Jiasi Shen

I am an Assistant Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology where I lead the HKUST Automated Reasoning and Transformation of Software research group.

My main research interests are in programming languages and software engineering. I aim to improve software development by automating tasks that currently require substantial manual engineering effort. My research focuses on developing automatic techniques that analyze, manipulate, and transform software. My broader interests include computer systems and security.

Before joining HKUST in 2023, I received a PhD degree from Massachusetts Institute of Technology, where I worked with Professor Martin Rinard. I have also received a master's degree from MIT and a bachelor's degree from Peking University.

Teaching

• COMP2711: Discrete Mathematical Tools for Computer Science (Spring 2024)
• COMP3021: Java Programming (Fall 2023)

Research

Software plays a central role in numerous aspects of human society. Current software development practices involve significant developer effort in all phases of the software life cycle, including the development of new software, detection and elimination of defects and security vulnerabilities in existing software, maintenance of legacy software, and integration of existing software into more contexts. My research goal is to automate software development tasks and enhance how people create, understand, and improve software. Towards this goal, I have developed automatic techniques that analyze, manipulate, and transform programs. Ongoing and past research projects include:

• Formal methods for automating the evolution and migration of software.
• Inferring programs via active learning: techniques for database-backed programs [PLDI'19] [TOPLAS'21] [Preprint1] , string-processing libraries [CCS'21] , data-structure accesses [Onward!'19] , and data storage/retrieval commands [Onward!'18] .
• Systematically augmenting programs with new capabilities: techniques for automatic parallelization [PPoPP'22] [Preprint2] , eliminating software supply-chain vulnerabilities [CCS'21] , eliminating SQL injection vulnerabilities [PLDI'19] [TOPLAS'21] [Preprint1] , augmenting front-end web interfaces [Onward!'18] , augmenting back-end databases [Onward!'19] , and providing atomic error recovery [SLE'17] .
• User studies on software development challenges: experiments on program comprehension [MSR'20] , use of program-repair tools [VL/HCC'19] , and handling input-processing errors [SLE'17] .

My PhD research pioneered a new approach, automatic software rejuvenation, which infers an existing program, formulates it as a precise model, and uses the model to regenerate a new program. This paradigm can deliver benefits in many aspects of the software life cycle, such as by generating high-quality code from simple prototypes, improving program comprehension and producing cleaner code, and extracting human knowledge from software and retargeting it to various languages or platforms. In the future, software will serve even more critical tasks in human society. It is important to reduce the fundamental inefficiencies in how people work with software. I aim to capture the maximal benefits from the human effort and knowledge embedded in software and enable new ways to express human creativity.

Service

Program Committee: OOPSLA 2024, ‹Programming› 2024, Onward! 2023, OOPSLA 2023 (ERC), ECOOP 2023 (ERC), Onward! 2022, OOPSLA 2022 (ERC), AISTA 2022, APLAS 2021, AISTA 2021

Journal Reviewer: TOSEM 2023

Organizing Committee: SPLASH 2024 (Student Research Competition Co-Chair), SPLASH 2023 (Student Research Competition Co-Chair and Posters Co-Chair), CCF ChinaSoft 2023 (Forum for Women in Software Engineering Co-Chair)

Artifact Evaluation Committee: OOPSLA 2023, ECOOP 2023, OOPSLA 2022, PLDI 2021, CAV 2020, SAS 2019, SLE 2016, OOPSLA 2016

Judges: SPLASH 2021 SRC