International Business Machines Corporation

Yufan Zhuang

Jim Alain Laredo

Alessandro Morari

Yunhui Zheng

Sahil Suneja

Techniques regarding training one or more AI models for a source code understanding task are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train an artificial intelligence model on source code samples for a source code understanding task. The source code samples can be ranked based on code complexity.

Complexity based artificial intelligence model training

Techniques regarding augmenting one or more training datasets for training one or more AI models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise training augmentation component that can generate an augmented training dataset for training an artificial intelligence model by extracting a simplified source code sample from a source code sample comprised within a training dataset.

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Yufan Zhuang