Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing
  of Software   Although machine learning (ML) has been successful in automating various
software engineering needs, software testing still remains a highly challenging
topic. In this paper, we aim to improve the generative testing of software by
directly augmenting the random number generator (RNG) with a deep reinforcement
learning (RL) agent using an efficient, automatically extractable state
representation of the software under test. Using the Cosmos SDK as the testbed,
we show that the proposed DeepRNG framework provides a statistically
significant improvement to the testing of the highly complex software library
with over 350,000 lines of code. The source code of the DeepRNG framework is
publicly available online.

3
The categories of this paper are: physics
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int