In this paper, we study the single-source shortest-path (SSSP) problem with positive edge weights, which is a notoriously hard problem in the parallel context. In practice, the $Δ$-stepping algorithm proposed by Meyer and Sanders has been widely adopted. However, $Δ$-stepping has no known worst-case bounds for general graphs. The performance of $Δ$-stepping also highly relies on the parameter $Δ$. There have also been lots of algorithms with theoretical bounds, such as Radius-stepping, but they either have no implementations available or are much slower than $Δ$-stepping in practice. We propose a stepping algorithm framework that generalizes existing algorithms such as $Δ$-stepping and Radius-stepping. The framework allows for similar analysis and implementations of all stepping algorithms. We also propose a new ADT, lazy-batched priority queue (LaB-PQ), that abstracts the semantics of the priority queue needed by the stepping algorithms. We provide two data structures for LaB-PQ, focusing on theoretical and practical efficiency, respectively. Based on the new framework and LaB-PQ, we show two new stepping algorithms, $ρ$-stepping and $Δ^*$-stepping, that are simple, with non-trivial worst-case bounds, and fast in practice. The stepping algorithm framework also provides almost identical implementations for three algorithms: Bellman-Ford, $Δ^*$-stepping, and $ρ$-stepping. We compare our code with four state-of-the-art implementations. On five social and web graphs, $ρ$-stepping is 1.3--2.5x faster than all the existing implementations. On two road graphs, our $Δ^*$-stepping is at least 14\% faster than existing implementations, while $ρ$-stepping is also competitive. The almost identical implementations for stepping algorithms also allow for in-depth analyses and comparisons among the stepping algorithms in practice.