Welcome to PassionNet Predictor!
Early detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. This Platfarm offers a comprehensive framework that facilitate development of 3 different types of predictive pipelines: language models based, multi-model similarity knowledge-driven and large language models (LLMs) context + multi-model similarity knowledge-driven. Within first type predictive pipelines landscape, framework facilitates conflicting/duplicate requirements identification by leveraging 8 distinct types of LLMs. In second type, framework supports development of predictive pipelines that leverage multi-scale and multi-model similarity knowledge, ranging from traditional similarity computation methods to advance similarity vectors generated by LLMs. In the third type, the framework synthesizes predictive pipelines by integrating contextual insights from LLMs with multi-model similarity knowledge. We have evaluated this framework on multiple public datasets and based on the top performing predictive pipeline, a prediction interface is developed that takes multiple requirements and annotate duplicate and confilicting requirements.