Test case selection and prioritization using machine learning: a systematic literature review

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consu...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Empirical software engineering : an international journal Ročník 27; číslo 2
Hlavní autori:	Pan, Rongqi, Bagherzadeh, Mojtaba, Ghaleb, Taher A., Briand, Lionel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2022 Springer Nature B.V
Predmet:	Compilers Computer Science Interpreters Literature reviews Machine learning Prediction models Programming Languages Questions Software Software Engineering/Programming and Operating Systems Systematic review Taxonomy Software testing Continuous integration Test case selection Test case prioritization Machine learning Systematic literature review
ISSN:	1382-3256, 1573-7616
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies. We summarize the results related to our research questions in a high-level summary that can be used as a taxonomy for classifying future TSP studies.
AbstractList	Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies. We summarize the results related to our research questions in a high-level summary that can be used as a taxonomy for classifying future TSP studies.
ArticleNumber	29
Author	Ghaleb, Taher A. Briand, Lionel Pan, Rongqi Bagherzadeh, Mojtaba
Author_xml	– sequence: 1 givenname: Rongqi surname: Pan fullname: Pan, Rongqi organization: School of Electrical Engineering and Computer Science (EECS), University of Ottawa – sequence: 2 givenname: Mojtaba surname: Bagherzadeh fullname: Bagherzadeh, Mojtaba organization: School of Electrical Engineering and Computer Science (EECS), University of Ottawa – sequence: 3 givenname: Taher A. orcidid: 0000-0001-9336-7298 surname: Ghaleb fullname: Ghaleb, Taher A. email: tghaleb@uottawa.ca organization: School of Electrical Engineering and Computer Science (EECS), University of Ottawa – sequence: 4 givenname: Lionel surname: Briand fullname: Briand, Lionel organization: School of Electrical Engineering and Computer Science (EECS), University of Ottawa, SnT Centre for Security, Reliability and Trust, University of Luxembourg
BookMark	eNp9kM9PwyAUx4mZidv0H_BE4rlKgVLqzSz-Sky8zKMhlL5Olo5OoJr514uriYmHnYCX74f33meGJq53gNB5Ti5zQsqrkBMheEZonqW3EJk4QtO8KFlWilxM0p1JmjFaiBM0C2FNCKlKXkzR6xJCxEYHwAE6MNH2DmvX4K23vbfRful9aQjWrfBGmzfrAHegvUuFa6xx2IUIm5QyuLMRvI6DB-zhw8LnKTpudRfg7Peco5e72-XiIXt6vn9c3DxlhgkWM2ZY23Ba07qqpeGmJrRoCWdQyaZiDJqirDTwludSUEPbgummaWjNtWgLzg2bo4vx363v34e0kVr3g3eppaKCVFJIIWRKyTFlfB-Ch1YZG_frRa9tp3KifmSqUaZKMtVephIJpf_Q5Gej_e4wxEYopLBbgf-b6gD1DYw1itE
CitedBy_id	crossref_primary_10_1109_ACCESS_2024_3488904 crossref_primary_10_1145_3576038 crossref_primary_10_1109_TSE_2024_3469582 crossref_primary_10_1016_j_jss_2025_112360 crossref_primary_10_3390_app142311168 crossref_primary_10_1145_3680467 crossref_primary_10_1016_j_eswa_2024_126086 crossref_primary_10_1109_TSE_2024_3479421 crossref_primary_10_1142_S0219649224500126 crossref_primary_10_1109_ACCESS_2024_3459656 crossref_primary_10_1002_smr_2708 crossref_primary_10_1145_3719006 crossref_primary_10_1016_j_procs_2025_04_658 crossref_primary_10_1002_smr_2506 crossref_primary_10_1007_s11257_024_09398_x crossref_primary_10_1007_s10664_023_10286_y crossref_primary_10_1007_s10664_024_10497_x crossref_primary_10_1007_s10664_024_10539_4 crossref_primary_10_1145_3616372 crossref_primary_10_1145_3730435 crossref_primary_10_1109_TSE_2022_3222160 crossref_primary_10_1007_s00521_022_07627_1 