Detecting memory leaks through introspective dynamic behavior modelling using machine learning
Sangho Lee, Changhee Jung, Santosh Pande · 2014
This paper expands staleness-based memory leak detection by presenting a machine learning-based framework. The proposed framework is based on an idea that object staleness can be better leveraged in regard to similarity of objects; i.e., an object is more likely to have leaked if it shows significantly high staleness not observed from other similar objects with the same allocation context.