activity
20242026
most citedRepoformer: Selective Retrieval for Repository-Level Code Completion

3 citations · 7 across the 16 of their papers we have counts for

collaborators
Showing cs.SEShow all

8 papers · 1 filter

cs.SE2026

Shortcutting the Fix: Identifying and Categorizing Agentic Exploits in Software Engineering Benchmarks

Nikolai Ludwig, Wasi Uddin Ahmad, Somshubra Majumdar +1

While autonomous software engineering (SWE) agents achieve high benchmark resolution rates, these scores can mask exploitative behaviors---such as leveraging local Git histories, a…

cs.SE2026

Open-SWE-Traces: Advancing Dual-Mode Multilingual Distillation for Software Engineering Agents

Wasi Uddin Ahmad, Nikolai Ludwig, Somshubra Majumdar +1

The path toward autonomous software engineering is currently bottlenecked by a severe deficit of diverse, large-scale trajectory data. We address this by introducing \ourdataset, a…

cs.SE2026

From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents

Nikolai Ludwig, Wasi Uddin Ahmad, Somshubra Majumdar +1

We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resourc…

cs.SE2025

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution

Terry Yue Zhuo, Xiaolong Jin, Hange Liu +37

Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain,…

cs.SE2025

From Output to Evaluation: Does Raw Instruction-Tuned Code LLMs Output Suffice for Fill-in-the-Middle Code Generation?

Wasi Uddin Ahmad, Somshubra Majumdar, Boris Ginsburg

Post-processing is crucial for the automatic evaluation of LLMs in fill-in-the-middle (FIM) code generation due to the frequent presence of extraneous code in raw outputs. This ext…

cs.SE2025★ 1 cited

OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs

Wasi Uddin Ahmad, Aleksander Ficek, Mehrzad Samadi +4

Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning. However, their continued advancement is…