Spectral Debugging with Weights and Incremental Ranking

Naish, Lee; Lee, Hua Jie; Ramamohanarao, Kotagiri

doi:10.1109/apsec.2009.32

Cited by 21 publications

(21 citation statements)

References 10 publications

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“…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. Therefore, our framework that provides a definite solution to the choice of the risk evaluation formula for SBFL can help in both the basic version of SBFL and its extended versions.…”

Section: Related Worksupporting

confidence: 78%

“…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%

See 1 more Smart Citation

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

Xie

Chen

Kuo

et al. 2013

ACM Trans. Softw. Eng. Methodol.

298

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 Worksupporting

confidence: 78%

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.

298

219

View full text Add to dashboard Cite

show abstract

“…Execution frequency counts for statements, rather than binary numbers, are used in [21] to weight the different a ij values and in [22] aggregates of the columns of the matrix are used to adjust the weights of different failed tests. The RAPID system [23] uses the Tarantula metric but uses branch spectra rather than statement spectra.…”

Section: Other Related Workmentioning

confidence: 99%

Duals in Spectral Fault Localization

Naish

Lee

2013

2013 22nd Australian Software Engineering Conference

Self Cite

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Numerous set similarity metrics have been used for ranking "suspiciousness" of code in spectral fault localization, which uses execution profiles of passed and failed test cases to help locate bugs. Research in data mining has identified several forms of possibly desirable symmetry in similarity metrics. Here we define several forms of "duals" of metrics, based on these forms of symmetries. Use of these duals, plus some other slight modifications, leads to several new similarity metrics. We show that versions of several previously proposed metrics are optimal, or nearly optimal, for locating single bugs. We also show that a form of duality exists between locating single bugs and locating "deterministic" bugs (execution of which always results in test case failure). Duals of the various single bug optimal metrics are optimal for locating such bugs. This more theoretical work leads to a conjecture about how different metrics could be chosen for different stages of software development.

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“…In the software debugging domain, for example, we know that test case failure is caused by execution of buggy statements. If a failed test case executes very few statements it is therefore particularly helpful for locating a bug and it is rational to give it high weight [NLK09], whereas naive use of information theory would give it low weight. In this paper we assume the simple approach of treating all cases equally.…”

Section: Related Similarity Problemsmentioning

confidence: 99%

Similarity to a single set

Naish

2016

Preprint

Self Cite

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Abstract. Identifying patterns and associations in data is fundamental to discovery in science. This work investigates a very simple instance of the problem, where each data point consists of a vector of binary attributes, and attributes are treated equally. For example, each data point may correspond to a person and the attributes may be their sex, whether they smoke cigarettes, whether they have been diagnosed with lung cancer, etc. Measuring similarity of attributes in the data is equivalent to measuring similarity of sets-an attribute can be mapped to the set of data points which have the attribute. Furthermore, there is one identified base set (or attribute) and only similarity to that set is considered-the other sets are just ranked according to how similar they are to the base set. For example, if the base set is lung cancer sufferers, the set of smokers may well be high in the ranking. Identifying set similarity or correlation has many uses and is often the first step in determining causality. Set similarity is also the basis for comparing binary classifiers such as diagnostic tests for any data set. More than a hundred set similarity measures have been proposed in the literature is but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties that similarity measures can have, weakening some previously proposed definitions so they are no longer incompatible, and identifying important forms of symmetry which have not previously been considered. It defines ordering relations over similarity measures and shows how some properties of a domain can be used to help choose a similarity measure which will perform well for that domain.

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Spectral Debugging with Weights and Incremental Ranking

Cited by 21 publications

References 10 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

Duals in Spectral Fault Localization

Similarity to a single set

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