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20242026
most citedRethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers

6 citations · 9 across the 16 of their papers we have counts for

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18 papers · 1 filter

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…