Current compilers show inefficiencies when optimizing complex applications, both analyzing dependences and exploiting critical performance issues, like data locality and instruction/thread parallelism. Complex applications usually present irregular and/or dynamic (pointer-based) computational/data structures. By irregular we means applications that arrange data as multi-dimensional arrays and issue memory references through array indirections. Pointer-based applications, on the other hand, organize data as pointer-based structures (lists, trees, ...) and issue memory references by means of pointers. This paper discusses optimization/parallelization and program analysis techniques we have developed to instruct a compiler to generate efficient object code from important classes of irregular and pointer-based applications. These techniques are embodied into a methodology that proceeds in three stages: program structure recognition, data analysis and program optimization/parallelization based on code/data transformations.