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Exploitable software vulnerabilities pose severe threats to its information security and privacy. Although a great amount of efforts have been dedicated to improving software security, research on quantifying software exploitability is still in its infancy. In this work, we propose ExploitMeter, a fuzzing-based framework of quantifying software exploitability that facilitates decision-making for software...
Static analysis tools are useful to find potential bugs and security vulnerabilities in a source code, however, false alarms from such tools lower their usability. In order to reduce various kinds of false alarms and enhance the performance of the tools, we propose a machine learning based false alarm reduction method. Abstract syntax trees (AST) are used to represent structural characteristics and...
Recently, machine learning classifiers have emerged as a way to predict the existence of a bug in a change made to a source code file. The classifier is first trained on software history data, and then used to predict bugs. Two drawbacks of existing classifier-based bug prediction are potentially insufficient accuracy for practical use, and use of a large number of features. These large numbers of...
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