Testing Framework for Black-box AI Models

With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automated test generation for different modalities such as text, tab...

Full description

Saved in:
Bibliographic Details
Published in:2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) pp. 81 - 84
Main Authors: Aggarwal, Aniya, Shaikh, Samiulla, Hans, Sandeep, Haldar, Swastik, Ananthanarayanan, Rema, Saha, Diptikalyan
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2021
Subjects:
Artificial intelligence
Decision making
Robustness
Software engineering
Test pattern generators
Testing
ISBN:1665412194, 9781665412193
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automated test generation for different modalities such as text, tabular, and time-series data and across various properties such as accuracy, fairness, and robustness. Our tool has been used for testing industrial AI models and was very effective to uncover issues present in those models. Demo video link-https://youtu.be/984UCU17YZI
AbstractList With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automated test generation for different modalities such as text, tabular, and time-series data and across various properties such as accuracy, fairness, and robustness. Our tool has been used for testing industrial AI models and was very effective to uncover issues present in those models. Demo video link-https://youtu.be/984UCU17YZI
Author Haldar, Swastik
Saha, Diptikalyan
Ananthanarayanan, Rema
Shaikh, Samiulla
Aggarwal, Aniya
Hans, Sandeep
Author_xml – sequence: 1
  givenname: Aniya
  surname: Aggarwal
  fullname: Aggarwal, Aniya
  email: aniyaagg@in.ibm.com
  organization: IBM Research AI
– sequence: 2
  givenname: Samiulla
  surname: Shaikh
  fullname: Shaikh, Samiulla
  email: samiullas@in.ibm.com
  organization: IBM Research AI
– sequence: 3
  givenname: Sandeep
  surname: Hans
  fullname: Hans, Sandeep
  email: shans001@in.ibm.com
  organization: IBM Research AI
– sequence: 4
  givenname: Swastik
  surname: Haldar
  fullname: Haldar, Swastik
  email: swhalda1@in.ibm.com
  organization: IBM Research AI
– sequence: 5
  givenname: Rema
  surname: Ananthanarayanan
  fullname: Ananthanarayanan, Rema
  email: arema@in.ibm.com
  organization: IBM Research AI
– sequence: 6
  givenname: Diptikalyan
  surname: Saha
  fullname: Saha, Diptikalyan
  email: diptsaha@in.ibm.com
  organization: IBM Research AI
BookMark eNotjE1Lw0AUABdU0Nb-Ai85CR4S39tP91hDq4GWHqznstt9K6FJtmwK6r-3UE9zGGYm7HpIAzH2iFAhgn1u6o9FWaf-6IY2DYprUBUHjhUASLxiE9RaSeRo5S2bjWPrQUpjz6m5Y09bGk_t8FUss-vpO-VDEVMuXju3P5Q-_RTzplinQN14z26i60aa_XPKPpeLbf1erjZvTT1flU6AOpXB2hC1A6447JWPEQ3GaL0P5IBMlN6BQI_aWH8W0b9YHZwPnEcTSEgxZQ-Xb0tEu2Nue5d_d1YCF9yKP1zwRfI
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICSE-Companion52605.2021.00041
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library Online
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library Online
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EndPage 84
ExternalDocumentID 9402329
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAWTH
ABLEC
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-a305t-d99df6a02520c5bff171ff9bbdea0e7f4ba031b1679b1fffb896dabd22f7de343
IEDL.DBID RIE
ISBN 1665412194
9781665412193
ISICitedReferencesCount 6
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000706450400021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:50:24 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a305t-d99df6a02520c5bff171ff9bbdea0e7f4ba031b1679b1fffb896dabd22f7de343
PageCount 4
ParticipantIDs ieee_primary_9402329
PublicationCentury 2000
PublicationDate 2021-May
PublicationDateYYYYMMDD 2021-05-01
PublicationDate_xml – month: 05
  year: 2021
  text: 2021-May
PublicationDecade 2020
PublicationTitle 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
PublicationTitleAbbrev ICSE-COMPANION
PublicationYear 2021
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssib044791097
Score 2.2444828
Snippet With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we...
SourceID ieee
SourceType Publisher
StartPage 81
SubjectTerms Artificial intelligence
Decision making
Robustness
Software engineering
Test pattern generators
Testing
Title Testing Framework for Black-box AI Models
URI https://ieeexplore.ieee.org/document/9402329
WOSCitedRecordID wos000706450400021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED-2IeKTyiZ-0wcRBOOaNG2aRxkbDmQMnLK30TQXEaSTfYh_vpe2mwi--JakIeSS_sgld_c7gCsuMDEyCplXFpg0mWA6ipHRXnMTyzxOSjqGl0c1GqXTqR434HYbC4OIpfMZ3vliacu383ztn8q6mi47kdBNaCqVVLFam39HSqW9MdXHbvmEupyQKGtKp0092oXrmmOzO-w99VkFOZI_9lo9XRYFL9k7-a9UK-VJM9j_3xwPoPMTsheMt4fRITSwaMPNxHNoFK_BYOOCFZCOGpSPdszMv4L7YeCTob0vO_A86E96D6zOjcAyQuiKWa2tSzLSWESYx8Y5rrhz2hiLWYjK0aoTXI03shj64EyqE5sZK4RTFiMZHUGrmBd4DEFuUCIhWyrCJk9znaDNUupNw8k8dCfQ9mLOPir6i1kt4enfzWew59ex8gk8h9ZqscYL2Mk_V2_LxWW5Z9-CHJLq
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mFPVJZRO_zYMIgnFNm37kUcbGhnMMnLK30TQXEaSTfYh_vpeumwi--Na0oTSX_sgld_f7AVwJHyMtA487Z4FLnfpcBSFymmuhQ5mFUUHH8NKL-_1kNFKDCtyua2EQsUg-wzt3WcTyzSRbuKOyhqLNTuCrDdh0yllltdbq75EyVi6c6qq3nKSuICzKktRp1Q624bpk2Wx0m08tvgQdWSB0fj1tF31R8HeKX2IrxVrT3vvfV-5D_adojw3Wy9EBVDCvwc3QsWjkr6y9SsJi5KWy4tiO68kXu-8yJ4f2PqvDc7s1bHZ4qY7AU8LonBuljI1S8ll8Lwu1tSIW1iqtDaYexpbsToDVLsyi6YHViYpMqo3v29hgIINDqOaTHI-AZRolErZlTOgUSaYiNGlCvel1MvPsMdTcMMcfSwKMcTnCk79vX8JOZ_jYG_e6_YdT2HU2XWYInkF1Pl3gOWxln_O32fSimL9vX_eWMw
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2021+IEEE%2FACM+43rd+International+Conference+on+Software+Engineering%3A+Companion+Proceedings+%28ICSE-Companion%29&rft.atitle=Testing+Framework+for+Black-box+AI+Models&rft.au=Aggarwal%2C+Aniya&rft.au=Shaikh%2C+Samiulla&rft.au=Hans%2C+Sandeep&rft.au=Haldar%2C+Swastik&rft.date=2021-05-01&rft.pub=IEEE&rft.isbn=9781665412193&rft.spage=81&rft.epage=84&rft_id=info:doi/10.1109%2FICSE-Companion52605.2021.00041&rft.externalDocID=9402329
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781665412193/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781665412193/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781665412193/sc.gif&client=summon&freeimage=true