An IDE Support for Validating Machine Learning Applications in Bioengineering Text Corpora

Abstract

Modeling in machine learning (ML) is critical for software systems in practice. ML applications are required to validate their models and implementations but quality validation is a challenging and time-consuming process for developers. To address this limitation, we present a novel validation technique for ML applications to help developers or researchers (e.g., bioengineering domain) inspect (1) software code (ML API usages) and (2) ML model (extracted features).