arXiv subjects

HeuiChan Lim

Publications and source records attributed to HeuiChan Lim.

5 recordsLinked to original sources

arXiv2025

JITScope: Interactive Visualization of JIT Compiler IR Transformations

The complexity of modern Just-In-Time (JIT) compiler optimization poses significant challenges for developers seeking to understand and debug intermediate representation (IR) behavior. This work introduces JITScope, an interactive visualization framework that illustrates how IR nodes and instructions evolve across compilation phases. The system features a full-stack architecture: a Python-based backend transforms raw JSON-formatted IR data-representing an abstract model of the JIT compiler IR-into a normalized SQLite database; a controller layer serves processed CSV data; and a D3.js-powered frontend renders an interactive, phase-aware graph of IR node transformations. The design emphasizes modularity, traceability, and flexibility. Our roadmap explores intuitive visual representations of phase-level changes in IR node connectivity, values, and access patterns. Ultimately, JITScope lays a foundation for future tooling that enables visual exploration of IR evolution, including phase filtering, value tracking, and function-access mapping-offering a new lens into the behaviors and impacts of compiler optimizations.

cs.SE↗

arXiv2023

Directed Test Program Generation for JIT Compiler Bug Localization

Bug localization techniques for Just-in-Time (JIT) compilers are based on analyzing the execution behaviors of the target JIT compiler on a set of test programs generated for this purpose; characteristics of these test inputs can significantly impact the accuracy of bug localization. However, current approaches for automatic test program generation do not work well for bug localization in JIT compilers. This paper proposes a novel technique for automatic test program generation for JIT compiler bug localization that is based on two key insights: (1) the generated test programs should contain both passing inputs (which do not trigger the bug) and failing inputs (which trigger the bug); and (2) the passing inputs should be as similar as possible to the initial seed input, while the failing programs should be as different as possible from it. We use a structural analysis of the seed program to determine which parts of the code should be mutated for each of the passing and failing cases. Experiments using a prototype implementation indicate that test inputs generated using our approach result in significantly improved bug localization results than existing approaches.

cs.SE↗

arXiv2021

Visualizing JIT Compiler Graphs

Just-in-time (JIT) compilers are used by many modern programming systems in order to improve performance. Bugs in JIT compilers provide exploitable security vulnerabilities and debugging them is difficult as they are large, complex, and dynamic. Current debugging and visualization tools deal with static code and are not suitable in this domain. We describe a new approach for simplifying the large and complex intermediate representation, generated by a JIT compiler and visualize it with a metro map metaphor to aid developers in debugging.

cs.PL↗

arXiv2021

Visualizing The Intermediate Representation of Just-in-Time Compilers

Just-in-Time (JIT) compilers are used by many modern programming systems in order to improve performance. Bugs in JIT compilers provide exploitable security vulnerabilities and debugging them is difficult as they are large, complex, and dynamic. Current debugging and visualization tools deal with static code and are not suitable in this domain. We describe a new approach for simplifying the large and complex intermediate representation, generated by a JIT compiler and visualize it with a metro map metaphor to aid developers in debugging. Experiments using our prototype implementation on Google's V8 JavaScript interpreter and TurboFan JIT compiler demonstrate that it can help identify and localize buggy code.

cs.PL↗

arXiv2020

AutoMATES: Automated Model Assembly from Text, Equations, and Software

Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real use, they must also be implemented as software, thus making code a third form of representing models. We introduce the AutoMATES project, which aims to build semantically-rich unified representations of models from scientific code and publications to facilitate the integration of computational models from different domains and allow for modeling large, complicated systems that span multiple domains and levels of abstraction.

cs.AI↗