Title : 
Application of reinforcement learning to requirements engineering: requirements tracing
         
        
            Author : 
Sultanov, Hakim ; Hayes, Jane Huffman
         
        
            Author_Institution : 
Univ. of Kentucky, Lexington, KY, USA
         
        
        
        
        
            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;
         
        
        
        
            Conference_Titel : 
Requirements Engineering Conference (RE), 2013 21st IEEE International
         
        
            Conference_Location : 
Rio de Janeiro
         
        
        
            DOI : 
10.1109/RE.2013.6636705