The research community in software engineering is trying to find a way on how to achieve the goal of having a fault-free software. The industry that will use a near fault-free software will have it easier to lower the costs of maintenance and the versions of delivered software will be more qualitative. In this case, fault prediction can be used in order to achieve the above objectives. Fully applied fault prediction is not yet achieved on an industrial scale. There is some progress attained in the field during recent years. But knowing and understanding what available tools and algorithms regarding fault prediction can give is yet a goal to be achieved by the industry. In this thesis, two fault prediction algorithms and several metrics combinations are tested in an industrial and open source project. The main goal is to understand how much fault prediction is integrated and effective in a continuous delivery environment using real case scenarios. The manually collected data, from several versions and in different time periods were applied using two already present algorithms: Naive Bayes and Clustering. As a result, while the usage of this prediction depends on the company needs, further research in the field can be extended.