crossref_primary_10_1007_s11219_023_09650_4 crossref_primary_10_3390_app15158596 crossref_primary_10_1016_j_infsof_2024_107448 crossref_primary_10_1016_j_engappai_2024_109776 crossref_primary_10_1007_s10515_022_00344_y crossref_primary_10_1145_3707450 crossref_primary_10_22367_jem_2025_47_05 crossref_primary_10_32604_cmc_2023_032308 crossref_primary_10_3389_fnano_2025_1627210 crossref_primary_10_3390_electronics14071463 crossref_primary_10_1007_s11760_024_03236_8 crossref_primary_10_1109_ACCESS_2024_3490453 crossref_primary_10_1007_s10515_024_00447_8 crossref_primary_10_1109_MS_2025_3546511 crossref_primary_10_1002_smr_70035 crossref_primary_10_2478_jee_2024_0018 crossref_primary_10_3390_app15042243 crossref_primary_10_1145_3644388 crossref_primary_10_1016_j_jss_2022_111419 crossref_primary_10_1109_TSE_2025_3554403 crossref_primary_10_1145_3579851 crossref_primary_10_1145_3511805 crossref_primary_10_1109_TSE_2022_3184842 crossref_primary_10_1109_ACCESS_2024_3424276 crossref_primary_10_1109_TSE_2025_3583027 crossref_primary_10_1007_s10664_025_10708_z crossref_primary_10_1109_MPOT_2023_3342366 crossref_primary_10_1145_3757912 crossref_primary_10_1145_3736758 crossref_primary_10_1109_TSE_2024_3418191 crossref_primary_10_1016_j_scico_2024_103157
Cites_doi	10.1145/3057269 10.1007/978-1-4614-7138-7 10.3233/JIFS-181998 10.1037/0033-2909.114.3.494 10.1162/evco.1995.3.2.149 10.1109/32.962562 10.1109/32.988497 10.2200/S00348ED1V01Y201104HLT012 10.1109/TR.2019.2892517 10.1016/j.infsof.2008.09.009 10.1007/s10664-012-9219-7 10.1109/TSE.2006.92 10.1109/ACCESS.2020.3039931 10.1090/S0002-9904-1952-09620-8 10.35940/ijeat.A9762.109119 10.1007/s11063-019-09999-3 10.1007/s11219-016-9339-1 10.1002/smr.1794 10.1162/neco.1997.9.8.1735 10.1016/j.infsof.2020.106269 10.1016/j.infsof.2020.106268 10.1145/3290353 10.1016/j.infsof.2017.08.014 10.1016/j.jss.2017.03.044 10.1145/2610384.2628055 10.1145/581339.581357 10.1109/SEAA51224.2020.00023 10.1145/1572272.1572296 10.1109/ICSM.2007.4362638 10.1145/775047.775067 10.1145/1277741.1277790 10.1145/3377811.3380369 10.1145/3382025.3414967 10.3917/audi.014.0163 10.1145/3236024.3236053 10.1109/APSIPAASC47483.2019.9023052 10.1145/3127005.3127006 10.1109/ICSE.2001.919106 10.1109/DSAA49011.2020.00076 10.1109/ICMLA.2016.0065 10.1109/ICSM.2011.6080805 10.1109/ICSM.2006.74 10.1109/ICST.2011.38 10.1145/3379597.3387458 10.1145/2950290.2983954 10.18653/v1/2020.findings-emnlp.139 10.1111/j.2517-6161.1974.tb00994.x 10.1109/IEMCON.2019.8936202 10.1109/ICST.2010.47 10.1109/MSR.2017.24 10.1145/3273934.3273944 10.1145/3092703.3092709 10.1109/SERE.2012.18 10.1145/3425174.3425210 10.1145/3377929.3398128 10.1109/ICST.2008.57
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
DBID	AAYXX CITATION 7SC 8FD 8FE 8FG ABJCF AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ JQ2 L6V L7M L~C L~D M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS S0W
DOI	10.1007/s10664-021-10066-6
DatabaseName	CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central SciTech Collection (ProQuest) ProQuest Computer Science Collection ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DELNET Engineering & Technology Collection
DatabaseTitle	CrossRef Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest DELNET Engineering and Technology Collection Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Technology Collection
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1573-7616
ExternalDocumentID	10_1007_s10664_021_10066_6
