Identifying self-admitted technical debt in issue tracking systems using machine learning

HIGHLIGHTS

• who: Yikun Li from the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands have published the research: Identifying self-admitted technical debt in issue tracking systems using machine learning, in the Journal: (JOURNAL)
• what: The authors aim at proposing and evaluating an approach for automatically identifying SATD in issue tracking systems. The authors compare the F1-score of different machine_learning approaches identifying SATD in issues and find out that Text CNN (Kim 2014) outperforms others. Besides, the authors show that projects using different issue tracking systems have less . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.