Abstract:

Static analysis tools can help developers locate potential code errors in the early phase of development. However, studies show that such tools always report a large number of alerts, and most of them are meaningless false ones. To enhance the availability of static analysis tools, researchers divide alerts to actionable and unactionable alerts using statistics and machine learning techniques. These classification techniques do not consider the class imbalance problem caused by false positives and the unequal cost of different misclassifications. Aiming at these problems, we apply the BP neural networks and cost sensitive neural networks based on over sampling, threshold moving and under sampling techniques to classify alerts respectively. Experimental results show that, compared with BP neural networks, the cost sensitive neural networks techniques can on average increase actionable alert recall rate by 44.07%. And when the cost of misclassification of an actionable alert is above a certain value, cost sensitive techniques can have a lower classification cost.

Key words: actionable alert, unactionable alert, cost sensitive, class imbalance, neural networks