RepoOMP combines dependency-graph evidence, rule-based routing, and bounded LLM context to parallelize OpenMP hotspots in large repositories with better acceptance, speedup, and token efficiency than unstructured agent baselines.
AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models
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abstract
Code retrieval is allowing software engineers to search codes through a natural language query, which relies on both natural language processing and software engineering techniques. There have been several attempts on code retrieval from searching snippet codes to function codes. In this paper, we introduce Augmented Code (AugmentedCode) retrieval which takes advantage of existing information within the code and constructs augmented programming language to improve the code retrieval models' performance. We curated a large corpus of Python and showcased the the framework and the results of augmented programming language which outperforms on CodeSearchNet and CodeBERT with a Mean Reciprocal Rank (MRR) of 0.73 and 0.96, respectively. The outperformed fine-tuned augmented code retrieval model is published in HuggingFace at https://huggingface.co/Fujitsu/AugCode and a demonstration video is available at: https://youtu.be/mnZrUTANjGs .
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RepoOMP: Repository-Aware Hotspot OpenMP Parallelization via Dependency-Aware Context Reduction
RepoOMP combines dependency-graph evidence, rule-based routing, and bounded LLM context to parallelize OpenMP hotspots in large repositories with better acceptance, speedup, and token efficiency than unstructured agent baselines.