GrantInformation_xml	– fundername: mitacs funderid: https://doi.org/10.13039/501100004489 – fundername: canada research chairs funderid: https://doi.org/10.13039/501100001804 – fundername: huawei technologies funderid: https://doi.org/10.13039/501100003816
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29G 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 78A 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYOK AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW L6V LAK LLZTM M4Y M7S MA- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P62 P9O PF0 PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S0W S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7V Z7X Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8R Z8T Z8U Z8W Z92 ZMTXR ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 8FD DWQXO JQ2 L7M L~C L~D PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c363t-3c3fd42b2b9b8c4cb025f043e98d933ed579ae4f41862c2f53addd2b4a6f544c3
IEDL.DBID	RSV
ISICitedReferencesCount	85
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000730174900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1382-3256
IngestDate	Tue Dec 02 16:49:16 EST 2025 Sat Nov 29 05:37:45 EST 2025 Tue Nov 18 22:10:51 EST 2025 Fri Feb 21 02:46:17 EST 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Keywords	Software testing Continuous integration Test case selection Test case prioritization Machine learning Systematic literature review
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c363t-3c3fd42b2b9b8c4cb025f043e98d933ed579ae4f41862c2f53addd2b4a6f544c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-9336-7298
OpenAccessLink	http://orbilu.uni.lu/handle/10993/48384
PQID	2609868668
PQPubID	326341
ParticipantIDs	proquest_journals_2609868668 crossref_citationtrail_10_1007_s10664_021_10066_6 crossref_primary_10_1007_s10664_021_10066_6 springer_journals_10_1007_s10664_021_10066_6
PublicationCentury	2000
PublicationDate	2022-03-01
PublicationDateYYYYMMDD	2022-03-01
PublicationDate_xml	– month: 03 year: 2022 text: 2022-03-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: Dordrecht
PublicationSubtitle	An International Journal
PublicationTitle	Empirical software engineering : an international journal
PublicationTitleAbbrev	Empir Software Eng
PublicationYear	2022
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	CR39 Manning, Schutze (CR42) 1999 Khatibsyarbini, Isa, Jawawi, Tumeng (CR31) 2018; 93 CR37 CR35 Jahan, Feng, Mahmud, Dong (CR24) 2019; 36 Robbins (CR53) 1952; 58 CR32 CR30 Kandil, Moussa, Badr (CR28) 2017; 29 CR71 CR70 Rothermel, Untch, Chu, Harrold (CR55) 2001; 27 Almaghairbe, Roper (CR1) 2017; 25 Kitchenham (CR33) 2004; 33 Hochreiter, Schmidhuber (CR23) 1997; 9 CR4 CR3 CR6 CR5 Kazmi, Jawawi, Mohamad, Ghani (CR29) 2017; 50 CR8 CR7 CR9 CR49 Sutton, Barto (CR62) 2018 CR47 CR46 Sharma, Agrawal (CR57) 2019; 9 CR44 CR43 CR40 Mahdieh, Mirian-Hosseinabadi, Etemadi, Nosrati, Jalali (CR41) 2020; 121 Li (CR36) 2011; 4 Medhat, Moussa, Badr, Tolba (CR45) 2020; 8 CR19 CR17 CR16 CR14 CR58 Singh, Bhatia, Singhrova (CR59) 2019; 8 Do, Rothermel (CR13) 2006; 32 CR12 CR56 Cliff (CR10) 1993; 114 CR11 Wilson (CR67) 1995; 3 Zhang, Zhang, Shi, Almpanidis, Fan, Shen (CR72) 2019; 50 CR54 CR52 CR51 CR50 Durelli, Durelli, Borges, Endo, Eler, Dias, Guimaraes (CR15) 2019; 68 Freund, Iyer, Schapire, Singer (CR21) 2003; 4 Lima, Vergilio (CR38) 2020; 121 Thomas, Hemmati, Hassan, Blostein (CR63) 2014; 19 Wilks (CR66) 2011 CR27 CR26 CR69 CR68 CR22 CR65 CR20 Kitchenham, Brereton, Budgen, Turner, Bailey, Linkman (CR34) 2009; 51 CR64 Stone (CR61) 1974; 36 Alon, Zilberstein, Levy, Yahav (CR2) 2019; 3 CR60 Nuñez-Varela, Pérez-Gonzalez, Martínez-Perez, Soubervielle-Montalvo (CR48) 2017; 128 Elbaum, Malishevsky, Rothermel (CR18) 2002; 28 James, Witten, Hastie, Tibshirani (CR25) 2013 N Cliff (10066_CR10) 1993; 114 G Rothermel (10066_CR55) 2001; 27 H Robbins (10066_CR53) 1952; 58 N Medhat (10066_CR45) 2020; 8 JAP Lima (10066_CR38) 2020; 121 AS Nuñez-Varela (10066_CR48) 2017; 128 10066_CR37 H Li (10066_CR36) 2011; 4 10066_CR39 10066_CR4 10066_CR43 10066_CR3 10066_CR44 10066_CR46 MM Sharma (10066_CR57) 2019; 9 10066_CR40 S Hochreiter (10066_CR23) 1997; 9 C Manning (10066_CR42) 1999 10066_CR9 10066_CR8 10066_CR7 RS Sutton (10066_CR62) 2018 10066_CR6 S Elbaum (10066_CR18) 2002; 28 10066_CR5 DS Wilks (10066_CR66) 2011 R Kazmi (10066_CR29) 2017; 50 10066_CR69 10066_CR26 10066_CR27 H Do (10066_CR13) 2006; 32 10066_CR32 10066_CR35 10066_CR30 R Almaghairbe (10066_CR1) 2017; 25 B Kitchenham (10066_CR34) 2009; 51 10066_CR70 Y Freund (10066_CR21) 2003; 4 10066_CR71 A Singh (10066_CR59) 2019; 8 M Khatibsyarbini (10066_CR31) 2018; 93 10066_CR19 10066_CR14 10066_CR58 10066_CR16 10066_CR17 SW Wilson (10066_CR67) 1995; 3 SW Thomas (10066_CR63) 2014; 19 10066_CR65 10066_CR22 10066_CR68 G James (10066_CR25) 2013 10066_CR20 10066_CR64 U Alon (10066_CR2) 2019; 3 10066_CR60 VHS Durelli (10066_CR15) 2019; 68 P Kandil (10066_CR28) 2017; 29 M Mahdieh (10066_CR41) 2020; 121 10066_CR47 10066_CR49 B Kitchenham (10066_CR33) 2004; 33 10066_CR54 M Stone (10066_CR61) 1974; 36 10066_CR11 10066_CR12 10066_CR56 10066_CR50 10066_CR51 10066_CR52 C Zhang (10066_CR72) 2019; 50 H Jahan (10066_CR24) 2019; 36
References_xml	– ident: CR70 – ident: CR22 – ident: CR49 – ident: CR68 – ident: CR4 – ident: CR39 – year: 1999 ident: CR42 publication-title: Foundations of statistical natural language processing – ident: CR16 – year: 2018 ident: CR62 publication-title: Reinforcement learning: An introduction – ident: CR51 – ident: CR12 – ident: CR35 – ident: CR54 – volume: 8 start-page: 700 issue: 3 year: 2019 end-page: 707 ident: CR59 article-title: Machine learning based test case prioritization in object oriented testing publication-title: Int J Recent Technol Eng – ident: CR8 – volume: 50 start-page: 1 issue: 2 year: 2017 end-page: 32 ident: CR29 article-title: Effective regression test case selection: A systematic literature review publication-title: ACM Comput Surv (CSUR) doi: 10.1145/3057269 – ident: CR58 – ident: CR46 – ident: CR71 – ident: CR19 – year: 2013 ident: CR25 publication-title: An introduction to statistical learning, vol 112 doi: 10.1007/978-1-4614-7138-7 – volume: 36 start-page: 6181 issue: 6 year: 2019 end-page: 6194 ident: CR24 article-title: Version specific test case prioritization approach based on artificial neural network publication-title: J Intell Fuzzy Syst doi: 10.3233/JIFS-181998 – volume: 114 start-page: 494 issue: 3 year: 1993 ident: CR10 article-title: Dominance statistics: Ordinal analyses to answer ordinal questions publication-title: Psychol Bull doi: 10.1037/0033-2909.114.3.494 – ident: CR50 – volume: 3 start-page: 149 issue: 2 year: 1995 end-page: 175 ident: CR67 article-title: Classifier fitness based on accuracy publication-title: Evol Comput doi: 10.1162/evco.1995.3.2.149 – volume: 27 start-page: 929 issue: 10 year: 2001 end-page: 948 ident: CR55 article-title: Prioritizing test cases for regression testing publication-title: IEEE Trans Softw