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Found 5 papers, 1 papers with code
Studying LLM Performance on Closed- and Open-source Data
We find that performance for C# changes little from OSS --> proprietary code, but does significantly reduce for C++; we find that this difference is attributable to differences in identifiers.
Can GPT-4 Replicate Empirical Software Engineering Research?
We specifically study their ability to surface assumptions made in empirical software engineering research methodologies, as well as their ability to plan and generate code for analysis pipelines on seven empirical software engineering papers.
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft
Code review is an integral part of any mature software development process, and identifying the best reviewer for a code change is a well-accepted problem within the software engineering community.
Program Merge Conflict Resolution via Neural Transformers
Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools.
ConE: A Concurrent Edit Detection Tool for Large Scale Software Development
We study half a year of changes made to six large repositories in Microsoft in which at least 1, 000 pull requests are created each month.
