Multiple Bug Spectral Fault Localization Using Genetic Programming

Naish, Lee; Neelofar,; Ramamohanarao, Kotagiri

doi:10.1109/aswec.2015.12

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…For each column, the performance peaks at the leading diagonal and decreases monotonically as we move away from the maximum (for the first two models and measures we have checked more significant figures than appear in the table). The experiments described in [NNK15] use the same models but a different class of measures (the contours being hyperbolas with coefficients found using machine learning) and a different performance measure (the rank of the top-most bug rather than all bugs). They also show a trend of reducing m-weight across the models.…”

Section: A Software Debugging Experimentsmentioning

confidence: 99%

“…Here we use six models, each with four statements, where execution of correct statements is statistically independent of test case failure but execution of buggy statements is correlated to varying degrees. The same models were used in [NNK15] to assess learning of similarity measures for a range of data sets. In the first model, M1, only the first statement is a bug.…”

Section: A Software Debugging Experimentsmentioning

confidence: 99%

“…A more empirical approach is to use machine learning techniques such as those in [NNK15] to search for good sub-optimal measures for different scenarios. We believe this approach has great potential but is best guided by theoretical insights to restrict the class of measures considered.…”

Section: Further Workmentioning

confidence: 99%

“…[LCKL15], expanding on [NLK11] and other work compares 157 similarity measures for software debugging. Similarity measures have also been developed and evaluated for software debugging automatically, using methods such as genetic programming [Yoo12,NNK15], leading to numbers of similarity measures in the hundreds of thousands at least.…”

Section: Collections Of Similarity Measuresmentioning

confidence: 99%

“…Without restriction the number of measures is so large that even machine learning techniques run into difficulties. The class of measures used in [NNK15] was chosen in part because the measures are monotone and include measures which are optimal in the two boundary cases. However, most other properties discussed here played no role in the choice.…”

Section: Further Workmentioning

confidence: 99%

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Similarity to a single set

Naish

2016

Preprint

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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 Software Debugging Experimentsmentioning

confidence: 99%

Section: A Software Debugging Experimentsmentioning

confidence: 99%

Section: Further Workmentioning

confidence: 99%

Section: Collections Of Similarity Measuresmentioning

confidence: 99%

Section: Further Workmentioning

confidence: 99%

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Naish

2016

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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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Spectral‐based fault localization using hyperbolic function

2017

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Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral-based fault localization. It tries 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. Here, we propose a restricted class of "hyperbolic" metrics, with a small number of numeric parameters. This class of functions is based on past theoretical and empirical results. We show that optimization methods such as genetic programming and simulated annealing can reliably discover effective metrics over a wide range of data sets of program spectra. We evaluate the performance for both real programs and model programs with single bugs, multiple bugs, "deterministic" bugs, and nondeterministic bugs and find that the proposed class of metrics performs as well as or better than the previous best-performing metrics over a broad range of data.

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Multiple Bug Spectral Fault Localization Using Genetic Programming

Cited by 10 publications

References 25 publications

Similarity to a single set

Similarity to a single set

Improving spectral‐based fault localization using static analysis

Spectral‐based fault localization using hyperbolic function

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