Eng doi: 10.1109/32.962562 – ident: CR11 – ident: CR9 – ident: CR32 – ident: CR60 – ident: CR5 – volume: 28 start-page: 159 issue: 2 year: 2002 end-page: 182 ident: CR18 article-title: Test case prioritization: A family of empirical studies publication-title: IEEE Trans Softw Eng doi: 10.1109/32.988497 – ident: CR64 – ident: CR26 – volume: 4 start-page: 1 issue: 1 year: 2011 end-page: 113 ident: CR36 article-title: Learning to rank for information retrieval and natural language processing publication-title: Synth Lect Hum Lang Technol doi: 10.2200/S00348ED1V01Y201104HLT012 – ident: CR43 – ident: CR47 – volume: 68 start-page: 1189 issue: 3 year: 2019 end-page: 1212 ident: CR15 article-title: Machine learning applied to software testing: A systematic mapping study publication-title: IEEE Trans Reliab doi: 10.1109/TR.2019.2892517 – volume: 4 start-page: 933 issue: Nov year: 2003 end-page: 969 ident: CR21 article-title: An efficient boosting algorithm for combining preferences publication-title: J Mach Learn Res – volume: 51 start-page: 7 issue: 1 year: 2009 end-page: 15 ident: CR34 article-title: Systematic literature reviews in software engineering–a systematic literature review publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2008.09.009 – ident: CR14 – volume: 19 start-page: 182 issue: 1 year: 2014 end-page: 212 ident: CR63 article-title: Static test case prioritization using topic models publication-title: Empir Softw Eng doi: 10.1007/s10664-012-9219-7 – volume: 32 start-page: 733 issue: 9 year: 2006 end-page: 752 ident: CR13 article-title: On the use of mutation faults in empirical assessments of test case prioritization techniques publication-title: IEEE Trans Softw Eng doi: 10.1109/TSE.2006.92 – ident: CR37 – volume: 8 start-page: 215716 year: 2020 end-page: 215726 ident: CR45 article-title: A framework for continuous regression and integration testing in iot systems based on deep learning and search-based techniques publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3039931 – ident: CR30 – year: 2011 ident: CR66 publication-title: Statistical methods in the atmospheric sciences, vol 100 – volume: 58 start-page: 527 issue: 5 year: 1952 end-page: 535 ident: CR53 article-title: Some aspects of the sequential design of experiments publication-title: Bull Am Math Soc doi: 10.1090/S0002-9904-1952-09620-8 – volume: 9 start-page: 2742 issue: 1 year: 2019 end-page: 2748 ident: CR57 article-title: Test case design and test case prioritization using machine learning publication-title: Int J Eng Adv Technol doi: 10.35940/ijeat.A9762.109119 – volume: 50 start-page: 957 issue: 1 year: 2019 end-page: 987 ident: CR72 article-title: On incremental learning for gradient boosting decision trees publication-title: Neural Process Lett doi: 10.1007/s11063-019-09999-3 – volume: 33 start-page: 1 issue: 2004 year: 2004 end-page: 26 ident: CR33 article-title: Procedures for performing systematic reviews publication-title: Keele, UK, Keele University – volume: 36 start-page: 111 issue: 2 year: 1974 end-page: 133 ident: CR61 article-title: Cross-validatory choice and assessment of statistical predictions publication-title: J R Stat Soc Ser B (Methodol) – ident: CR6 – ident: CR56 – volume: 25 start-page: 803 issue: 3 year: 2017 end-page: 840 ident: CR1 article-title: Separating passing and failing test executions by clustering anomalies publication-title: Softw Qual J doi: 10.1007/s11219-016-9339-1 – volume: 29 start-page: e1794 issue: 6 year: 2017 