Analysis of Software Engineering data is often concerned with treatment of incomplete knowledge, with management of inconsistent pieces of information and with manipulation of various levels of representation of data. Existing techniques of data analysis are mainly based on quite strong assumptions (some knowledge about dependencies, probability distributions, large number of experiments), are unable to derive conclusions from incomplete knowledge, or can not manage inconsistent pieces of information. A rough set is a collection of objects which, in general, cannot be precisely characterized in terms of the values of the set of attributes, while a lower and an upper approximation of the collection can do. Rough sets were successfully applied for data analysis in different areas. In this paper, the approach is applied for analysis of Software Engineering data resulting from goal-oriented measurement. Fundamental principles and concepts of rough sets are presented. They are illustrated by the example to predict criticality of software modules based on metrics data from early development phases. In a further application, analysis of COCOMO cost drivers is studied.