Understanding Knowledge Sharing Activities in Software Fault-prone Prediction: a Transfer Learning Study
WU Fang-ju · Journal of Chinese Computer Systems · 2014
Software fault-prone prediction,an active research topic in software engineering domain,plays an important role in improving software quality,controlling and balancing software cost. A large amount of different fault prediction studies have been developed to solve different problems from different aspects. However,there are still some problems needed to be researched,such as imbalanced software fault data,different misclassification cost,and difficulty in sharing fault-prone prediction empirical experiences. To solve the above problems,this paper presents a method to share fault-prone prediction empirical experiences across projects based on transfer learning. The proposed method extends the famous transfer learning framework Tr Ada Boost by adding misclassification cost to improve the probability of detecting fault-prone modules. It adapts different update weight strategies to emphasize different roles of different project data. The proposed method is compared with multiple existing data mining and machine learning approaches on JM1 and KC2 data sets of the NASA metrics data program repository. Comparative experimental results showthat the proposed method was better than the others in effectiveness and stability. Simulation results indicate that software development teams can share fault-prone prediction experiences under the similar homogeneous domain and process,thus improve software quality effectively.