Abstract: Concepts and technologies are described herein for determining memory safety of floating-point computations. The concepts and technologies described herein analyze code to determine if any floating-point computations exist in the code, and if so, if the floating-point computations are memory safe. The analysis can include identifying floating-point instructions and conditional statements in the code. The code can be symbolically executed, and behavior of the floating-point instructions and the conditional statements can be monitored to determine if a floating point calculation is ever involved in computation of any memory address during the execution of the code.

Abstract: Concepts and technologies are described herein for incremental compositional dynamic test generation. The concepts and technologies described herein are used to increase the code coverage and security vulnerability identification abilities of testing applications and devices, without significantly increasing, and in some cases decreasing, computational and time costs associated with the testing. Test summaries that describe how code is tested by a test engine are generated and stored during testing of code. These test summaries can be evaluated when additional iterations or versions of the code are tested. If functions corresponding to the test summaries are unchanged from, or logically equivalent to, a version of the function previously tested, the test summary may be used when testing the new version of the code.

Abstract: A method of analyzing a computer application is disclosed. The method may break an application into components and the method may determine if the components have already been analyzed for errors, either through static analysis or by a code analysis. If the component has already been analyzed, the previous analysis may be used and the method may move on to the next code section. If the component has not been analyzed, it may be determined if the component may be reached from a given starting point. If the component cannot be reached from a given starting point, the component may not be analyzed. Both static and code testing tools may be used to determine if errors exist. The fined-grained coupling and alternation of may (universal) and must (existential) summaries allow the method to easily navigate through these code fragments while traditional may-only, must-only or non-compositional may-must al-gorithms are stuck in their specific analyses.

Abstract: An exemplary method includes providing software for testing; during execution of the software, performing a symbolic execution of the software to produce path constraints; injecting issue constraints into the software where each issue constraint comprises a coded formula; solving the constraints using a constraint solver; based at least in part on the solving, generating input for testing the software; and testing the software using the generated input to check for violations of the injected issue constraints. Such a method can actively check properties of the software. Checking can be performed on a path for a given input using a constraint solver where, if the check fails for the given input, the constraint solver can also generate an alternative input for further testing of the software. Various exemplary methods, devices, systems, etc., are disclosed.

Abstract: A method and apparatus for performing unit testing of software modules uses a novel directed automated random testing approach that advantageously combines automated extraction of the interface of a program with its external environment using static source code parsing; automatic generation of a test driver for this interface that advantageously performs random testing to simulate the most general environment the program can operate in; and dynamic analysis of how the program behaves under random testing and automatic generation of new test inputs to direct systematically the execution along alternative program paths. Together, these techniques constitute a directed automated random testing approach (DART). With DART, testing can be performed completely automatically on any program that compiles without the need to write any test driver or harness code. During testing, DART detects standard errors such as program crashes, assertion violations, and non-termination conditions.