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AutoDev: Automated AI-Driven Development
This enables the AI Agents to execute tasks in a fully automated manner with a comprehensive understanding of the contextual information required.
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development.
RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot
We compare our approach with the various prompt variations and state of the art methods in the task of performance bug fixing.
Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?
We recognize that the current advances in machine learning can be used to detect vulnerable code patterns on syntactically incomplete code snippets as the developer is writing the code at EditTime.
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance
Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations.
Generating Examples From CLI Usage: Can Transformers Help?
Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks.
Learning to Reduce False Positives in Analytic Bug Detectors
Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software.
