In mission critical systems, such as those developed by NASA, it is very important that the test engineers properly recognize the severity of each issue they identify during testing. Proper severity assessment is essential for appropriate resource allocation and planning for fixing activities and additional testing. Severity assessment is strongly influenced by the experience of the test engineers and by the time they spend on each issue. The paper presents a new and automated method named SEVERIS (severity issue assessment), which assists the test engineer in assigning severity levels to defect reports. SEVERIS is based on standard text mining and machine learning techniques applied to existing sets of defect reports. A case study on using SEVERIS with data from NASApsilas Project and Issue Tracking System (PITS) is presented in the paper. The case study results indicate that SEVERIS is a good predictor for issue severity levels, while it is easy to use and efficient.