Effects of Pointers on Data Dependences

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Orso, Alessandro
Sinha, Saurabh
Harrold, Mary Jean
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Data dependences, which relate statements that compute data value to statements that use those values, are useful for automating a variety of program-comprehension-related activities, such as reverse engineering, impact analysis, and debugging. Unfortunately, data dependences are difficult to compute and understand in the presence of commonly-used language features such as pointers, arrays, and structures. To facilitate the comprehension of data dependences in programs that use such features, we define a technique for computing and classifying data dependences that takes into account the complexities introduced by specific language constructs. The classification that we present is finer-grained than previously proposed classification. Moreover, unlike previous work, we present empirical results that illustrate the distribution of data dependences for a set of C subjects. We also present a potential application for the proposed classification: program slicing. We propose a technique that allows for computing slices based on data-dependence types. This technique facilitates the use of slicing for understanding a program because a user can either incrementally augment a slice by incorporating data dependences based on their relevance, or focus on specific kinds of dependences. Finally, we present a case study that shows how the incremental computation of slices can (1) highlight subtle data dependences within a program, and (2) provide useful information about those dependences.
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