1 citations · 3 across the 19 of their papers we have counts for
10 papers · 1 filter
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…
Towards Reliable Generation of Executable Workflows by Foundation Models
Sogol Masoumzadeh, Keheliya Gallaba, Dayi Lin +1
Recent advancements in Foundation Models (FMs) have demonstrated significant progress in processing complex natural language to perform intricate tasks. Successfully executing thes…
An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
Mohammed Mehedi Hasan, Hao Li, Emad Fallahzadeh +3
Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assur…
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…
The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)
Kirill Vasilevski, Benjamin Rombaut, Gopi Krishnan Rajbahadur +10
Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KD…
Characterizing Multi-Hunk Patches: Divergence, Proximity, and LLM Repair Challenges
Noor Nashid, Daniel Ding, Keheliya Gallaba +2
Multi-hunk bugs, where fixes span disjoint regions of code, are common in practice, yet remain underrepresented in automated repair. Existing techniques and benchmarks pre-dominant…
Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement
Keheliya Gallaba, Ali Arabat, Dayi Lin +2
Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a signif…
Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization
Ximing Dong, Shaowei Wang, Dayi Lin +1
Optimizing Large Language Model (LLM) performance requires well-crafted prompts, but manual prompt engineering is labor-intensive and often ineffective. Automated prompt optimizati…
LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance
Jingwen Tan, Gopi Krishnan Rajbahadur, Zi Li +5
Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets. Ambiguities i…