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A systematic literature review of actionable alert identification techniques for automated static code analysis
2011
Information and Software Technology
Context: Automated static analysis (ASA) identifies potential source code anomalies early in the software development lifecycle that could lead to field failures. Excessive alert generation and a large proportion of unimportant or incorrect alerts (unactionable alerts) may cause developers to reject the use of ASA. Techniques that identify anomalies important enough for developers to fix (actionable alerts) may increase the usefulness of ASA in practice. Objective: The goal of this work is to
doi:10.1016/j.infsof.2010.12.007
fatcat:bwettl5fqjczhl4svfikm7545q