During the software maintenance and evolution phase, the majority of a developer’s time is spent on programming comprehension tasks. Feature location (i.e., finding the first location to make a modification), impact analysis (i.e., determining what and to what extent a program is affected by a change), and traceability (i.e., determining where requirements are implemented in the program), are all examples of such tasks. Recent research in the area of program comprehension has focused on using textual information, structural information (i.e., information regarding the creation and use of objects and methods within the code), and execution traces to develop tools that ease the burden on developers and decrease the time spent in each task. Furthermore, new studies in automating these tasks have started using text retrieval techniques, such as the vector space model (VSM), latent semantic indexing (LSI), and latent Dirichlet allocation (LDA) for searching software. This doctoral symposium summary presents two promising areas for improving existing techniques by combining structural information with text retrieval. The first is a methodology for evaluating the usefulness of text obtained from a program by looking at the structural location of terms (e.g., method name, comments, identifiers). The second focuses on improving the existing text retrieval approaches by providing more flexible queries (i.e., search strings). These two areas are complementary to each other and may be combined.