A Method to Support Feature Dependency Comprehension Based on Semi-static Structure of Object Graph

Abstract

To understand why features of existing software can depend on each other is important for correct addition of a new feature to the software. Although some work has been done to detect feature dependency, it is not clear how effective such existing approaches are when they are applied to feature dependency comprehension because they are aimed at detection of runtime dependency between features. Therefore in this thesis, the author presents an extraction method of source code that can be used to comprehend feature dependency. The method can extract a wider range of source code than existing techniques of feature dependency detection by using delta extraction. The author conducted a controlled experiment with 20 professional Java programmers and confirmed that the source code extracted by our method is more effective for feature dependency comprehension than exsiting method. To figure out an internal mechanism to enable feature dependency, the author also defined semi-static parts of object graphs that can be used to make features depend on each other. Finally, the author confirmed that semi-static structures are actually used in feature dependencies in three open-source programs and can be effectively extracted by our method.