Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the
Dynamic Shortest Path (DSP)
problem has drawn lots of attention in recent years. However, as
DSP
has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the
determinant dimensions and constraint dimensions
of the
DSP
problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art
DSP
methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of
dynamic degree
to classify the dynamic environments and use
throughput
to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.