• 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