU

Education and qualifications (1)

University of Luxembourg: Luxembourg, LU

Works (50 of 63)

Better Supporting Human Aspects in Mobile eHealth Apps: Development and Validation of Enhanced Guidelines

Software Engineering for OpenHarmony: A Research Roadmap

Don’t Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems

Demystifying the Evolution of Android Malware Variants

LLM for Mobile: An Initial Roadmap

Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning

Automatically Recommend Code Updates: Are We There Yet?

Demystifying React Native Android Apps for Static Analysis

DynaMO: Protecting Mobile DL Models through Coupling Obfuscated DL Operators

GlitchProber: Advancing Effective Detection and Mitigation of Glitch Tokens in Large Language Models

Towards Demystifying Android Adware: Dataset and Payload Location

Improving Logic Bomb Identification in Android Apps via Context-Aware Anomaly Detection

Large Language Models for Software Engineering: A Systematic Literature Review

AI Coders Are among Us: Rethinking Programming Language Grammar towards Efficient Code Generation

API Misuse Detection via Probabilistic Graphical Model

FDI: Attack Neural Code Generation Systems through User Feedback Channel

Model-less Is the Best Model: Generating Pure Code Implementations to Replace On-Device DL Models

Fusing Code Searchers

Smart Software Analysis for Software Quality Assurance

Neural Library Recommendation by Embedding Project-Library Knowledge Graph

On the Reliability and Explainability of Language Models for Program Generation

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Understanding the quality and evolution of Android app build systems

Are Your Android App Analyzers Still Relevant?

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools

Investigating White-Box Attacks for On-Device Models

Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models

When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference

How Are Multilingual Systems Constructed: Characterizing Language Use and Selection in Open-Source Multilingual Software

Developer and End-User Perspectives on Addressing Human Aspects in Mobile eHealth Apps

Automatically Detecting Incompatible Android APIs

Taming Android Fragmentation through Lightweight Crowdsourced Testing

CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models

LazyCow: A Lightweight Crowdsourced Testing Tool for Taming Android Fragmentation

Natural Language to Code: How Far Are We?

Reliable Fix Patterns Inferred from Static Checkers for Automated Program Repair

The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches

Why Is Static Application Security Testing Hard to Learn?

A Deep Dive into the Featured iOS Apps

How Does Visualisation Help App Practitioners Analyse Android Apps?

Are Mobile Advertisements in Compliance with App’s Age Group?

Demystifying Hidden Sensitive Operations in Android Apps

APIMatchmaker: Matching the Right APIs for Supporting the Development of Android Apps

A Systematic Assessment on Android Third-Party Library Detection Tools

Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility Issues

Research on Third-Party Libraries in Android Apps: A Taxonomy and Systematic Literature Review

How secondary school girls perceive Computational Thinking practices through collaborative programming with the micro:bit

Developing Mobile Applications Via Model Driven Development: A Systematic Literature Review

ANCHOR: locating android framework-specific crashing faults

Icon2Code: Recommending code implementations for Android GUI components

Peer review (129 reviews for 24 publications/grants)