A formalisation of the relationship between forms of program slicing

Binkley, David; Danicic, Sebastian; Gyimóthy, Tibor; Harman, Mark; Kiss, Ákos; Korel, Bogdan

doi:10.1016/j.scico.2006.04.007

Cited by 34 publications

(35 citation statements)

References 39 publications

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“…Kamkar et al [21] and Zhang and Gupta [45] are able to reduce the cost of dynamic slicing, but the cost of fully-accurate slicing remains too high for production use. Venkatesh [39] and Binkley et al [8] formalize the semantics of program slicing and discuss the distinctions and orderings among the different types of program slices.…”

Section: Program Slicingmentioning

confidence: 99%

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Ohmann

Liblit

2016

Autom Softw Eng

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Abstract-Debugging is difficult and costly. As a human programmer looks for a bug, it would be helpful to see a complete trace of events leading to the point of failure. Unfortunately, full tracing is simply too slow to use in deployment, and may even be impractical during testing.We aid post-deployment debugging by giving programmers additional information about program activity shortly before failure. We use latent information in post-failure memory dumps, augmented by low-overhead, tunable run-time tracing. Our results with a realistically-tuned tracing scheme show low enough overhead (0-5%) to be used in production runs. We demonstrate several potential uses of this enhanced information, including a novel postmortem static slice restriction technique and a reduced view of potentially-executed code. Experimental evaluation shows our approach to be very effective, such as shrinking stack-sensitive interprocedural static slices by 49-78% in larger applications.

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Section: Program Slicingmentioning

confidence: 99%

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Ohmann

Liblit

2016

Autom Softw Eng

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“…This section first discusses how callstack-sensitive slicing fits in the slicing framework defined by Binkley et al [24], [25], and then considers how it relates to techniques for reducing the sizes of slices based on criteria other than active callstacks.…”

Section: Related Workmentioning

confidence: 99%

“…Binkley et al [24], [25] use that theory to show the semantic relationships among eight different kinds of slicing. For example, it is shown that dynamic slicing subsumes static slicing because every semantically correct static slice is also a semantically correct dynamic slice.…”

Section: Slicing Frameworkmentioning

confidence: 99%

“…It is worth noting, however, that the relationships defined by Binkley et al [24], [25] provide no information about the relative sizes of the slices produced by algorithms that implement the different kinds of slicing. In the proposed new fourth dimension, callstack-sensitive static slicing would subsume "plain" (i.e., full) static slicing, but this says nothing about how much smaller callstacksensitive slices are in practice.…”

Section: Slicing Frameworkmentioning

confidence: 99%

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Better Debugging via Output Tracing and Callstack-Sensitive Slicing

Horwitz

Liblit

Polishchuk

2010

IIEEE Trans. Software Eng.

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Abstract-Debugging often involves 1) finding the point of failure (the first statement that produces bad output) and 2) finding and fixing the actual bug. Print statements and debugger break points can help with step 1. Slicing the program back from values used at the point of failure can help with step 2. However, neither approach is ideal: Debuggers and print statements can be clumsy and timeconsuming and backward slices can be almost as large as the original program. This paper addresses both problems. We present callstack-sensitive slicing, which reduces slice sizes by leveraging the series of calls active when a program fails. We also show how slice intersections may further reduce slice sizes. We then describe a set of tools that identifies points of failure for programs that produce bad output. Finally, we apply our point-of-failure tools to a suite of buggy programs and evaluate callstack-sensitive slicing and slice intersection as applied to debugging. Callstack-sensitive slicing is very effective: On average, a callstack-sensitive slice is about 0.31 time the size of the corresponding full slice, down to just 0.06 time in the best case. Slice intersection is less impressive, on average, but may sometimes prove useful in practice.Index Terms-Static program slicing, callstack-sensitive analysis, points of failure, output tracing and attribution.

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“…The notion of Abstract Program Slicing was first introduced by Hong, Lee and Sokolsky (Seok Hong et al, 2005). Mastroeni and Nicolić (Mastroeni and Nikolic, 2010) recently extended the theoretical framework of slicing proposed by Binkley (Binkley et al, 2006) to an abstract domain in order to define abstract slicing, and to represent and compare different forms of slicing in the abstract domain. Other remarkable works on abstract program slicing include (Cortesi and Halder, 2010;Mastroeni and Zanardini, 2008;Zanardini, 2008;Bhattacharya, 2011).…”

Section: Introductionmentioning

confidence: 99%

Tukra: An Abstract Program Slicing Tool

Halder¹,

Cortesi

2012

Proceedings of the 7th International Conference on Software Paradigm Trends

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Abstract:We introduce Tukra, a tool that allows the practical evaluation of abstract program slicing algorithms. The tool exploits the notions of statement relevancy, semantic data dependences and conditional dependences. The combination of these three notions allows Tukra to refine traditional syntax-based program dependence graphs, generating more accurate slices. We provide the architecture of the tool, some snapshots describing how it works, and some preliminary experimental results giving evidence of the accuracy improvements it supports.

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A formalisation of the relationship between forms of program slicing

Cited by 34 publications

References 39 publications

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Better Debugging via Output Tracing and Callstack-Sensitive Slicing

Tukra: An Abstract Program Slicing Tool

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