Testing vision-based control systems using learnable evolutionary algorithms
Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorit...
Uložené v:
| Vydané v: | 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) s. 1016 - 1026 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY, USA
ACM
27.05.2018
|
| Edícia: | ACM Conferences |
| Predmet: | |
| ISBN: | 9781450356381, 1450356389 |
| ISSN: | 1558-1225 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures. |
|---|---|
| AbstractList | Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures. |
| Author | Briand, Lionel C. Abdessalem, Raja Ben Nejati, Shiva Stifter, Thomas |
| Author_xml | – sequence: 1 givenname: Raja Ben surname: Abdessalem fullname: Abdessalem, Raja Ben email: benabdessalem@svv.lu organization: University of Luxembourg – sequence: 2 givenname: Shiva surname: Nejati fullname: Nejati, Shiva email: nejati@svv.lu organization: University of Luxembourg – sequence: 3 givenname: Lionel C. surname: Briand fullname: Briand, Lionel C. email: briand@svv.lu organization: University of Luxembourg – sequence: 4 givenname: Thomas surname: Stifter fullname: Stifter, Thomas email: thomas.stifter@iee.lu organization: IEE S.A., Luxembourg |
| BookMark | eNqNkD1PwzAQQM2XRCmdGVgysiT47MRxR1TxJVViKbNlO-diSGIUp5X673FpJyamG97T6e5dkfM-9EjIDdACoKzuOUgKVVX8TkFPyGxeywQorwSXcEomicocGKvO_rBLMovxk1LKhCwliAlZrjCOvl9nWx996HOjIzaZDf04hDaLuzhiF7NN3Cst6qHXpsUMt6HdjMnXwy7T7ToMfvzo4jW5cLqNODvOKXl_elwtXvLl2_Pr4mGZayblmDfOgWXOWT1HME7yEpwVYJiljROSOVlzhpYlhLWtMX2LdcOkMICcG82n5Paw1yOi-h58l-5Qsqz2SRK9O1BtO2VC-IoKqNqnU8d06pguqcU_VWUGj47_AD7CbZI |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| Copyright | 2018 ACM |
| Copyright_xml | – notice: 2018 ACM |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1145/3180155.3180160 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Xplore IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9781450356381 1450356389 |
| EISSN | 1558-1225 |
| EndPage | 1026 |
| ExternalDocumentID | 8453180 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IG 6IL 6IM 6IN AAJGR ABLEC ABQGA ACM ADPZR ALMA_UNASSIGNED_HOLDINGS APO BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK GUFHI IEGSK IJVOP LHSKQ OCL RIB RIC RIE RIL RIO -~X .4S .DC 123 23M 29O 5VS 6IH 6IK 8US AAWTH ADZIZ AFFNX ARCSS AVWKF CHZPO EDO FEDTE I-F I07 IPLJI M43 RNS XOL |
| ID | FETCH-LOGICAL-a288t-dff1c2ffca9e1bf8341fc61b2c0df682f8732ec2bf8e7c7e015e7d286b1e33ba3 |
| IEDL.DBID | RIE |
| ISBN | 9781450356381 1450356389 |
| ISICitedReferencesCount | 190 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000454843300126&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 02:51:12 EDT 2025 Fri Sep 13 11:04:35 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Keywords | automotive software systems software testing evolutionary algorithms search-based software engineering |
| Language | English |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org |
| LinkModel | DirectLink |
| MeetingName | ICSE '18: 40th International Conference on Software Engineering |
| MergedId | FETCHMERGED-LOGICAL-a288t-dff1c2ffca9e1bf8341fc61b2c0df682f8732ec2bf8e7c7e015e7d286b1e33ba3 |
| OpenAccessLink | https://orbilu.uni.lu/handle/10993/33706 |
| PageCount | 11 |
| ParticipantIDs | acm_books_10_1145_3180155_3180160 ieee_primary_8453180 acm_books_10_1145_3180155_3180160_brief |
