Behavior - Oriented Software Feature Location Method

When the developers of software are doing maintenance work, they need to modify or improve some functions. Before this, they need to find out the source codes which were relation to these functions and need to be rewrite. This process is called feature location. The growing scale of software and the processing of software evolution make the software documents become chaos and the structure of the software become degradation. All of these have brought great difficulties for the feature location.Feature location is one of the prerequisites for the sooth realization of software evolution intent, and has great meaning for software maintenance and software evolution research. The feature location method can be divided into four categories: dynamic, static, text and hybrid. There are some problems in current location method, such as low precision, huge location space and low automatic degree. For these problems, this paper proposed a feature location method based on behavior knowledge and topic knowledge. It combined the behavior knowledge with topic knowledge to reduce the state space of feature location, then realized the feature location by analysis the similarity between topic knowledge and software function attributes. The main works of this research as follows:First, proposed the behavior and topic oriented software feature location method based on the dynamic and text feature location method. It is a hybrid method, and can locate the features by analyzing the software’s dynamic behavior and text topic.Second, the dynamic analysis processing use the execution traces as the expression of the software. The text analysis processing build an index for the source codes by the latent semantic indexing and based on the dynamic analysis result. And user can locate the features by querying in the index.Third, using the source code of jEdit4.3as the benchmark for comparison in the experiment. The experiment validates the proposed method.Fourth, have discussed another two experimental questions:the necessity of preprocessing and the possibility of applying text clustering algorithm to feature location.In Summary, a precision feature location method which has low program understanding requirements and low labor cost based on behavior knowledge and topic knowledge is proposed.