Many software systems are developed in a number of consecutive releases.In each release not only new code is added but also existing code is often modified.In this study we show that the modified code can be an iinportant source of faults.Faults are widely recognized as one of the major cost drivers in software projects.Therefore,we look for methods that improve the fault detection in the modified code.We propose and evaluate a number of prediction models that increase the efficiency of fault detection.To build and evaluate our models we use data collected from two large telecommunication systems produced by Ericsson.We evaluate the performance of our models by applying them both to a different release of the system than the one they are built on and to a different system.The performance of our models is compared to the performance of the theoretical best model,a simple model based on size,as well as to analyzing the code in a random order (not using any model).We find that the use of our models provides a significant improvement over not using any model at all and over using a simple model based on the class size.The gain offered by our models corresponds to 38～57% of the theoretical maximum gain. Many software systems are developed in a number of consecutive releases. In each release not only new code is added but also existing code is often modified. In this study we show that the modified code can be an iinportant source of faults. as one of the major cost drivers in software projects.Therefore, we look for methods that improve the fault detection in the modified code.We propose and evaluate a number of prediction models that increase the efficiency of fault detection. To build and evaluate our models we use data collected from two large telecommunication systems produced by Ericsson.We evaluate the performance of our models by applying them both to a different release of the system than the one they are built on and to a different system. The performance of our models is compared to the performance of the theoretical best model, a simple model based on size, as well as to analyzing the code in a random order (not using any model) .We find that the use of our models provides a significant improvement over not using any model at all and over using a simple model based on the class size. The gain offered by our models corresponds to 38-57% of the theoretical maximum gain.