2 citations · 2 across the 12 of their papers we have counts for
14 papers
An Investigation on Numerical Bugs in GPU Programs Towards Automated Bug Detection
Ravishka Rathnasuriya, Nidhi Majoju, Zihe Song +1
General-purpose graphics processing unit (GPU) computing has emerged as a leading parallel computing paradigm, offering significant performance gains in various domains such as sci…
Beyond Confidence: Rethinking Uncertainty Calibration in Deep Code Models
Ravishka Rathnasuriya, Wei Yang
Deep learning models have shown significant promise in code-related tasks. While confidence calibration techniques have been proposed to improve model reliability, their effectiven…
Characterizing Overconfident Failure in LLM-Based Code Generation
Ravishka Rathnasuriya, Wei Yang
Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based corr…
Correct but Slow: An Empirical Study of the GPU Kernel Evaluation Gap in Modern Domain-Specific Languages
Tingxi Li, Ravishka Rathnasuriya, Wei Yang
Modern GPU domain-specific languages (DSLs), such as Triton and TileLang, are increasingly used to implement specialized deep-learning kernels and as target languages for automated…
When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions
Ravishka Rathnasuriya, Wei Yang
Code language models are increasingly adopted for both understanding and generative tasks. Despite their success, these models frequently produce overconfident incorrect prediction…
On-the-Fly Input Adaptation for Reliable Code Intelligence
Ravishka Rathnasuriya, Wei Yang
Code language models (CLMs) play a central role in software engineering across both generation and classification tasks. However, these models still exhibit notable mispredictions…
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
Characterizing Real-World Bugs in Tile Programs for Automated Bug Detection
Ravishka Rathnasuriya, Zihe Song, Nidhi Majoju +4
Tile-based programming frameworks are increasingly adopted to write high-performance GPU kernels in domains such as deep learning and scientific computing. While these frameworks e…
Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Wei Yang
The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet th…
Efficiency Robustness of Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Tingxi Li, Zexin Xu +4
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address eff…
Can You Mimic Me? Exploring the Use of Android Record & Replay Tools in Debugging
Zihe Song, S M Hasan Mansur, Ravishka Rathnasuriya +4
Android User Interface (UI) testing is a critical research area due to the ubiquity of apps and the challenges faced by developers. Record and replay (R&R) tools facilitate manual…
Framework for On the Fly Input Refinement for Deep Learning Models
Ravishka Rathnasuriya
Advancements in deep learning have significantly improved model performance across tasks involving code, text, and image processing. However, these models still exhibit notable mis…
CodeImprove: Program Adaptation for Deep Code Models
Ravishka Rathnasuriya, Zijie Zhao, Wei Yang
Leveraging deep learning (DL)-based code analysis tools to solve software engineering tasks is becoming increasingly popular. Code models often suffer performance degradation due t…
HateModerate: Testing Hate Speech Detectors against Content Moderation Policies
Jiangrui Zheng, Xueqing Liu, Guanqun Yang +5
To protect users from massive hateful content, existing works studied automated hate speech detection. Despite the existing efforts, one question remains: do automated hate speech…