Source Code Reduction to Summarize False Positives

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dc.contributor.advisorPorter, Adamen_US
dc.contributor.authorMarenchino, Matiasen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2016-02-06T06:32:01Z
dc.date.available2016-02-06T06:32:01Z
dc.date.issued2015en_US
dc.description.abstractThe main disadvantage of static code analysis tools is the high rates of false positives they produce. Users may need to manually analyze a large number of warnings, to determine if these are false or legitimate warnings, reducing the benefits of automatic static analysis. Our long term goal is to significantly reduce the number of false positives that these tools report. A learning system could classify the warnings into true positives and false positives by means of features extracted from the program source code. This work implements and evaluates a technique to reduce the source code producing false positives into code snippets that are simpler to analyze. Results indicate that the method considerably reduces the source code size and it is feasible to use it to characterize false positives.en_US
dc.identifierhttps://doi.org/10.13016/M2K99F
dc.identifier.urihttp://hdl.handle.net/1903/17210
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledFalse Positivesen_US
dc.subject.pquncontrolledSoftware Engineeringen_US
dc.subject.pquncontrolledSource Code Reductionen_US
dc.subject.pquncontrolledStatic Code Analysisen_US
dc.titleSource Code Reduction to Summarize False Positivesen_US
dc.typeThesisen_US

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