DocumentCode :
1634336
Title :
Application of reinforcement learning to requirements engineering: requirements tracing
Author :
Sultanov, Hakim ; Hayes, Jane Huffman
Author_Institution :
Univ. of Kentucky, Lexington, KY, USA
fYear :
2013
Firstpage :
52
Lastpage :
61
Abstract :
We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.
Keywords :
formal verification; learning (artificial intelligence); program diagnostics; RL method; machine learning technique; reinforcement learning; requirements engineering; requirements traceability method; textual requirements artifacts; Educational institutions; Joining processes; Learning (artificial intelligence); Navigation; Software; Vocabulary; Research Project 2 of Grand Challenges of Traceability; Ubiquitous Grand Challenge; information retrieval; machine learning; reinforcement learning; requirements traceability; software engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Requirements Engineering Conference (RE), 2013 21st IEEE International
Conference_Location :
Rio de Janeiro
Type :
conf
DOI :
10.1109/RE.2013.6636705
Filename :
6636705
Link To Document :
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