Duals in Spectral Fault Localization

Naish, Lee; Lee, Hua Jie

doi:10.1109/aswec.2013.16

Cited by 21 publications

(33 citation statements)

References 24 publications

(44 reference statements)

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“…Its error-dual is another extreme, giving minimal weight to m. This is optimal for another class of software debugging problems [NL13]. Note however that the unscaled version of the error-dual (thenm-dual) of Op does not always give minimal weight to m and the partial order and other properties we discuss here critically depend on the scaling.…”

Section: A Partial Order For Monotone Measuresmentioning

confidence: 99%

“…For this reason we do not consider these other forms of symmetry here. The three forms of symmetry that can preserve monotonicity are discussed in the context of debugging in [NL13]. Here we show how these forms of symmetry can also be adapted to allow for a form of scaling.…”

Section: 7nm-symmetrymentioning

confidence: 99%

“…In the natural sciences, boundary cases such as this rarely occur because causality is typically more complex and data is noisy. In the software debugging domain there is another boundary case of interest, where n c = 0 (there are no "false positives" or "type I errors"), essentially a dual to the single bug problem [NL13]. It corresponds to the case where there may be several bugs but they are always "deterministic" -whenever a bug is executed the test case fails.…”

Section: Interpolating Between Two Boundary Casesmentioning

confidence: 99%

“…For example, in software debugging, a very small proportion of test cases failing suggests there few bugs and/or execution of a buggy statement rarely leads to failure, whereas a large proportion of test cases failing suggests there are multiple bugs and/or execution of a bug leads to failure relatively frequently. This information can be useful for finding a similarity measure that performs well for locating bugs [NL13].…”

Section: Definition 8 (General Scalable Measure)mentioning

confidence: 99%

“…For software debugging, the discussion of [NL13] can be adapted: at early stages of software development there are almost certain to be multiple bugs and deterministic bugs are relatively likely so a low P ER (a relatively low false positive rate) estimate is reasonable, whereas late in development there are fewer bugs (perhaps just one) and they are typically less consistent, hence a high P ER (a relatively low false negative rate) estimate is desirable. From this an iso-P ER line can be drawn, shown as a dotted line in Figure 9.…”

Section: P Er and Roc Analysismentioning

confidence: 99%

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Naish

2016

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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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Section: A Partial Order For Monotone Measuresmentioning

confidence: 99%

Section: 7nm-symmetrymentioning

confidence: 99%

Section: Interpolating Between Two Boundary Casesmentioning

confidence: 99%

Section: Definition 8 (General Scalable Measure)mentioning

confidence: 99%

Section: P Er and Roc Analysismentioning

confidence: 99%

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Naish

2016

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Spectrum‐Based Techniques for Software Fault Localization

Wong

Lee

Gao³

et al. 2023

Handbook of Software Fault Localization

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Improving spectral‐based fault localization using static analysis

et al. 2017

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Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral-based fault localization (SBFL). It helps to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. SBFL is an example of a dynamic analysis -an analysis of computer program that is performed by executing it with sufficient number of test cases. Static analysis, on the other hand, is performed in a non-runtime environment. We introduce a weighting technique by combining these two kinds of program analysis. Static analysis is performed to categorize program statements into different classes and giving them weights based on the likelihood of being buggy statement. Statements are finally ranked on the basis of the weights computed by statements' categorization (static analysis) and scores computed by SBFL metrics (dynamic analysis). We evaluate the performance of our technique on Siemens test suite and Flex (having seeded bugs seeded by expert developers), Sed (having mixture of real and seeded bugs), and Space (having real bugs). In our evaluation, proposed weighting technique improves the performance of a wide variety of fault localization metrics up to 20% on single bug datasets and up to 42% on multi-bug datasets.We perform our experiments on a range of programs including Siemens test suite, Space, Sed, and Flex. Siemens test suite and Flex contain bugs seeded by expert developers, Sed have both real and seeded bugs, while Space contains real software bugs.

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Duals in Spectral Fault Localization

Cited by 21 publications

References 24 publications

Similarity to a single set

Similarity to a single set

Spectrum‐Based Techniques for Software Fault Localization

Improving spectral‐based fault localization using static analysis

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