ident: CR28 article-title: Cluster-based test cases prioritization and selection technique for agile regression testing publication-title: J Softw Evol Process doi: 10.1002/smr.1794 – ident: CR40 – ident: CR27 – volume: 9 start-page: 1735 issue: 8 year: 1997 end-page: 1780 ident: CR23 article-title: Long short-term memory publication-title: Neural Comput doi: 10.1162/neco.1997.9.8.1735 – ident: CR69 – ident: CR44 – volume: 121 start-page: 106269 year: 2020 ident: CR41 article-title: Incorporating fault-proneness estimations into coverage-based test case prioritization methods publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2020.106269 – ident: CR65 – ident: CR3 – volume: 121 start-page: 106268 year: 2020 ident: CR38 article-title: Test case prioritization in continuous integration environments A systematic mapping study publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2020.106268 – ident: CR52 – ident: CR17 – volume: 3 start-page: 1 issue: POPL year: 2019 end-page: 29 ident: CR2 article-title: code2vec: Learning distributed representations of code publication-title: Proc ACM Programm Lang doi: 10.1145/3290353 – volume: 93 start-page: 74 year: 2018 end-page: 93 ident: CR31 article-title: Test case prioritization approaches in regression testing: A systematic literature review publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2017.08.014 – volume: 128 start-page: 164 year: 2017 end-page: 197 ident: CR48 article-title: Source code metrics: A systematic mapping study publication-title: J Syst Softw doi: 10.1016/j.jss.2017.03.044 – ident: CR7 – ident: CR20 – volume: 4 start-page: 933 issue: Nov year: 2003 ident: 10066_CR21 publication-title: J Mach Learn Res – ident: 10066_CR52 – ident: 10066_CR27 doi: 10.1145/2610384.2628055 – volume: 8 start-page: 700 issue: 3 year: 2019 ident: 10066_CR59 publication-title: Int J Recent Technol Eng – ident: 10066_CR32 doi: 10.1145/581339.581357 – ident: 10066_CR3 doi: 10.1109/SEAA51224.2020.00023 – ident: 10066_CR70 doi: 10.1145/1572272.1572296 – ident: 10066_CR14 – volume: 33 start-page: 1 issue: 2004 year: 2004 ident: 10066_CR33 publication-title: Keele, UK, Keele University – ident: 10066_CR68 doi: 10.1109/ICSM.2007.4362638 – ident: 10066_CR26 doi: 10.1145/775047.775067 – ident: 10066_CR37 – volume: 50 start-page: 957 issue: 1 year: 2019 ident: 10066_CR72 publication-title: Neural Process Lett doi: 10.1007/s11063-019-09999-3 – ident: 10066_CR56 – ident: 10066_CR71 doi: 10.1145/1277741.1277790 – volume: 8 start-page: 215716 year: 2020 ident: 10066_CR45 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3039931 – ident: 10066_CR5 doi: 10.1145/3377811.3380369 – volume: 51 start-page: 7 issue: 1 year: 2009 ident: 10066_CR34 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2008.09.009 – ident: 10066_CR40 doi: 10.1145/3382025.3414967 – ident: 10066_CR51 – volume: 19 start-page: 182 issue: 1 year: 2014 ident: 10066_CR63 publication-title: Empir Softw Eng doi: 10.1007/s10664-012-9219-7 – ident: 10066_CR11 doi: 10.3917/audi.014.0163 – volume: 3 start-page: 149 issue: 2 year: 1995 ident: 10066_CR67 publication-title: Evol Comput doi: 10.1162/evco.1995.3.2.149 – volume: 29 start-page: e1794 issue: 6 year: 2017 ident: 10066_CR28 publication-title: J Softw Evol Process doi: 10.1002/smr.1794 – volume: 93 start-page: 74 year: 2018 ident: 10066_CR31 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2017.08.014 – ident: 10066_CR9 doi: 10.1145/3236024.3236053 – ident: 10066_CR22 doi: 