| PublicationCentury | 2000 |
| PublicationDate | 20180527 2018-May |
| PublicationDateYYYYMMDD | 2018-05-27 2018-05-01 |
| PublicationDate_xml | – month: 05 year: 2018 text: 20180527 day: 27 |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY, USA |
| PublicationPlace_xml | – name: New York, NY, USA |
| PublicationSeriesTitle | ACM Conferences |
| PublicationTitle | 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) |
| PublicationTitleAbbrev | ICSE |
| PublicationYear | 2018 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| SSID | ssj0002684816 ssj0006499 |
| Score | 2.5391304 |
| Snippet | Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex... |
| SourceID | ieee acm |
| SourceType | Publisher |
| StartPage | 1016 |
| SubjectTerms | Automotive engineering Automotive Software Systems Classification algorithms Control systems Decision trees Evolutionary algorithms Evolutionary computation Roads Search-based Software Engineering Software and its engineering -- Software creation and management -- Search-based software engineering Software and its engineering -- Software creation and management -- Software verification and validation -- Software defect analysis -- Software testing and debugging Software Testing Testing |
| Title | Testing vision-based control systems using learnable evolutionary algorithms |
| URI | https://ieeexplore.ieee.org/document/8453180 |
| WOSCitedRecordID | wos000454843300126&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/eLvHCXMwlV3PS8MwFA7b8OBp6ibOX0QQvNjNpj-SHnVseBhjhzl2K0n6Mgeuk64b-N-bpHEiCOKppU2gfLzmvZe873sI3aooicz5jyc5gBeyRHgiYJknWKx4kKiQWobcbETHYzafJ5Maut9zYQDAFp9B19zas_xsLbdmq6zHQm0xTCfodUrjiqu1308xqiXMhDpuFY51KO-kfPww6pl52nV27dXoUda5XP3oqGIdyrD5v085Qu1vZh6e7H3OMapBfoKaX60ZsPtTW2g0NeoZ-QLPLHfce9LOKsP9qi4dO5lybOsFsJVYNQwqPNg5Q-TFB358W6yLZfm62rTRy3Aw7T97rnGCxwljpZcp5UuilOQJ-EIx7amUjH1B5EOmYkYUowEBSfQroJKCRgZoRlgsfAgCwYNT1MjXOZwhHFEVSylYFgidSSrgnJgkh0SE-lxP7qAbDWRqMoJNWpGco9SBnTqwO-juzzGpKJagOqhloE7fK6WN1KF8_vvjC3SowxdWlR9eokZZbOEKHchdudwU19Y8PgFpnrco |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED90Cvo0PybOzwiCL1bX9Ct91OFQnGMPU_ZWkvQyB7pJVwf-9yZpnAiC-NTSJlB-XHN3yf1-B3CqojQy5z-e5IheyFLhiYDlnmCx4kGqwsQy5J66Sa_HhsO0vwTnCy4MItriM7wwt_YsP5_Kd7NVdslCbTFMJ-grURjSVsXWWuyoGN0SZoIdtw7HOph3Yj5-GF2amdp5XtirUaRc5vL1R08V61I69f99zAY0vrl5pL_wOpuwhJMtqH81ZyDuX92G7sDoZ0xG5Mmyx71r7a5y0q4q04kTKie2YoBYkVXDoSI3c2eKvPggVy-jaTEun19nDXjs3Azat55rneBxyljp5Ur5kioleYq-UEz7KiVjX1DZylXMqGJJQFFS_QoTmaBGBpOcslj4GASCBztQm0wnuAskSlQspWB5IHQuqZBzatIcGtHE53pyE040kJnJCWZZRXOOMgd25sBuwtmfYzJRjFE1YdtAnb1VWhuZQ3nv98fHsHY7eOhm3bve_T6s62CGVcWIB1Ari3c8hFU5L8ez4siayicfubpv |
| 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=Proceedings+of+the+40th+International+Conference+on+Software+Engineering&rft.atitle=Testing+vision-based+control+systems+using+learnable+evolutionary+algorithms&rft.au=Abdessalem%2C+Raja+Ben&rft.au=Nejati%2C+Shiva&rft.au=Briand%2C+Lionel+C.&rft.au=Stifter%2C+Thomas&rft.series=ACM+Conferences&rft.date=2018-05-27&rft.pub=ACM&rft.isbn=9781450356381&rft.spage=1016&rft.epage=1026&rft_id=info:doi/10.1145%2F3180155.3180160 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450356381/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450356381/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450356381/sc.gif&client=summon&freeimage=true |