6 citations · 12 across the 18 of their papers we have counts for
20 papers
LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering
Gopi Krishnan Rajbahadur, Amir M. Ebrahimi, Boyuan Chen +1
Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixture budgets without regressing…
DCAS: Decoupling CLI Agent Scaffolding to Internalize Planning across Scaffolds
Kishanthan Thangarajah, Boyuan Chen, Ahmed E. Hassan
CLI-based software-engineering agents have matured rapidly, yet the open ecosystem has converged on a single training environment: trajectory datasets used to fine-tune open models…
SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements
Pengyu Xue, He Yang Yuan, Xin Wang +6
Although coding agents have achieved impressive performance on correctness-oriented benchmarks, their ability to make behavior-preserving non-functional improvements (NFIs) remains…
SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
Yihao Chen, Shi Chang, Feng Lin +4
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent bench…
MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
Yihao Chen, Shi Chang, Khaled Chawa +4
Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation. However, constructing a…
Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment
Kirill Vasilevski, Ximing Dong, Benjamin Rombaut +8
LLMs have substantially improved software engineering yet real-world development requires architectural understanding. Such understanding is prohibitively expensive to label manual…
SynConfRoute: Syntax-Aware Routing for Efficient Code Completion with Small CodeLLMs
Kishanthan Thangarajah, Boyuan Chen, Ahmed E. Hassan
Enterprises want AI code completion that is both high-quality and private, but they face a tension: proprietary models yield better results yet risk exposing proprietary code, whil…
When Elo Lies: Hidden Biases in Codeforces-Based Evaluation of Large Language Models
Shenyu Zheng, Ximing Dong, Xiaoshuang Liu +6
As Large Language Models (LLMs) achieve breakthroughs in complex reasoning, Codeforces-based Elo ratings have emerged as a prominent metric for evaluating competitive programming c…
Beyond Tokens: Semantic-Aware Speculative Decoding for Efficient Inference by Probing Internal States
Ximing Dong, Shaowei Wang, Dayi Lin +2
Large Language Models (LLMs) achieve strong performance across many tasks but suffer from high inference latency due to autoregressive decoding. The issue is exacerbated in Large R…
SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints
Zhiyu Fan, Kirill Vasilevski, Dayi Lin +6
The advancement of large language models (LLMs) and code agents has demonstrated significant potential to assist software engineering (SWE) tasks, such as autonomous issue resoluti…
RepoForge: Training a SOTA Fast-thinking SWE Agent with an End-to-End Data Curation Pipeline Synergizing SFT and RL at Scale
Zhilong Chen, Chengzong Zhao, Boyuan Chen +9
Training software engineering (SWE) LLMs is bottlenecked by expensive infrastructure, inefficient evaluation pipelines, scarce training data, and costly quality control. We present…
SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimation
Gustavo A. Oliva, Gopi Krishnan Rajbahadur, Aaditya Bhatia +7
High-quality labeled datasets are crucial for training and evaluating foundation models in software engineering, but creating them is often prohibitively expensive and labor-intens…
Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
Shi Chang, Boyuan Chen, Kishanthan Thangarajah +2
Code Large Language Models (CodeLLMs) are increasingly integrated into modern software development workflows, yet efficiently serving them in resource-constrained, self-hosted envi…
Context-Aware CodeLLM Eviction for AI-assisted Coding
Kishanthan Thangarajah, Boyuan Chen, Shi Chang +1
AI-assisted coding tools powered by Code Large Language Models (CodeLLMs) are increasingly integrated into modern software development workflows. To address concerns around privacy…
SLA-Awareness for AI-assisted coding
Kishanthan Thangarajah, Arthur Leung, Boyuan Chen +1
The integration of AI-assisted coding tools within development environments drastically reduces development time, and allows developers to focus more on creative and critical aspec…
Software Performance Engineering for Foundation Model-Powered Software
Haoxiang Zhang, Shi Chang, Arthur Leung +4
The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into productio…
Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin +3
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. H…
Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin +3
The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhanci…
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur +9
Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built us…
Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets
Gopi Krishnan Rajbahadur, Erika Tuck, Li Zi +5
Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets is governed by dataset licenses. These dataset licenses ou…