Prioritized static slicing and its application to fault localization

•We build a model PrioSlice that prioritizes statements in a static slice.•Not all statements in a static slice are equally likely to propagate effects.•Some data dependencies are more likely to occur than others.•Control dependencies are less likely than data dependencies to propagate effects.•Prio...

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Veröffentlicht in:The Journal of systems and software Jg. 114; S. 38 - 53
Hauptverfasser: Zhang, Yiji, Santelices, Raul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Inc 01.04.2016
Elsevier Sequoia S.A
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ISSN:0164-1212, 1873-1228
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Abstract •We build a model PrioSlice that prioritizes statements in a static slice.•Not all statements in a static slice are equally likely to propagate effects.•Some data dependencies are more likely to occur than others.•Control dependencies are less likely than data dependencies to propagate effects.•PrioSlice helps localize faults more effectively than other static-slicing methods. Static slicing is a popular program analysis used in software engineering to find which parts of a program affect other parts. Unfortunately, static slicing often produces large and imprecise results because of its conservative nature. Dynamic slicing can be an alternative in some cases, but it requires detailed runtime information that can be hard or impossible to obtain or re-create. This is often the case when users report bugs in deployed software. In this paper, we significantly improve the precision of static slicing through PrioSlice, a novel technique that exploits the insight that not all statements in a static slice are equally likely to affect another statement such as a failing point. PrioSlice first computes a probabilistic model of the dependencies in the program. In this model, some data dependencies are more likely to occur than others and control dependencies are less likely than data dependencies to propagate effects (e.g., errors). PrioSlice then traverses the program backwards, like static slicing, but in an order defined by the computed dependence probabilities. Our study of fault localization on various Java subjects indicates that PrioSlice can help localize faults much more effectively than existing static-slicing approaches.
AbstractList •We build a model PrioSlice that prioritizes statements in a static slice.•Not all statements in a static slice are equally likely to propagate effects.•Some data dependencies are more likely to occur than others.•Control dependencies are less likely than data dependencies to propagate effects.•PrioSlice helps localize faults more effectively than other static-slicing methods. Static slicing is a popular program analysis used in software engineering to find which parts of a program affect other parts. Unfortunately, static slicing often produces large and imprecise results because of its conservative nature. Dynamic slicing can be an alternative in some cases, but it requires detailed runtime information that can be hard or impossible to obtain or re-create. This is often the case when users report bugs in deployed software. In this paper, we significantly improve the precision of static slicing through PrioSlice, a novel technique that exploits the insight that not all statements in a static slice are equally likely to affect another statement such as a failing point. PrioSlice first computes a probabilistic model of the dependencies in the program. In this model, some data dependencies are more likely to occur than others and control dependencies are less likely than data dependencies to propagate effects (e.g., errors). PrioSlice then traverses the program backwards, like static slicing, but in an order defined by the computed dependence probabilities. Our study of fault localization on various Java subjects indicates that PrioSlice can help localize faults much more effectively than existing static-slicing approaches.
Static slicing is a popular program analysis used in software engineering to find which parts of a program affect other parts. Unfortunately, static slicing often produces large and imprecise results because of its conservative nature. Dynamic slicing can be an alternative in some cases, but it requires detailed runtime information that can be hard or impossible to obtain or re-create. This is often the case when users report bugs in deployed software. In this paper, we significantly improve the precision of static slicing through PrioSlice, a novel technique that exploits the insight that not all statements in a static slice are equally likely to affect another statement such as a failing point. PrioSlice first computes a probabilistic model of the dependencies in the program. In this model, some data dependencies are more likely to occur than others and control dependencies are less likely than data dependencies to propagate effects (e.g., errors). PrioSlice then traverses the program backwards, like static slicing, but in an order defined by the computed dependence probabilities. Our study of fault localization on various Java subjects indicates that PrioSlice can help localize faults much more effectively than existing static-slicing approaches.
Author Santelices, Raul
Zhang, Yiji
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Keywords Fault localization
Dependence analysis
Probabilistic slicing
Program analysis
Thin slicing
Static slicing
Language English
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SSID ssj0007202
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Snippet •We build a model PrioSlice that prioritizes statements in a static slice.•Not all statements in a static slice are equally likely to propagate effects.•Some...
Static slicing is a popular program analysis used in software engineering to find which parts of a program affect other parts. Unfortunately, static slicing...
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SubjectTerms Computer programs
Debugging
Dependence analysis
Dynamical systems
Dynamics
Effectiveness studies
Fault localization
Fault location
Faults
Java
Java (programming language)
Localization
Probabilistic slicing
Program analysis
Programming languages
Slicing
Software
Software engineering
Static slicing
Thin slicing
Title Prioritized static slicing and its application to fault localization
URI https://dx.doi.org/10.1016/j.jss.2015.10.052
https://www.proquest.com/docview/1765948143
https://www.proquest.com/docview/1816029776
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