ConSUS: a scalable approach to conditioned slicing

Daoudi, Mohammed; Ouarbya, Lahcen; Howroyd, John; Danicic, Sebastian; Harman, Mark; Fox, Chris; Ward, Martin

doi:10.1109/wcre.2002.1173069

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…1(a) and assume we would like to slice it wrt location 9 and variable z. The statement x:=0 at 1 should not be included in the slice because any path that reaches 8 through 5 redefines x and any path that reaches 8 through 6 (without redefining x) is infeasible. Note that a path insensitive algorithm would not be able to infer this from the CFG.…”

Section: Motivating Examplementioning

confidence: 99%

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Path-Sensitive Backward Slicing

et al. 2012

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Abstract. Backward slicers are typically path-insensitive (i.e., they ignore the evaluation of predicates in guards), or they are only partial sometimes producing too big slices. Though the value of path-sensitivity is always desirable, the major challenge is that there are, in general, an exponential number of predicate combinations to be considered. We present a path-sensitive backward slicer and demonstrate its practicality with real C programs. The core is a symbolic execution-based algorithm that excludes spurious dependencies lying on infeasible paths while pruning paths that cannot improve the accuracy of the dependencies already computed by other paths.

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Section: Motivating Examplementioning

confidence: 99%

“…Conditioned slicing described in [3,5,6] performs symbolic execution in order to exclude infeasible paths before applying a static slicing algorithm, so they are fully path-sensitive (for loop-free programs) similar to us. However, they perform full path enumeration and essentially explore the search space of the naive SET.…”

Section: Related Workmentioning

confidence: 99%

Path-Sensitive Backward Slicing

et al. 2012

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“…Conditioned slicing [31], [32], [33] is very similar to parametric slicing in that the programmer is able to specify constraints on input values. The examples discussed above for which parametric slicing is better, worse, and the same as callstack-sensitive slicing apply to conditioned slicing as well.…”

Section: Conditioned Slicingmentioning

confidence: 99%

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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“…This technique can therefore be used to simplify the code, before applying a traditional static slicing algorithm. Conditioned slicing has been automated with significant success on C and WSL code [Daoudi et al, 2002], [Danicic et al, 2000].…”

Section: Definition 4 Conditioned Slicing Criterionmentioning

confidence: 99%

Static Program Transformations for Efficient Software Model Checking

Vasudevan

Abraham

Building the Information Society

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The need to build next generation air force systems with highly complex functions, but at relatively low cost, will inevitably means a major investment in software. Without highly reliable software, any ambitious air force program cannot succeed. Indeed, software is the keystone (or perhaps the Achilles heel) of most large-scale automation projects; and the problem of making software reliable has become one of today's most important technological challenges. To address this problem and to improve software reliability, we designed novel program analysis techniques that significantly speed up software model checking, thereby enabling the checking of much larger programs and broader class of program properties than previously possible. In particular, we developed a software model checker for efficiently checking data oriented programs with respect to complex data dependent properties. We used our model checker for checking programs that use linked data structures such as lists, queues, trees, and maps. Verifying such programs has often been an obstacle to progress in the past and is a key underlying technical challenge in software verification. Because these programs have complex data dependent properties, the state space reduction techniques (such as predicate abstraction or partial order reduction) used by other model checkers are largely ineffective on such programs. Our model checker uses novel techniques to achieve orders of magnitude state space reduction. In addition, we also developed a novel trace driven approach to use counter example guided abstraction refinement (CEGAR) to check for concurrency errors in multithreaded programs.

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ConSUS: a scalable approach to conditioned slicing

Cited by 10 publications

References 22 publications

Path-Sensitive Backward Slicing

Path-Sensitive Backward Slicing

Better Debugging via Output Tracing and Callstack-Sensitive Slicing

Static Program Transformations for Efficient Software Model Checking

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