any software systems are developed in a number of consecutive releases. Each new release does not only add new code but also modifies already existing one. In this study we have shown that the modified code can be an important source of faults. The faults are widely recognized as one of the major cost drivers in software projects. Therefore we look for methods of improving fault detection in the modified code. We suggest and evaluate a number of prediction models for increasing the efficiency of fault detection. We evaluate them against the theoretical best model, a simple model based on size, as well as against analyzing the code in a random order (not using any model). We find that using our models provides a significant improvement both over not using any model at all and using the simple model based on the class size. The gain offered by the models corresponds to 30% to 60% of the theoretical maximum.