Existing methods to compute software reliability use the testing data of input and output. However, the data cannot truly reflect the behaviors of the software. For examples, it is possible that testing has false positive outputs, and one input with multiple faults cannot be revealed in the testing. This paper attempts to use program invariants to compute software reliability. First, test cases are selected to dynamically obtain program invariants. Then, the failure data from these invariants are extracted. Finally, the software reliabilities is computed based on Nelson model. In experiments, the reliabilities of the software in the Siemens set are computed. Program invariants are obtained by applying three different testing methods:random, branch coverage and block coverage. The reliabilities are computed next based on these invariants. To check the correctness of the results, software reliabilities are also computed with traditional methods. A comparison shows that the differences between two types of reliabilities are small no matter which testing method is selected. Further variance analysis shows that reliabilities by the proposed method have lower fluctuation, i.e., more stable, than those with existing methods, thus is closer to the real reliability of the system. This suggests that software reliability can be computed based on program invariants.