Research Article
Research on Cross-Company Defect Prediction Method to Improve Software Security
Algorithm 1
Metric clustering algorithm.
Input: source and target multigranularity metric feature vector and ; number of clusters K; | Output: source and target representative vector and ; | (1) | For each project {Source project S, Target project T} | (2) | Randomly select K metrics as the starting centroid of the project ; | (3) | Repeat the following process until convergence | (4) | For each metric : | (5) | Calculate the Euclidean distance to each starting centroid based on eq.(2); | (6) | Assign the metric to its nearest cluster with minimum distance; | (7) | End for | (8) | For each cluster : | (9) | Calculate the mean value of the cluster and update its centroid; | (10) | End for | (11) | For each cluster : | (12) | Use PCA method to extract the corresponding representative vector ; | (13) | End for | (14) | End for | (15) | Output and ; |
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