Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
- URL: http://arxiv.org/abs/2008.11856v1
- Date: Wed, 26 Aug 2020 23:24:34 GMT
- Title: Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
- Authors: Mohammad Jafar Mashhadi and Hadi Hemmati
- Abstract summary: We propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system.
We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code.
- Score: 2.294541416972175
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Inferring behavior model of a running software system is quite useful for
several automated software engineering tasks, such as program comprehension,
anomaly detection, and testing. Most existing dynamic model inference
techniques are white-box, i.e., they require source code to be instrumented to
get run-time traces. However, in many systems, instrumenting the entire source
code is not possible (e.g., when using black-box third-party libraries) or
might be very costly. Unfortunately, most black-box techniques that detect
states over time are either univariate, or make assumptions on the data
distribution, or have limited power for learning over a long period of past
behavior. To overcome the above issues, in this paper, we propose a hybrid deep
neural network that accepts as input a set of time series, one per input/output
signal of the system, and applies a set of convolutional and recurrent layers
to learn the non-linear correlations between signals and the patterns, over
time. We have applied our approach on a real UAV auto-pilot solution from our
industry partner with half a million lines of C code. We ran 888 random recent
system-level test cases and inferred states, over time. Our comparison with
several traditional time series change point detection techniques showed that
our approach improves their performance by up to 102%, in terms of finding
state change points, measured by F1 score. We also showed that our state
classification algorithm provides on average 90.45% F1 score, which improves
traditional classification algorithms by up to 17%.
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