Those user stories delivered to Dev team must be with a good quality to ensure Highest Value Delivery, Fosters Collaboration, Boost Transparency, Minimalnumber of defects.
- Define Quality criteria: Start by defining the criteria for assessing user story
quality. Consider factors like clarity, completeness, correctness, consistency and
alignment with projectgoals. These criteria will beused to train our AI model.
- Data collection: Gather a dataset of user stories that have been labeled with quality scores. You can dothis manually by having your team assess the quality of a sample of user stories.
The dataset shouldinclude both high-quality and low-quality examples.
- Data preprocessing: Preprocess the text data. This includes tasks like tokenization, removing stop words and converting text to a numerical format that AI models can understand.
- Select an AI model: Choose a machine learning or deep learning model suitable for NLP tasks. Common choices include recurrent neural networks (RNNs), convolutional neural networks(CNNs) or transformer-based models like BERT or GPT.
Date d’expiration: 06 février, 2024