Information for Paper ID 7100
Paper Information:
Paper Title: Supporting Root Cause Analysis of Inaccurate Bug Prediction Based on Machine Learning – Lessons Learned When Interweaving Training Data and Source Code 
Affiliation Type: Industry 
Keywords: Machine Learning, Bug Prediction, Verification 
Abstract: How do you verify and debug an implementation based on machine learning (ML)? You got the proof of concept working but now the real implementation is not working. Where do you start? The challenge is that the root cause could be almost anywhere. e.g. bad training data, implementation bugs or a mismatch between data or features during training and inference. This paper describes our lessons learned from a case study. Our implementation is a bug prediction mechanism implemented in software, but we believe these insights could be of interest to anyone working with ML, be it software or hardware. 
Track ID: 3.1 
Track Name: Automating the Optimization of Verification Processes 
Final Decision: Accept as Lecture 
Session Name: Automation using Machine Learning (Lecture) 
Author Questions:
Confirmed: Yes