Computation of intraprocedural dynamic program slices

Mund, G. B.; Mall, Rajib; Sarkar, Sudeshna

doi:10.1016/s0950-5849(03)00029-6

Cited by 23 publications

(7 citation statements)

References 33 publications

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“…Mund et al [9] proposed another algorithm for computing intra-procedural dynamic slices. They had used PDG as the intermediate program representation.…”

Section: Review Of Related Workmentioning

confidence: 99%

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Computing Dynamic Slices of Object-Oriented Programs using Dependency Information

Mohanty¹,

Behera²,

Mohapatra³

2013

IJCA

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Now a days, object-oriented programs are becoming very popular amongst the developers and hence almost all software are designed using the object-oriented paradigm. Advanced features of object-oriented programming has made it complicated to understand, test, debug and maintain. To better manage these software, slicing techniques have been proved to be quite efficient. This paper proposed an algorithm for dynamic slicing of object-oriented software. It uses SDG(System Dependence Graph) and DG(Dynamic Graph) as the intermediate program representation while computing the dynamic slices. In this paper dynamic slicing algorithm is based on traversing through the outgoing control dependence edges and incoming data dependence edges of Dynamic Graph. The major advantage of the proposed algorithm is that the time required to compute the dynamic slice of the objectoriented programs is directly proportional to the number of dependencies (control and/or data) arising during the run time. Also the proposed algorithm depends on the numbers of nodes present in the intermediate program representation. GENERAL TERMProgram analysis, Dynamic slice, Object-oriented programs.

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“…Mund et al [9] proposed another algorithm for computing intra-procedural dynamic slices. They had used PDG as the intermediate program representation.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Hence the time of computation of slice must be increase. Mund et al [9] proposed algorithm for computing intra-procedural dynamic slice. And the time complexity of their algorithm is O(n 2 ) .…”

Section: Comparison With Related Workmentioning

confidence: 99%

Computing Dynamic Slices of Object-Oriented Programs using Dependency Information

Mohanty¹,

Behera²,

Mohapatra³

2013

IJCA

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“…During the initialization step, our NMDS algorithm first unmarks all the nodes of the EASDG and sets dslice(u,var)=φ for every node u of the EASDG. Now, for the input data argv[0]=4, the program will execute the statements 1, 2, 3, 13,14,15,4,5,6,7,8,9,7,8,9,7,8,9,7,8,9,7,16,17,11, 3 in order. So NMDS algorithm marks the vertices 1, 2, 3, 13,14,15,4,5,6,7,8,9,16,17,11.…”

Section: Overview Of Nmds Algorithmmentioning

confidence: 99%

“…Many researchers [9,10,11,12,13,14] have carried out the work on dynamic slicing of objectoriented features. But none of these researchers have considered the aspect-oriented features.…”

Section: Comparison With Related Workmentioning

confidence: 99%

A Node-Marking Technique for Dynamic Slicing of Aspect-Oriented Programs

Sahu¹,

Mohapatra²

2007

10th International Conference on Information Technology (ICIT 2007)

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We propose an efficient dynamic slicing technique for aspect-oriented programs. We use a dependencebased intermediate program representation called extended aspect-oriented system dependence graph (EASDG) to represent aspect-oriented software. The EASDG of an aspect-oriented program consists of a system dependence graph (SDG) for non-aspect code, a group of dependence graphs for aspect code and some additional dependence edges used to connect the system dependence graph for non-aspect code to the dependence graphs for aspect code. Our dynamic slicing algorithm is based on marking and unmarking of the executed nodes in EASDG appropriately during run-time. In our approach, we do not use any trace file to store the execution history. Also, our approach does not create any additional nodes during run-time. We use the term node and vertex interchangeably in this paper.

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“…Let the value of this number kC3 be m. Now, we have to compute the time requirement for finding the union of m sets, each set having at most O(n) elements, where n is the number of executable statements in the program. The complexity of set union is known to be O(mn) [30]. So, the worst case time complexity of our algorithm for computing the dynamic slice, for the whole program is O(mn).…”

Section: Complexity Analysismentioning

confidence: 99%