Software defect prediction is always one of the most active research areas in software engineering. Researchers have proposed a lot of defect prediction techniques. These techniques consist of module-level, file-level, and change-level defect prediction according to the granularity. Change-level defect prediction can predict the defect-proneness of changes when they are initially submitted. Hence, such a technique is referred to as just-in-time defect prediction. Recently, just-in-time defect prediction becomes the hot area in defect prediction because of its timely manner and fine-grained. There are a lot of achievements in this area and there are also many challenges in data labeling, feature extracting, and model evaluation. More advanced and unified theoretic and technical guidelines are needed to enhance just-in-time defect prediction. Therefore, in this study, a road map for prior just-in-time defect prediction studies is presented in three folds, data labeling, feature extraction, and model evaluation. In summary, the contributions of this study are: (1) The data labeling methods and their advantages and disadvantages are concluded; (2) The feature categories and computing methods are concluded and classified; (3) The modeling techniques are concluded and classified; (4) The model validation and performance measures in model evaluation are concluded; (5) The current problems in this area are highlighted; and (6) The trends of Just-in-Time defect prediction are concluded.