Dynamic slicing method for maintenance of large C programs

Beszedes, Arpad; Gergely, Tamás; Szabó, Zsanett; Csirik, János; Gyimóthy, Tibor

doi:10.1109/csmr.2001.914974

Cited by 40 publications

(54 citation statements)

References 9 publications

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“…The internal validity of this experiment lies in the precision of dynamic slices obtained by our tool. However, we utilized forward computation of dynamic slices algorithm similar to the algorithm proposed by Beszedes et al [25]. Dynamic slices are used to narrow the search space, and the results show that dynamic slices we obtained are effective in fault localization.…”

Section: Threats To Validitymentioning

confidence: 99%

Applying Association Analysis to Dynamic Slicing Based Fault Localization

Cao

Jiang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYFault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

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Section: Threats To Validitymentioning

confidence: 99%

Applying Association Analysis to Dynamic Slicing Based Fault Localization

Cao

Jiang

et al. 2014

IEICE Trans. Inf. & Syst.

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“…In contrast, most later work on dynamic slicing 'defines' dynamic slicing based on the algorithms used to compute it (e.g., Agrawal et al [1] and Demillo et al [16]). Although many research prototypes and approaches exist [2], [6], [7], [36], [42], [53], [54], all approaches are for a single specific programming language whereas the observation based nature of ORBS allows it to slice programs distracted from multiple programming languages [10].…”

Section: Rq3: Observational Sensitivity To Inadequate Testingmentioning

confidence: 99%

ORBS and the limits of static slicing

Binkley

Gold

Harman

et al. 2015

2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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Abstract-Observation-based slicing is a recently-introduced, language-independent, slicing technique based on the dependencies observable from program behaviour. Due to the wellknown limits of dynamic analysis, we may only compute an under-approximation of the true observation-based slice. However, because the observation-based slice captures all possible dependence that can be observed, even such approximations can yield insight into the limitations of static slicing. For example, a static slice, S that is strictly smaller than the corresponding observation based slice is guaranteed to be unsafe. We present the results of three sets of experiments on 12 different programs, including benchmarks and larger programs, which investigate the relationship between static and observation-based slicing. We show that, in extreme cases, observation-based slices can find the true static minimal slice, where static techniques cannot. For more typical cases, our results illustrate the potential for observation-based slicing to highlight unsafe static slices. Finally, we report on the sensitivity of observation-based slicing to test quality.

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“…As originally formulated by Korel and Laski [15], a dynamic slice of a program P is defined by three parameters besides P , namely a variable set V , an initial input state d and an integer n. The slice with respect to these parameters is required to follow the same path as P up to the nth statement (with statements not lying in the slice deleted from the path through the slice) and give the same value for each element of V as P after the nth statement after execution from the initial state d. Many dynamic slicing algorithms have been written [16,17,18,19,20,15,21,22]. Most of these compute a slice using the data and control dependence relations along the given path through the original program.…”

Section: Introductionmentioning

confidence: 99%

On the computational complexity of dynamic slicing problems for program schemas

Danicic

Hierons

Laurence

2011

Math. Struct. Comp. Sci.

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Given a program, a quotient can be obtained from it by deleting zero or more statements. The field of program slicing is concerned with computing a quotient of a program which preserves part of the behaviour of the original program. All program slicing algorithms take account of the structural properties of a program such as control dependence and data dependence rather than the semantics of its functions and predicates, and thus work, in effect, with program schemas. The dynamic slicing criterion of Korel and Laski requires only that program behaviour is preserved in cases where the original program follows a particular path, and that the slice/quotient follows this path. In this paper we formalise Korel and Laski's definition of a dynamic slice as applied to linear schemas, and also formulate a less restrictive definition in which the path through the original program need not be preserved by the slice. The less restrictive definition has the benefit of leading to smaller slices. For both definitions, we compute complexity bounds for the problems of establishing whether a given slice of a linear schema is a dynamic slice and whether a linear schema has a non-trivial dynamic slice and prove that the latter problem is NP-hard in both cases. We also give an example to prove that minimal dynamic slices (whether or not they preserve the original path) need not be unique.

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Dynamic slicing method for maintenance of large C programs

Abstract: Abstract

Cited by 40 publications

References 9 publications

Applying Association Analysis to Dynamic Slicing Based Fault Localization

Applying Association Analysis to Dynamic Slicing Based Fault Localization

ORBS and the limits of static slicing

On the computational complexity of dynamic slicing problems for program schemas

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