This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function Miller and Spooner, 1976, Korel, 1990]. In our work, the function is minimized by using one of two genetic algorithms in place of the local minimization techniques used in earlier research. We describe the implementation of our GA-based system, and examine the e ectiveness of this approach on a number of programs, one of which is signi cantly larger than those for which results have previously been reported in the literature. We also examine the e ect of program complexity on the test data generation problem by executing our system on a number of synthetic programs that have varying complexities.

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In this paper we present PEAT

Public reporting burden for this collection ol information is estimated to average 1 hour per response, including the time lot reviewing instructions, searching existing data sources, gathering and maintaining the data needett and completing and reviewing the collection of information. This report presents an overview of the results of a three year DARPA-sponsored effort investigating dynamic software security analysis. This research effort resulted in the design and implementation of two major tool sets (FIST and VISTA), each comprised of many individual tools, and the development of a methodology that provides the capability to perform a thorough security analysis on a piece of security-critical software written in C or C+ + . The Fault Injection Security Tool (FIST) automates white-box dynamic security analysis of software using program inputs, fault injection and assertion monitoring of programs written in C and C + +. The Visualizing STatic Analysis (VISTA) Tool provides a way of viewing and navigating static analysis properties of a program. Together these tools provide static and dynamic analysis capabilities that can identify security vulnerabilities in source code before its release. However, a major research issue remains. Though the current approach is able to discover security vulnerabilities through a process of fault injection and dynamic monitoring, the tools themself are not able to determine whether such an event could occur through standard attacker input at the program interface. This effort only scratched the surface of work on this important problem.

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