Code refactoring means applying transformations on the code structure of a software project. Refactoring usually intends to remove poor code structures that harm the software maintenance. Each single transformation rarely suffices to fully remove poor code structures, even the simplest ones. For instance, shortening a long method often requires many method extractions. Up to 60 percent of the refactorings in software projects are constituted of a set of interrelated transformations, the so-called batches, rather than single transformations applied in isolation. Although batches are frequent in practice, the knowledge of batch characteristics is fragmented across studies. What is the usual size of batches? How do transformations vary within a batch? There is no summary that helps to address these questions. More critically, there is little empirical evidence of the batch effect on maintenance. Are batches more likely to introduce or remove poor code structures, especially those spotted by code smells? The current answer to questions like this is insufficient to support the batch application in practice. This Master s dissertation presents two complementary empirical studies that address both aforementioned literature gaps. The dissertation starts with a literature review of batch refactoring with 29 studies. We identified seven batch characteristics such as the scope in which batches are applied to code structures, plus seven types of batch effect on software maintenance, including code smell removal. All batch characteristics and types of effect were summarized in a conceptual map. The dissertation ends with the quantitative analysis of 57 open and closed software projects. From 4,607 heuristic-computed batches, we found that most batches occur entirely within one commit (93 percent) but affect more than just one method (90 percent). Surprisingly, batches mostly end up introducing (51 percent) or not removing (38 percent) code smells. Our results enabled us to reveal certain forms of batches, not documented by previous studies, that are useful to fully remove certain types of code smells.