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Discovering discrepancies in numerical libraries

Authors:

Jackson Vanover,

Cindy Rubio-GonzálezAuthors Info & Claims

ISSTA 2020: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 488 - 501

https://doi.org/10.1145/3395363.3397380

Published: 18 July 2020 Publication History

Abstract

Numerical libraries constitute the building blocks for software applications that perform numerical calculations. Thus, it is paramount that such libraries provide accurate and consistent results. To that end, this paper addresses the problem of finding discrepancies between synonymous functions in different numerical libraries as a means of identifying incorrect behavior. Our approach automatically finds such synonymous functions, synthesizes testing drivers, and executes differential tests to discover meaningful discrepancies across numerical libraries. We implement our approach in a tool named FPDiff, and provide an evaluation on four popular numerical libraries: GNU Scientific Library (GSL), SciPy, mpmath, and jmat. FPDiff finds a total of 126 equivalence classes with a 95.8% precision and 79% recall, and discovers 655 instances in which an input produces a set of disagreeing outputs between function synonyms, 150 of which we found to represent 125 unique bugs. We have reported all bugs to library maintainers; so far, 30 bugs have been fixed, 9 have been found to be previously known, and 25 more have been acknowledged by developers.

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Xia ZChen YNie PWang Z(2024)Detecting Numerical Deviations in Deep Learning Models Introduced by the TVM Compiler2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00018(73-83)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00018
Zou DGu YShi YWang MXiong YSu Z(2022)Oracle-free repair synthesis for floating-point programsProceedings of the ACM on Programming Languages10.1145/35633226:OOPSLA2(957-985)Online publication date: 31-Oct-2022
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Index Terms

Discovering discrepancies in numerical libraries
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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cover image ACM Conferences

ISSTA 2020: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2020

591 pages

ISBN:9781450380089

DOI:10.1145/3395363

General Chair:
Sarfraz Khurshid
University of Texas at Austin, USA
,
Program Chair:
Corina S. Păsăreanu
Carnegie Mellon University Silicon Valley / NASA Ames Research Center, USA

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 18 July 2020

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ISSTA '20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 18 - 22, 2020

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Overall Acceptance Rate 58 of 213 submissions, 27%

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https://dl.acm.org/doi/10.1109/TSE.2024.3461657
Xia ZChen YNie PWang Z(2024)Detecting Numerical Deviations in Deep Learning Models Introduced by the TVM Compiler2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00018(73-83)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00018
Zou DGu YShi YWang MXiong YSu Z(2022)Oracle-free repair synthesis for floating-point programsProceedings of the ACM on Programming Languages10.1145/35633226:OOPSLA2(957-985)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1145/3563322
Deng YYang CWei AZhang LRoychoudhury ACadar CKim M(2022)Fuzzing deep-learning libraries via automated relational API inferenceProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549085(44-56)Online publication date: 7-Nov-2022
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