Biblio

Code-graph based software defect prediction methods have become a research focus in SDP field. Among them, Code Property Graph is used as a form of data representation for code defects due to its ability to characterize the structural features and dependencies of defect codes. However, since the coarse granularity of Code Property Graph, redundant information which is not related to defects often attached to the characterization of software defects. Thus, it is a problem to be solved in how to locate software defects at a finer granularity in Code Property Graph. Static analysis is a technique for identifying software defects using set defect rules, and there are many proven static analysis tools in the industry. In this paper, we propose a method for locating specific types of defects in the Code Property Graph based on the result of static analysis tool. Experiments show that the location method based on static analysis results can effectively predict the location of specific defect types in real software program.

Software defects will lead to software running error and system crashes. In order to detect software defect as early as possible at early stage of software development, a series of machine learning approaches have been studied and applied to predict defects in software modules. Unfortunately, the imbalanceof software defect datasets brings great challenge to software defect prediction model training. In this paper, a new manifold learning based subspace learning algorithm, Discriminative Locality Alignment(DLA), is introduced into software defects prediction. Experimental results demonstrate that DLA is consistently superior to LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) in terms of discriminate information extraction and prediction performance. In addition, DLA reveals some attractive intrinsic properties for numeric calculation, e.g. it can overcome the matrix singular problem and small sample size problem in software defect prediction.