10.1109/APSIPAASC47483.2019.9023052 – volume: 3 start-page: 1 issue: POPL year: 2019 ident: 10066_CR2 publication-title: Proc ACM Programm Lang doi: 10.1145/3290353 – volume: 28 start-page: 159 issue: 2 year: 2002 ident: 10066_CR18 publication-title: IEEE Trans Softw Eng doi: 10.1109/32.988497 – volume: 25 start-page: 803 issue: 3 year: 2017 ident: 10066_CR1 publication-title: Softw Qual J doi: 10.1007/s11219-016-9339-1 – ident: 10066_CR47 doi: 10.1145/3127005.3127006 – ident: 10066_CR17 doi: 10.1109/ICSE.2001.919106 – ident: 10066_CR20 – ident: 10066_CR58 doi: 10.1109/DSAA49011.2020.00076 – volume: 27 start-page: 929 issue: 10 year: 2001 ident: 10066_CR55 publication-title: IEEE Trans Softw Eng doi: 10.1109/32.962562 – ident: 10066_CR35 doi: 10.1109/ICMLA.2016.0065 – ident: 10066_CR7 doi: 10.1109/ICSM.2011.6080805 – volume: 36 start-page: 6181 issue: 6 year: 2019 ident: 10066_CR24 publication-title: J Intell Fuzzy Syst doi: 10.3233/JIFS-181998 – volume: 121 start-page: 106269 year: 2020 ident: 10066_CR41 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2020.106269 – ident: 10066_CR64 doi: 10.1109/ICSM.2006.74 – ident: 10066_CR8 doi: 10.1109/ICST.2011.38 – ident: 10066_CR43 doi: 10.1145/3379597.3387458 – volume-title: Reinforcement learning: An introduction year: 2018 ident: 10066_CR62 – ident: 10066_CR6 doi: 10.1145/2950290.2983954 – ident: 10066_CR16 – ident: 10066_CR44 – ident: 10066_CR12 – volume: 32 start-page: 733 issue: 9 year: 2006 ident: 10066_CR13 publication-title: IEEE Trans Softw Eng doi: 10.1109/TSE.2006.92 – ident: 10066_CR19 doi: 10.18653/v1/2020.findings-emnlp.139 – volume-title: An introduction to statistical learning, vol 112 year: 2013 ident: 10066_CR25 doi: 10.1007/978-1-4614-7138-7 – volume: 36 start-page: 111 issue: 2 year: 1974 ident: 10066_CR61 publication-title: J R Stat Soc Ser B (Methodol) doi: 10.1111/j.2517-6161.1974.tb00994.x – ident: 10066_CR30 doi: 10.1109/IEMCON.2019.8936202 – ident: 10066_CR69 doi: 10.1109/ICST.2010.47 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10066_CR23 publication-title: Neural Comput doi: 10.1162/neco.1997.9.8.1735 – volume: 128 start-page: 164 year: 2017 ident: 10066_CR48 publication-title: J Syst Softw doi: 10.1016/j.jss.2017.03.044 – volume: 50 start-page: 1 issue: 2 year: 2017 ident: 10066_CR29 publication-title: ACM Comput Surv (CSUR) doi: 10.1145/3057269 – ident: 10066_CR4 doi: 10.1109/MSR.2017.24 – volume-title: Statistical methods in the atmospheric sciences, vol 100 year: 2011 ident: 10066_CR66 – volume: 68 start-page: 1189 issue: 3 year: 2019 ident: 10066_CR15 publication-title: IEEE Trans Reliab doi: 10.1109/TR.2019.2892517 – ident: 10066_CR49 doi: 10.1145/3273934.3273944 – ident: 10066_CR60 doi: 10.1145/3092703.3092709 – volume-title: Foundations of statistical natural language processing year: 1999 ident: 10066_CR42 – volume: 58 start-page: 527 issue: 5 year: 1952 ident: 10066_CR53 publication-title: Bull Am Math Soc doi: 10.1090/S0002-9904-1952-09620-8 – volume: 114 start-page: 494 issue: 3 year: 1993 ident: 10066_CR10 publication-title: Psychol Bull doi: 10.1037/0033-2909.114.3.494 – volume: 9 start-page: 2742 issue: 1 year: 2019 ident: 10066_CR57 publication-title: Int J Eng Adv Technol doi: 10.35940/ijeat.A9762.109119 – ident: 10066_CR65 doi: 10.1109/SERE.2012.18 – ident: 10066_CR39 doi: 10.1145/3425174.3425210 – ident: 10066_CR50 doi: 10.1145/3425174.3425210 – volume: 4 start-page: 1 issue: 1 year: 2011 ident: 10066_CR36 publication-title: Synth Lect Hum Lang Technol doi: 10.2200/S00348ED1V01Y201104HLT012 – volume: 121 start-page: 106268 year: 2020 ident: 10066_CR38 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2020.106268 – ident: 10066_CR54 doi: 10.1145/3377929.3398128 – ident: 10066_CR46 doi: 10.1109/ICST.2008.57
