Generating test input with deep reinforcement learning

Test data generation is a tedious and laborious process. Search-based Software Testing (SBST) automatically generates test data optimising structural test criteria using metaheuristic algorithms. In essence, metaheuristic algorithms are systematic trial-and-error based on the feedback of fitness fun...

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Bibliographic Details
Published in:2018 IEEE ACM 11th International Workshop on Search Based Software Testing (SBST) pp. 51 - 58
Main Authors: Kim, Junhwi, Kwon, Minhyuk, Yoo, Shin
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 28.05.2018
Series:ACM Conferences
Subjects:
Instruments
Learning (artificial intelligence)
Machine learning
Neural networks
Python
reinforcement learning
sbst
Software algorithms
Software and its engineering > Software creation and management > Search-based software engineering
test data generation
ISBN:9781450357418, 1450357415
Online Access:Get full text
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