Naohiko Tsuda, Masaki Takada, Hironori Washizaki, Yoshiaki Fukazawa, Shunsuke Sugimura, Yuichiro Yasuda, Masanao Futakami, “Iterative Process to Improve GQM Models with Metrics Thresholds to Detect High-risk Files,” IEEE TENCON 2016, Marina Bay Sands, Singapore, 22-25 November 2016. (to appear)(CORE Rank C)

Manual code inspections are intense and time-consuming activities to improve the maintainability and reusability of source code. Although automatic detection of high-risk source code files by metrics thresholds can help inspectors, determining the optimal thresholds is difficult. Thus, we propose an iterative process to define and improve GQM models with metrics thresholds to detect high-risk files. Our process clarifies experts’ viewpoints in the inspection and the measurement metrics using the GQM method, defines how to interpret the metrics values, searches concrete thresholds for a specific project by supervised learning using some of the files in the project as training data, and analyzes how to improve models and thresholds. We implemented our tool in R language and evaluated our process using a industrial project. Small-sized embedded C++ systems require only a few training data.