Software smell detection based on machine learning and its empirical study

As an important maintenance measure, software reconfiguration is the key to detect the unreasonable part of the code module, namely code smell. Traditional detection methods rely on the experience of engineers, and the location efficiency of reconfiguration points is low. The existing automatic detection tools identify code smell with limited accuracy. Aiming at the problem that the number of reconstructed points in software system is huge and various, and the automation of reconstructed activities is low and difficult to optimize, the research framework of software smell prediction based on machine learning is studied and designed. Taking four common code smells as the research object, the classification algorithm and detection model of the best code smell are established, and the dimension reduction method of feature extraction is further improved. The highest accuracy rate is 89.8%, which can improve the automation level of software smell detection.