Detecting TensorFlow Program Bugs in Real-World Industrial Environment

Deep learning has been widely adopted in industry and has achieved great success in a wide range of application areas. Bugs in deep learning programs can cause catastrophic failures, in addition to a serious waste of resources and time.This paper aims at detecting industrial TensorFlow program bugs. We report an extensive empirical study on 12,289 failed TensorFlow jobs, showing that existing static tools can effectively detect 72.55% of the top three types of Python bugs in industrial TensorFlow programs. In addition, we propose (for the first time) a constraint-based approach for detecting TensorFlow shape-related errors (one of the most common TensorFlow-specific bugs), together with an associated tool, ShapeTracer. Our evaluation on a set of 60 industrial TensorFlow programs shows that ShapeTracer is efficient and effective: it analyzes each program in at most 3 seconds and detects effectively 40 out of 60 industrial TensorFlow program bugs, with no false positives. ShapeTracer has been deployed in the platform-X platform and will be released soon.