Recently, Python is the most-widely used language in artificial intelligence (AI) projects requiring huge amount of CPU and memory resources, and long execution time for training. For saving the project duration and making AI software systems more reliable, it is inevitable to handle exceptions appropriately at the code level. However, handling exceptions highly relies on developer’s experience. This is because, as an interpreter-based programming language, it does not force a developer to catch exceptions during development. In order to resolve this issue, we propose an approach to suggesting appropriate exceptions for the AI code segments during development after training exceptions from the existing handling statements in the AI projects. This approach learns the appropriate token units for the exception code and pretrains the embedding model to capture the semantic features of the code. Additionally, the attention mechanism learns to catch the salient features of the exception code. For evaluating our approach, we collected 32,771 AI projects using two popular AI frameworks (i.e. Pytorch and Tensorflow) and we obtained the 0.94 of Area under the Precision-Recall Curve (AUPRC) on average. Experimental results show that the proposed method can support the developer’s exception handling with better exception proposal performance than the compared models.

In Version Control System (VCS), a developer frequently uploads multiple tasks such as adding features, code refactoring, and fixing bugs, into a single commit and crumbles each task’s summary when writing a commit message. It causes code readers to feel challenged in understanding the developer’s past tasks within the commit history. To resolve this issue, we propose an automatic approach to generating a task summary to help comprehend multiple mixed tasks in a commit and developed tool support named Task summary Generator (TsGen). In our approach, we use the commit with a single task as input and identify the task to sort its elements sequentially. Then we generate feature vectors from each sorted element to train the Neural Machine Translation (NMT) model. Based on the trained NMT model, we generate the feature vector from each task of a commit with multiple tasks and put each of them into the model to provide the task summary. In evaluation, we compared the performance of TsGen with two existing methods for nine open-source projects. As a result, TsGen outperformed CoDiSum and Jiang’s NMT by 52.08% and 28.07% in BiLingual Evaluation Understudy (BLEU) scores. In addition, the human evaluation was carried out to demonstrate that TsGen helps understand mixed tasks in a commit and gained a 0.27 higher preference than the actual commit message.