Locating faulty code using failure-inducing chops

Gupta, Neelam; He, Haifeng; Zhang, Xiangyu; Gupta, Rajiv

doi:10.1145/1101908.1101948

Cited by 149 publications

(122 citation statements)

References 16 publications

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“…Other spectrum-based techniques [20], [25], [43], [52] only use failed executions as the input and systematically alter the program structure or program runtime states to locate faults. Zhang et al [52] search for faulty program predicates by switching the states of program predicates at runtime.…”

Section: A Fault Localizationmentioning

confidence: 99%

“…Sterling and Olsson use the concept of program chipping [43] to automatically remove parts of a program so that the part that contributes to the failure may become more apparent. While their tool, ChipperJ, works on syntax trees for Java programs, Gupta et al [20] work on program dependency graphs and use the intersection of forward and backward program slices to reduce the sizes of failure-relevant code for further inspection. Jeffrey et al use a value profile based approach to rank program statements according to their likelihood of being faulty [25].…”

Section: A Fault Localizationmentioning

confidence: 99%

“…Many approaches have been proposed to help in automating debugging processes, especially in localizing root causes of failures (i.e., faults) in programs [20], [27], [29], [31], [33], [34], [36], [38], [51]. One common kind of approaches is spectrum-based fault localization [1], [11], [14], [27], [31], where program traces or abstractions of traces (called program spectra) are used to represent program behaviors and the spectra of correct executions and failed executions are compared.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive evaluation of association measures for fault localization

Lucia

Jiang

et al. 2010

2010 IEEE International Conference on Software Maintenance

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Abstract-In statistics and data mining communities, there have been many measures proposed to gauge the strength of association between two variables of interest, such as odds ratio, confidence, Yule-Y, Yule-Q, Kappa, and gini index. These association measures have been used in various domains, for example, to evaluate whether a particular medical practice is associated positively to a cure of a disease or whether a particular marketing strategy is associated positively to an increase in revenue, etc. This paper models the problem of locating faults as association between the execution or non-execution of particular program elements with failures. There have been special measures, termed as suspiciousness measures, proposed for the task. Two state-of-the-art measures are Tarantula and Ochiai, which are different from many other statistical measures. To the best of our knowledge, there is no study that comprehensively investigates the effectiveness of various association measures in localizing faults. This paper fills in the gap by evaluating 20 wellknown association measures and compares their effectiveness in fault localization tasks with Tarantula and Ochiai. Evaluation on the Siemens programs show that a number of association measures perform statistically comparable as Tarantula and Ochiai.

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Section: A Fault Localizationmentioning

confidence: 99%

Section: A Fault Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive evaluation of association measures for fault localization

Lucia

Jiang

et al. 2010

2010 IEEE International Conference on Software Maintenance

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“…However, slicing tends to produce fat slices containing not only the failure inducing dependencies but also benign dependencies. Although various techniques have been proposed to prune dynamic slices [18,19], without using a reference execution to exclude benign chains, inspecting pruned slices still requires non-trivial human effort. Dicing [20] aggregates slices from multiple executions.…”

Section: Related Workmentioning

confidence: 99%

Algorithms for Automatically Computing the Causal Paths of Failures

Sumner¹,

Zhang²

2009

Fundamental Approaches to Software Engineering

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Abstract.We have proposed an automated debugging technique that explains a failure by computing its causal path leading from the root cause to the failure. Given a failing execution, the technique first searches for a dynamic patch. Fine-grained execution comparison between the failing run and the patched run is performed to isolate the causal path. The comparison is enabled by precisely aligning the two executions. We herein propose and study two algorithms aiming at efficiency. We also evaluate the effectiveness and cost of our technique on a set of real bugs, including requirement bugs in which no a single or small set of statements can be blamed as the root cause. In such cases, understanding a failure is more important.

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“…It is widely used in debugging [20]. Gupta et al [6] propose a forward dynamic slicing approach to narrow down slices. They further integrate the forward approach with standard dynamic slicing approaches [26].…”

Section: Related Workmentioning

confidence: 99%

Is non-parametric hypothesis testing model robust for statistical fault localization?

Zhang

Chan

Tse

et al. 2009

Information and Software Technology

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Fault localization is one of the most difficult activities in software debugging. Many existing statistical faultlocalization techniques estimate the fault positions of programs by comparing the program feature spectra between passed runs and failed runs. Some existing approaches develop estimation formulas based on mean values of the underlying program feature spectra and their distributions alike. Our previous work advocates the use of a nonparametric approach in estimation formulas to pinpoint fault-relevant positions. It is worthy of further study to resolve the two schools of thought by examining the fundamental, underlying properties of distributions related to fault localization. In particular, we ask: Can the feature spectra of program elements be safely considered as normal distributions so that parametric techniques can be soundly and powerfully applied? In this paper, we empirically investigate this question from the program predicate perspective. We conduct an experimental study based on the Siemens suite of programs. We examine the degree of normality on the distributions of evaluation biases of the predicates, and obtain three major results from the study. First, almost all examined distributions of evaluation biases are either normal or far from normal, but not in between. Second, the most fault-relevant predicates are less likely to exhibit normal distributions in terms of evaluation biases than other predicates. Our results show that normality is not common as far as evaluation bias can represent. Furthermore, the effectiveness of our non-parametric predicate-based fault-localization technique weakly correlates with the distributions of evaluation biases, making the technique robust to this type of uncertainty in the underlying program spectra.

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Locating faulty code using failure-inducing chops

Cited by 149 publications

References 16 publications

Comprehensive evaluation of association measures for fault localization

Comprehensive evaluation of association measures for fault localization

Algorithms for Automatically Computing the Causal Paths of Failures

Is non-parametric hypothesis testing model robust for statistical fault localization?

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