Abstract-A novel approach to improve feature location by enhancing the corpus (i.e., source code) with static information is presented. An information retrieval method, namely Latent Semantic Indexing (LSI), is used for feature location. Adding stereotype information to each method/function enhances the corpus. Stereotypes are terms that describe the abstract role of a method, for example get, set, and predicate are well-known method stereotypes. Each method in the system is automatically stereotyped via a static-analysis approach. Experimental comparisons of using LSI for feature location with, and without, stereotype information are conducted on a set of open-source systems. The results show that the added information improves the recall and precision in the context of feature location. Moreover, the use of stereotype information decreases the total effort that a developer would need to expend to locate relevant methods of the feature.
A highly efficient lightweight forward static slicing approach is presented and evaluated. The approach does not compute the program/system dependence graph but instead dependence and control information is computed as needed while computing the slice on a variable. The result is a list of line numbers, dependent variables, aliases, and function calls that are part of the slice for all variables (both local and global) for the entire system. The method is implemented as a tool, called srcSlice, on top of srcML, an XML representation of source code. The approach is highly scalable and can generate the slices for all variables of the Linux kernel in approximately 20 min on a typical desktop. Benchmark results are compared with the CodeSurfer slicing tool from GrammaTech Inc., and the approach compares well with regard to accuracy of slices.
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