aiXcoder-7B-v2 addresses a critical limitation in LLMs for code completion: their inability to effectively utilize information from long repository contexts.

• Identified that LLMs often ignore useful information in long contexts even when relevant APIs or similar code are present
• Developed a novel training approach to enhance LLMs' ability to utilize repository-level context
• Created a model that achieves improved performance in repository-level code completion tasks
• Demonstrated practical engineering benefits through better context utilization in real-world coding scenarios

This research matters for software engineering by increasing developer productivity with more contextually relevant code suggestions that understand entire codebases rather than just immediate surroundings.

aiXcoder-7B-v2: Training LLMs to Fully Utilize the Long Context in Repository-level Code Completion