Fault localization using execution slices and dataflow tests

Agrawal, Hiralal; Horgan, J.R.; London, S.; Wong, W. Eric

doi:10.1109/issre.1995.497652

Cited by 221 publications

(211 citation statements)

References 7 publications

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“…Jones et al [6] compared the effectiveness of different fault localization techniques including Tarantula [19], Set intersection [26], Set Union [27], Nearest neighbor [7] and Cause transition [20]. In their experiment, the effectiveness is measured based on the percentage of the subject program that needs not be examined to find a faulty statement.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Chandrasekaran

Ghandehari

Lei

et al. 2016

2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Section: Related Workmentioning

confidence: 99%

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Chandrasekaran

Ghandehari

Lei

et al. 2016

2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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“…Podgurski et al [22] proposed a semi-automated procedure to classify similar faults and plot them by using a Hierarchical Multi Dimension Scaling (HMDS) algorithm. A tool named Xslice [23] visually differentiates the execution slices of passing and failing part of a test. Another tool Tarantula uses color-coding to track the statements of a program during and after the execution of the test suite [24].…”

Section: Related Workmentioning

confidence: 99%

Automated Discovery of Failure Domain

Ahmad¹,

Manuel²

2013

LNSE

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Abstract-There are several automated random strategies of software testing based on the presence of point, block and strip failure domains inside the whole input domain. As yet no particular, fully automated test strategy has been developed for the discovery and evaluation of the failure domains. We therefore have developed Automated Discovery of Failure Domain (ADFD), a new random test strategy that finds the failures and their domains in a given system under test. It further provides visualization of the identified pass and fail domains. In this paper we describe ADFD strategy, its implementation in York Extensible Testing Infrastructure (YETI) and illustrate its working with the help of an example. We report on experiments in which we tested error-seeded one and two-dimensional numerical programs. Our experimental results show that for each program, ADFD strategy successfully performs identification of failures, failure domains and their representation on graphical chart.

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“…Such techniques [4][5][6][7] typically implement and execute programs with test suites in order to gather runtime information. Other faults localization techniques are: χSlice [8] which collects coverage from failed test runs and passed test runs and then uses the set of statements executed only from the failed test run to be reported as likely faulty statements, Nearest Neighborhood (NN) [9] is an extension of [8] which features an extra step of passed test run selection. Tarantula [10] defines a color scheme to measure correlations i.e.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Approach of Fault Distribution for Debugging in Parallel

Srivasatav¹,

Singh²,

Chauhan³

2010

Journal of Information Processing Systems

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Abstract-Software Debugging is the most time consuming and costly process in the software development process. Many techniques have been proposed to isolate different faults in a program thereby creating separate sets of failing program statements. Debugging in parallel is a technique which proposes distribution of a single faulty program segment into many fault focused program slices to be debugged simultaneously by multiple debuggers. In this paper we propose a new technique called Faulty Slice Distribution (FSD) to make parallel debugging more efficient by measuring the time and labor associated with a slice. Using this measure we then distribute these faulty slices evenly among debuggers. For this we propose an algorithm that estimates an optimized group of faulty slices using as a parameter the priority assigned to each slice as computed by value of their complexity. This helps in the efficient merging of two or more slices for distribution among debuggers so that debugging can be performed in parallel. To validate the effectiveness of this proposed technique we explain the process using example.

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Fault localization using execution slices and dataflow tests

Cited by 221 publications

References 7 publications

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Automated Discovery of Failure Domain

An Optimized Approach of Fault Distribution for Debugging in Parallel

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