文档介绍：Adaptive Probabilistic Model for Ranking Code-Based Static Analysis Alerts
Sarah Smith Heckman
North Carolina State University, Campus Box 8206, Raleigh, NC 27695
sarah_******@
Abstract software engineer’s subjective threshold. The
Automated Warning Application for Reliability
2
Software engineers tend to repeat mistakes when Engineering (AWARE) tool automates the adaptive
developing software. Automated static analysis tools ranking model and gathers data to validate the
can detect some of these mistakes early in the software proposed theories.
process. However, these tools tend to generate a
significant number of false positive alerts. Due to the 2. Proposed solution
need for manual inspection of alerts, the high number
of false positives may make an automated static The focus of this research is on the development of
analysis tool too costly to use. In this research, we a probabilistic model for the adaptive ranking of alerts
propose to rank alerts generated from automated static generated by ASA tools to mitigate the cost of FPs.
analysis tools via an adaptive model that predicts the
probability an alert is a true fault in a system. The . Key model concepts
model adapts based upon a history of the actions the
software engineer has taken to either filter false Software engineers will select an alert from the
posi