A Genetic Algorithm for Detecting Significant Floating-Point Inaccuracies

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Daming Zou and Ran Wang and Yingfei Xiong and 
                 Lu Zhang and Zhendong Su and Hong Mei",
  title =        "A Genetic Algorithm for Detecting Significant
                 Floating-Point Inaccuracies",
  booktitle =    "37th {IEEE/ACM} International Conference on Software
                 Engineering, ICSE 2015",
  year =         "2015",
  editor =       "Antonia Bertolino and Gerardo Canfora and 
                 Sebastian G. Elbaum",
  volume =       "1",
  pages =        "529--539",
  address =      "Florence, Italy",
  month =        may # " 16-24",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "https://doi.org/10.1109/ICSE.2015.70",
  DOI =          "doi:10.1109/ICSE.2015.70",
  timestamp =    "Thu, 15 Jun 2017 21:42:45 +0200",
  biburl =       "https://dblp.org/rec/bib/conf/icse/ZouWXZSM15",
  bibsource =    "dblp computer science bibliography, https://dblp.org",
  size =         "11 pages",
  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.",
  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