Classifying data dependences in the presence of pointers for program comprehension, testing, and debugging

• Authors: Alessandro Orso;Saurabh Sinha;Mary Jean Harrold
• Venue: ACM Transactions on Software Engineering and Methodology (TOSEM)
• Year: 2004

Understanding data dependences in programs is important for many software-engineering activities, such as program understanding, impact analysis, reverse engineering, and debugging. The presence of pointers can cause subtle and complex data dependences that can be difficult to understand. For example, in languages such as C, an assignment made through a pointer dereference can assign a value to one of several variables, none of which may appear syntactically in that statement. In the first part of this article, we describe two techniques for classifying data dependences in the presence of pointer dereferences. The first technique classifies data dependences based on definition type, use type, and path type. The second technique classifies data dependences based on span. We present empirical results to illustrate the distribution of data-dependence types and spans for a set of real C programs. In the second part of the article, we discuss two applications of the classification techniques. First, we investigate different ways in which the classification can be used to facilitate data-flow testing. We outline an approach that uses types and spans of data dependences to determine the appropriate verification technique for different data dependences; we present empirical results to illustrate the approach. Second, we present a new slicing approach that computes slices based on types of data dependences. Based on the new approach, we define an incremental slicing technique that computes a slice in multiple steps. We present empirical results to illustrate the sizes of incremental slices and the potential usefulness of incremental slicing for debugging.