Stone, Andrew, authorStrout, Michelle, advisorRajopadhye, Sanjay Vishnu, committee memberLiu, Jiangguo, committee member2007-01-032007-01-032009http://hdl.handle.net/10217/37716Department Head: L. Darrell Whitley.Data-flow analysis is a common technique for gathering program information for use in performance improving transformations such as register allocation, deadcode elimination, common subexpression elimination, and scheduling. Current tools for generating data-flow analysis implementations enable analysis details to be specified orthogonally to the solution algorithm, but still require implementation details regarding the may and must use and definition sets that occur due to the effects of pointers, side effects, arrays, and user-defined structures. This thesis presents the Data-Flow Analysis Generator tool (DFAGen), which enables analysis writers to generate pointer, aggregate, and side-effect cognizant analyzers for separable and nonseparable data-flow analyses, from a specification that assumes only scalars. By hiding the compiler-specific details behind predefined set definitions, the analysis specifications for the DFAGen tool are typically less than ten lines long and similar to those in standard compiler textbooks. The two main contributions of this work are the automatic determination of when to use the may or must variant of a predefined set reference in the analysis specification, and the design of the analysis specification language so that data-flow problem and compiler framework implementation details are specified orthogonally.masters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text