In this paper, we review several important issues in interprocedural analysis for parallel computing: call effects, variable aliasing, and execution context. We show the use of traditional summary data flow information in program transformation for parallel computing. We examine three different methods for interprocedural data dependence analysis: (1) linearization; (2) global regions; and (3) atom images. We observe that although useful in some cases, linearization should be avoided when possible, because it often results in less precise data dependence analysis. Global region methods also suffer loss of exact information of subscript coefficients which is required by some commonly used data dependence test algorithms. As a result, an expensive data dependence test must be used. The atom images method is able to overcome this difficulty and is more efficient. Detailed discussions are provided to justify our observation. We also address the issue of alias identification and the propagation of execution context. The problems in array alias identification and a few possible solutions are discussed.