Proximity based weighting of test cases to improve spectrum based fault localization

Bandyopadhyay, Aritra; Ghosh, Sudipto

doi:10.1109/ase.2011.6100088

Cited by 25 publications

(14 citation statements)

References 9 publications

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“…Such an intuition is conceived by some extended SBFL techniques. For example, both Baah et al [2010] and Bandyopadhyay and Ghosh [2011] chose formula Ochiai in their studies, because of its empirically good performance. Naish et al [2009] have applied their weighting factors in different formulas, but interestingly, the performance comparison results of these formulas with weighting factors are consistent with the results without weighting factors.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some extended SBFL techniques have emerged, which integrate the basic procedure with other models or employ additional information, such as the SBFL with causal inference using program dependence graphs by Baah et al [2010], some weighted SBFL techniques using additional information from either passed test case or failed test case as weighting factors by Bandyopadhyay and Ghosh [2011], and by Naish et al [2009], etc. However, no matter how SBFL is extended, selecting a wellperformed risk evaluation formula is always the most fundamental and essential task.…”

Section: Related Workmentioning

confidence: 99%

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A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization

Xie

Chen

Kuo

et al. 2013

ACM Trans. Softw. Eng. Methodol.

302

219

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An important research area of Spectrum-Based Fault Localization (SBFL) is the effectiveness of risk evaluation formulas. Most previous studies have adopted an empirical approach, which can hardly be considered as sufficiently comprehensive because of the huge number of combinations of various factors in SBFL. Though some studies aimed at overcoming the limitations of the empirical approach, none of them has provided a completely satisfactory solution. Therefore, we provide a theoretical investigation on the effectiveness of risk evaluation formulas. We define two types of relations between formulas, namely, equivalent and better. To identify the relations between formulas, we develop an innovative framework for the theoretical investigation. Our framework is based on the concept that the determinant for the effectiveness of a formula is the number of statements with risk values higher than the risk value of the faulty statement. We group all program statements into three disjoint sets with risk values higher than, equal to, and lower than the risk value of the faulty statement, respectively. For different formulas, the sizes of their sets are compared using the notion of subset. We use this framework to identify the maximal formulas which should be the only formulas to be used in SBFL.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization

Xie

Chen

Kuo

et al. 2013

ACM Trans. Softw. Eng. Methodol.

302

219

View full text Add to dashboard Cite

show abstract

“…In the past, Wong et al [25,26] reported that the contribution of the failing test cases in computing its suspiciousness value is larger than or equal to that of the passing test cases. Bandyopadhyay and Ghosh [27] proposed a method to improve the efficiency of spectrum-based fault localization by incorporating the relative importance of different test cases in the calculation of suspiciousness values. Therefore, in the following, we will further study how to assign different weights of failing and the passing test cases in the similarity coefficients.…”

Section: Coverage-combined Techniquementioning

confidence: 99%

Evaluation and Analysis of Spectrum-Based Fault Localization with Modified Similarity Coefficients for Software Debugging

You

Huang

Peng³

et al. 2013

2013 IEEE 37th Annual Computer Software and Applications Conference

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During the process of fault localization, the spectrum-based techniques are frequently used and widely studied since they can automatically and effectively localize the faults of software and be implemented easily. So far most of spectrum-based fault localization techniques have relied heavily on the use of similarity coefficients. However, we noticed that existing similarity coefficients for fault localization may lack measure(s) to properly reflect the relationship between failing and passing test cases. It also has to note that the failing test cases usually are expected to provide more information to the similarity coefficients than the passing test cases. In order to evaluate the importance of failing and passing test cases in the similarity coefficients, a number of modified similarity coefficients in fault localization are presented and discussed. The modified similarity coefficients which are assigned the weights of the failing and/or passing test cases will be studied and analyzed with the multi-coverage-combined techniques. Five open source programs and 75 faulty versions in total from Siemens suite, which have been widely used for software testing and comparison of fault localization techniques, were selected as experiment subjects. Detailed analysis of the results shows that assigning the weights of failing and passing test cases to the similarity coefficients would be able to localize the faults more effectively and accurately.

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“…In our previous work [13], we proposed an approach to assign weights to test cases, based on an extension of the nearest neighbor model. The weight of a test case represents its importance in fault localization.…”

Section: A Coincidental Correctnessmentioning

confidence: 99%

Tester Feedback Driven Fault Localization

Bandyopadhyay

Ghosh

2012

2012 IEEE Fifth International Conference on Software Testing, Verification and Validation

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Coincidentally correct test cases are those that execute faulty statements but do not cause failures. Such test cases reduce the effectiveness of spectrum-based fault localization techniques, such as Ochiai, because the correlation of failure with the execution of a faulty statement is lowered. Thus, coincidentally correct test cases need to be predicted and removed from the test suite used for fault localization. Techniques for predicting coincidentally correct test cases can produce false positives, such as when one predicts a fixed percentage that is higher than the actual percentage of coincidentally correct test cases. False positives may cause nonfaulty statements to be assigned higher suspiciousness scores than the faulty statements.We propose an approach that iteratively predicts and removes coincidentally correct test cases. In each iteration, we present the tester the set of statements that share the highest Ochiai suspiciousness score. If the tester reports that these statements are not faulty, we use that feedback to determine a number that is guaranteed to be less than or equal to the actual number of coincidentally correct test cases. We predict and remove that number of coincidentally correct test cases, recalculate the suspiciousness scores of the remaining statements, and repeat the process.We evaluated our approach with the Siemens benchmark suite and the Unix utilities, grep and gzip. Our approach outperformed an existing approach that predicts a fixed percentage of test cases as coincidentally correct. The results with Ochiai were mixed. In some cases, our approach outperformed Ochiai by up to 67%. In others, Ochiai was more effective.

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Proximity based weighting of test cases to improve spectrum based fault localization

Cited by 25 publications

References 9 publications

A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization

A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization

Evaluation and Analysis of Spectrum-Based Fault Localization with Modified Similarity Coefficients for Software Debugging

Tester Feedback Driven Fault Localization

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