Title:
Incremental Slicing Based on Data-Dependences Types
Incremental Slicing Based on Data-Dependences Types
Author(s)
Orso, Alessandro
Sinha, Saurabh
Harrold, Mary Jean
Sinha, Saurabh
Harrold, Mary Jean
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Abstract
Program slicing is useful for assisting with
software-maintenance tasks, such as program understanding,
debugging, impact analysis, and regression
testing. The presence and frequent usage of pointers,
in languages such as C, causes complex data
dependences. To function effectively on such programs,
slicing techniques must account for pointerinduced
data dependences. Although many existing
slicing techniques function in the presence of pointers,
none of those techniques distinguishes data dependences
based on their types. This paper presents
a new slicing technique, in which slices are computed
based on types of data dependences. This new slicing
technique offers several benefits and can be exploited
in different ways, such as identifying subtle
data dependences for debugging purposes, computing
reduced-size slices quickly for complex programs, and
performing incremental slicing. In particular, this
paper describes an algorithm for incremental slicing
that increases the scope of a slice in steps, by incorporating
different types of data dependences at each
step. The paper also presents empirical results to
illustrate the performance of the technique in practice.
The experimental results show how the sizes
of the slices grow for different small- and mediumsized
subjects. Finally, the paper presents a case
study that explores a possible application of the slicing
technique for debugging.
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Date Issued
2000
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242416 bytes
Resource Type
Text
Resource Subtype
Technical Report