Software pipelining is an effective approach of loop scheduling. Pipelining of loops with conditional branches remains a challenge. Current algorithms are divided into four classes: loop linearizaton, path splitting, as-a-whole scheduling and path selecting. All of them fail to solve concurrently two conflicting problems: transfer time minimization and worst-case constraints. In this paper, a novel software pipelining framework is presented to do so. The key ideas are: (1) Path grouping, which splits paths into distinct groups based on their execution and transfer probability, so as to minimize transfer time; (2) Data dependence relaxation. Some data dependences may not have instances during execution when the loop has multiple paths. The ideal policy is to obey them only when they have instances. Thus the worst-case constraints are avoided. Preliminary experiments and qualitative analysis all suggest that the temporal benefit of the proposed approach is better than that of path splitting and as-a-whole scheduling.