The test case prioritization aims to order test cases to increase rate of fault detection, and to reduce the time for detecting faults. In this study, a static source code analysis based approach, that uses change impact analysis is proposed. The proposed change impact analysis approach uses program slicing technique, method change information and Bayesian Network. With respect to the change impact analysis results, two test case prioritization approaches called LoM and LoM-Addtl are proposed, which is inspired by the "Law of Minimum" from biology and agronomy. The change impact analysis and test case prioritization approaches are performed on three well-known projects. The proposed change impact analysis results are evaluated with precision and recall metrics. On the other hand, the proposed test case prioritization methods LoM and LoM-Addtl are compared with five other test case prioritization techniques and evaluated with the APFD measure. The results of the change impact analysis showed that when a software has completed 75% of its development, 97%-100% of the affected methods and changed methods are predicted. On the other hand, the LoM and LoM-Addtl test case prioritization approaches showed consistent results when compared to the traditional test case prioritization techniques. However, it has been observed that, LoM and LoM-Addtl performed better than the traditional methods when version jumps are smaller. Furthermore, following an Additional in LoM (LoM-Addtl) has shown better results compare to LoM.