NDLI: A Genetic Algorithm for Detecting Significant Floating-Point Inaccuracies

Content Provider	IEEE Xplore Digital Library
Author	Daming Zou Ran Wang Yingfei Xiong Lu Zhang Zhendong Su Hong Mei
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of California, Davis, Davis, CA, USA (Zhendong Su) \|\| Key Lab. of High Confidence Software Technol., Peking Univ., Beijing, China (Daming Zou; Ran Wang; Yingfei Xiong; Lu Zhang; Hong Mei)
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.
Starting Page	529
Ending Page	539
File Size	894647
Page Count	11
File Format	PDF
ISBN	9781479919345
DOI	10.1109/ICSE.2015.70
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-16
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Sociology Statistics Algorithm design and analysis Accuracy Search problems Software
Content Type	Text
Resource Type	Article

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