Machine Learning to Evaluate Evolvability Defects: Code Metrics Thresholds for a Given Context accepted for QRS 2018 (CORE Rank B)(acceptance rate 33/171 =19%)

Naohiko Tsuda, Hironori Washizaki, Yoshiaki Fukazawa, Yuichiro Yasuda and Shunsuke Sugimura, “Machine Learning to Evaluate Evolvability Defects: Code Metrics Thresholds for a Given Context,” The 18th IEEE International Conference on Software Quality, Reliability & Security (QRS 2018), July 16 – 20, 2018, Lisbon, Portugal (CORE Rank B)(acceptance rate 33/171 =19%)