US 11,734,156 B2
Crash localization using crash frame sequence labelling
Chetan Bansal, Seattle, WA (US); Manish Shetty Molahalli, Karnataka (IN); Suman Kumar Nath, Redmond, WA (US); Siamak Ahari, Seattle, WA (US); Haitao Wang, Sammamish, WA (US); Sean A. Bowles, Seattle, WA (US); and Kamil Ozgur Arman, Seattle, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Sep. 23, 2021, as Appl. No. 17/483,571.
Prior Publication US 2023/0091899 A1, Mar. 23, 2023
Int. Cl. G06F 11/36 (2006.01)
CPC G06F 11/3636 (2013.01) 19 Claims
OG exemplary drawing
 
1. A computing system comprising:
one or more processors; and
one or more computer-readable storage devices having thereon computer-executable instructions that are structured such that, if executed by the one or more processors, would cause the computing system to estimate a blame frame of a crash stack by performing the following:
parsing a crash stack associated with a crash into a sequence of frames; and
estimating a blame frame of the crash stack by, for each of a plurality of the sequence of frames, performing the following:
identifying a plurality of features of the corresponding frame;
feeding the plurality of features to a neural network;
using the model to obtain an output comprising (i) a rough estimate of an initial prediction of frames for selecting the blame frame and (ii) hidden data that affects attention applied by the neural network for selecting the blame frame from the initial prediction of frames; and
using the output of the neural network to make a prediction on whether the corresponding frame is the blame frame of the crash;
the computer-executable instructions being further structured such that estimation of the blame frame of a crash stack is performed during inference time using a previously trained neural network.