A Genetic Algorithm for Detecting Significant Floating-Point Inaccuracies
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{DBLP:conf/icse/ZouWXZSM15,
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author = "Daming Zou and Ran Wang and Yingfei Xiong and
Lu Zhang and Zhendong Su and Hong Mei",
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title = "A Genetic Algorithm for Detecting Significant
Floating-Point Inaccuracies",
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booktitle = "37th {IEEE/ACM} International Conference on Software
Engineering, ICSE 2015",
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year = "2015",
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editor = "Antonia Bertolino and Gerardo Canfora and
Sebastian G. Elbaum",
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volume = "1",
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pages = "529--539",
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address = "Florence, Italy",
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month = may # " 16-24",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, SBSE",
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URL = "https://doi.org/10.1109/ICSE.2015.70",
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DOI = "doi:10.1109/ICSE.2015.70",
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timestamp = "Thu, 15 Jun 2017 21:42:45 +0200",
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biburl = "https://dblp.org/rec/bib/conf/icse/ZouWXZSM15",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "11 pages",
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abstract = "It is well-known that using floating-point numbers may
inevitably result in inaccurate results and sometimes
even cause serious software failures. Safety-critical
software often has strict requirements on the upper
bound of inaccuracy, and a crucial task in testing is
to check whether significant inaccuracies may be
produced. The main existing approach to the
floating-point inaccuracy problem is error analysis,
which produces an upper bound of inaccuracies that may
occur. However, a high upper bound does not guarantee
the existence of inaccuracy defects, nor does it give
developers any concrete test inputs for debugging. In
this paper, we propose the first metaheuristic
search-based approach to automatically generating test
inputs that aim to trigger significant inaccuracies in
floating-point programs. Our approach is based on the
following two insights: (1) with FPDebug, a recently
proposed dynamic analysis approach, we can build a
reliable fitness function to guide the search; (2) two
main factors - the scales of exponents and the bit
formations of significands - may have significant
impact on the accuracy of the output, but in largely
different ways. We have implemented and evaluated our
approach over 154 real-world floating-point functions.
The results show that our approach can detect
significant inaccuracies in the subjects.",
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notes = "uses genetic programming to generate test input for
floating-point programs",
- }
Genetic Programming entries for
Daming Zou
Ran Wang
Yingfei Xiong
Lu Zhang
Zhendong Su
Hong Mei
Citations