1 citations · 3 across the 19 of their papers we have counts for
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What Does an Agentic Software Engineering Benchmark Measure? Profiling Task Demands and Agent Behaviour Beyond What Category Labels Reveal
Radin Shayanfar, Keheliya Gallaba, Ahmed E. Hassan
Agentic software engineering benchmarks are typically summarized by nominal category labels such as "bug fix" or "feature implementation," yet benchmarks carrying the same label ar…
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…
To Add Is Machine, To Delete Is Human: Measuring and Mitigating Deletion Avoidance in LLM Code Editing
Amir M. Ebrahimi, Mohammed Mehedi Hasan, Aaditya Bhatia +2
Large language models increasingly write and repair production code, yet evidence is mounting that their test-passing patches leave codebases harder to maintain. We identify one co…
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…
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…