A steady increasing share and complexity of automotive software is a huge challenge for quality management during software development and in-use phases. In cases of faults occurring in customer’s use, warranty leads to product recalls which are typically associated with high costs. To avoid software faults efficiently, quality management and enhanced development processes have to be realized by the introduction of specific analysis methods and Key Process/Performance Indicators (KPIs) to enable objective quality evaluations as soon as possible during product development process. The paper introduces an application of specific analysis methods by using KPIs and discusses their potential for automotive software quality improvement. Target is to support quality evaluation and risk-analysis for the release process of automotive software. A new approach is presented, which enables an objective analysis of the software development process by use of stochastic analysis methods to gain an estimation of software reliability. Modelling of the expected residual error rate at certain release points during development provides basis information for the decision, if further development or tests are necessary. In addition, it delivers prediction of expected failure performance during in-use phases. A comparison of stochastic fault rate models, covering development and in-use phases, highlights potentials of enhancement and improvement of different prediction models. Finally, the paper presents and evaluates a combination of indicators, metrics as well as stochastic methods to deliver risk analysis for software release processes, with the target to optimize reliability at the costumer, decrease fault costs and support an improvement of development processes.