I am working at Ant Group as a Senior Staff Engineer and Professoriate Senior Engineer. Additionally, I am an Adjunct Senior Lecturer at the University of New South Wales (UNSW) (UNSW personal website) and an Adjunct Professorate Researcher at the Chinese Academy of Sciences. I also serve as an Industrial PhD Mentor at Zhejiang University.

I earned my PhD degree from UNSW under supervised by Scientia Professor Jingling Xue. And then I worked as a Research Associate at Compiler Research Group and cooperatively developed an inter-procedural dependence analysis tool SVF that is maintained by Dr. Yulei Sui.

Before joining Ant, I worked at Huawei as a Technical Expert of AI compiler. During the period, I am the founder of MindSpore/AKG (GitHub, Gitee) which is an open-sourced AI compiler for Huawei Ascend 910.

I lead the software engineering team responsible for maintaining and developing Ant Group’s DevSecOps. Our primary focus lies in developing practical program analysis platforms tailored for complex and extensive industrial codebases.

Recently, our team has been dedicated to integrating Large Language Models (LLMs) into real-world industrial scenarios, aiming to enhance the effectiveness and efficiency of software development. The pretrained code LLM, CodeFuse, which was trained by our team at AntGroup, has been open-sourced on GitHub and HuggingFace. The technical report has been published on link. We also open-sourced CodeFuse-Query that is a scalable datalog-based static program analyser to assist the CodeFuse pre-train.

Research Interests

• Program Analysis, Software Engineering and Security, AI4SE
• Programming Language and Compilation Techniques
• Parallel Programming and Optimizations for Heterogeneous Systems

News

My team is looking for self-motivated technical experts, who are interested in program analysis, software security, AI4SE. Please no hesitate to contact me (dipeng dot dp at antgroup dot com).

• 2024-10: Our paper, titled “Datalog-Based Language-Agnostic Change Impact Analysis for Microservices” has been accepted by ICSE 2025. This paper introduces the application of our datalog-based program analysis system CodeFuse-Query on the complex change impact analysis of microservices.
• 2024-10: “MiniChecker: Detecting Data Privacy Risk of Abusive Permission Request Behavior in Mini-Programs” earned the distinguished paper award of ASE’24.
• 2024-08: Our paper, titled “Understanding Code Changes Practically with Small-Scale Language Models” has been accepted by ASE 2024. Congratulations to Cong Li, the first author of this work, who joined the Ant Postdoctoral Programme one year ago! Well done! And another paper from our team, titled “MiniChecker: Detecting Data Privacy Risk of Abusive Permission Request Behavior in Mini-Programs”, has been accepted by ASE 2024 too.
• 2024-07: Our paper, titled “Tumbling Down the Rabbit Hole: How do Assisting Exploration Strategies Facilitate Grey-box Fuzzing?” has been accepted by ICSE 2025.
• 2024-06: Our paper, titled “Scaling Abstraction Refinement for Program Analyses in Datalog Using Graph Neural Networks”， which introduced an approach to combining graph neural networks and constrait solving to improve program analyses, has been accepted by OOPSLA 2024.
• 2024-05: Our team won 2024 T-Star Award, Ant Group’s highest technological award. This award celebrates our innovative contributions to CodeFuse.
• 2024-04: Our paper, titled “Finding and Understanding Defects in Static Analyzers by Constructing Automated Oracles”， which was cooperated with East China Normal University, ETH Zurich, and University of New South Wales, has been accepted by FSE 2024.
• 2024-03: Our paper, titled “Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis”， which was cooperated with the Chinese Academy of Sciences and Nanjing University, has been accepted by IEEE Transactions on Software Engineering (TSE).
• 2023-12: Our two papers “CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model” and “MicroFuzz: An Efficient Fuzzing Framework for Microservices” are accepted by ICSE-SEIP 2024.
• 2023-12: Good News: we opened our datalog-based static program analyser CodeFuse-Query.
• 2023-11: Our paper “Modeling the Interplay between Loop Tiling and Fusion in Optimizing Compilers using Affine Relations” was accepted by ACM Transactions on Computer Systems (TOCS). We are so fortunate to be the authors of one of the last round of TOCS publications since this journal is no longer accepting submissions and will cease publication after 2023.
• 2023-09: The pretrained code LLM, CodeFuse, which was trained by our team at AntGroup, has been open-sourced on GitHub and HuggingFace. The technical report has been published on link.
• 2023-04: Our paper “Hybrid Inlining: A Framework for Compositional and Context-Sensitive Static Analysis” is accepted by ISSTA 2023.
• 2022-12: Our two papers “Incremental Call Graph Construction in Industrial Practice” and “Scalable Compositional Static Taint Analysis for Sensitive Data Tracing on Industrial Micro-Services” are accepted by ICSE-SEIP 2023.
• 2022-12: Our SAST and SCA products officially enter the Chinese Product and Manufacturer List of Software Supply Chain Security.
• 2022-10: I was invited to serve SANER 2023 as an industrial track PC member.
• 2022-02: I was invited to join the Industry Advisory Board of Monash University.
• 2022-01: Our two papers “Record and Replay of Online Traffic for Microservices with Automatic Mocking Point Identification” and “Field-based Static Taint Analysis for Industrial Microservice” are accepted by ICSE-SEIP 2022.
• 2021-12: I was invited to introduce ANT’s AIOps on CCF ChinaSoft 2021. link
• 2021-10: I was invited to present our unified program analysis framework on CCF CNCC 2021. link
• 2021-03: Our paper “AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations” is accepted by PLDI 2021.
• 2020-10: Our paper “Optimizing the Memory Hierarchy by Compositing Automatic Transformations on Computations and Data” gains Best Paper Nomination.
• 2020-07: Our paper “Optimizing the Memory Hierarchy by Compositing Automatic Transformations on Computations and Data” is accepted by MICRO-53.
• 2020-06: Our AKG is open-sourced. (GitHub, Gitee).