SSID	ssj0009745
Score	2.609264
Snippet	Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
SubjectTerms	Compilers Computer Science Interpreters Literature reviews Machine learning Prediction models Programming Languages Questions Software Software Engineering/Programming and Operating Systems Systematic review Taxonomy
SummonAdditionalLinks	– databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NS8MwGA46PXhxfuL8IgdvGtySNG28iIjD0xCcsIuUfLRDmN1cp7_fN2m6ouAuntuG0CfJ8yZ53-dB6EIBa-QQCRNH54RHiSXKwK5VUSqVURrmF_dmE_FgkIxG8ikcuJUhrbJeE_1CbafGnZFfQ9wtE5EIkdzOPohzjXK3q8FCYx1tOJWEnk_de25Ed2NvUuxk9ggDbg9FM6F0TghOXIJCz9EuET-JqYk2f12Qet7pt__b4x20HSJOfFcNkV20lhV7qF27OeAwuffR6xCaxgZYDZfeHAcQw6qwGDo2ddJHVcEmdpnyY_zukzAzHFwnxjdY4UYVGk-Was24Ko45QC_9h-H9IwnmC8QwwRaEGZZbTjXVUieGGw3BUd7lLJOJlYxlNoqlynjOe7AnMjSPGKyUlmquBGDPDTtErWJaZEcIRznTXvc9V5bbWMMIgLhQd5mJqOQ566Be_edTE5TJnUHGJG00lR1aKaCVerRS0UGXy29mlS7HyrdPa4jSMEfLtMGng65qkJvHf7d2vLq1E7RFXY2ET1Q7Ra3F_DM7Q5vma_FWzs_9CP0GRePr6w priority: 102 providerName: ProQuest
Title	Test case selection and prioritization using machine learning: a systematic literature review
URI	https://link.springer.com/article/10.1007/s10664-021-10066-6 https://www.proquest.com/docview/2609868668
Volume	27
WOSCitedRecordID	wos000730174900003&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
journalDatabaseRights	– providerCode: PRVAVX databaseName: Springer Link Journals customDbUrl: eissn: 1573-7616 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009745 issn: 1382-3256 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PS8MwFH7o9ODF-ROnc-TgTQNbk6atN5UNT2NsU4YgJUnbIcxO1unf70varioq6KWXpkl5L8n3Qt77PoAziaiRYCRMDZxT7voRlRpPrdJxAqmlwvXFrdiE1-_7k0kwKIrCsjLbvbyStDv1h2I3ITg1KQUdA5RUrMMGwp1vBBuGo_uKatez0sSGXI8yRPSiVOb7Pj7DURVjfrkWtWjTq__vP3dgu4guyVU-HXZhLU73oF4qN5BiIe_D4xiHJxoRjGRWCAe9Q2QaERxqbmiO8uJMYrLip-TZJlzGpFCYmF4SSSoGaDJbMTOTvBDmAO563fHNLS2EFqhmgi0p0yyJuKMcFShfc60wEEranMWBHwWMxZHrBTLmCe_g-Uc7ictwV4wcxaVAP3PNDqGWztP4CIibMGU53hMZ8chT6G2MAVWbadcJeMIa0CntHeqChdyIYczCij_Z2C9E-4XWfqFowPnqm5ecg-PX1s3SjWGxHrMQT22BL3wh_AZclG6rXv_c2_Hfmp_AlmPqI2ySWhNqy8VrfAqb-m35lC1asHHd7Q-GLZNgOsLnwH1o2bn7DrMH5TE
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LSsQwFL3oKOjGtzg-s9CVBmeSNNMKIuIDRR0ER3AjNY9WBJ0ZnVHxp_xGb9LWoqA7F67bBpKe3HvS3nsOwKrCrJEiE6YunVMRhJYqg6dWxVikjNK4v4Q3m2g0m-HVVXQ-AO9FL4wrqyxiog_UtmPcN_JN5N1RKEMpw53uI3WuUe7vamGhkcHiJHl7xSNbb_t4H9_vGmOHB629I5q7ClDDJe9TbnhqBdNMRzo0wmjM-mlN8CQKLZ7uExs0IpWIVNSR7BuWBhxDgGVaKImTEobjuIMwhDSCRb5U8KIU-W14U2Qn60c5com8SSdv1ZNSUFcQUXdpnsqvibBkt99-yPo8dzj-31ZoAsZyRk12sy0wCQNJewrGC7cKkgevabhu4VSIwaxNet78BxFJVNsSXIiOk3bKGlKJ6wS4JQ--yDQhuavG7RZRpFS9JvefatQka_6Zgcs_meQsVNqddjIHJEi59rr2qbLCNjQiHHmvrnETsEikvAr14k3HJldedwYg93GpGe3QESM6Yo-OWFZh_fOZbqY78uvdiwUk4jwG9eISD1XYKEBVXv55tPnfR1uBkaPW2Wl8etw8WYBR5vpBfFHeIlT6T8_JEgybl_5d72nZ7w4CN38Ntg8sX0pH
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bSxwxFD5YldIXtbXi1kvzYJ806CaZzIwgIupSUZZ9UBBBprlMRLC71l0V_5q_zpNMxkFB33zo88wEknw5l8k53wewotBrOIyEqXfnVCSZpcpg1qoYy5VRGs-XCGITabebnZ7mvTF4rHthfFllbRODobYD4_-Rr2PcnWcykzJbd7EsorfX2b7-R72ClL9preU0Kogclg_3mL4Ntw72cK9_MdbZP979TaPCADVc8hHlhjsrmGY615kRRmME4DYEL_PMYqZf2iTNVSmcaGPgb5hLOJoDy7RQEicoDMdxP8FEijmmLyfsJWcN4W8aBJI9xR_lGFfEhp3YtieloL44ou1dPpUvnWIT6b66nA0-rzP9P6_WDEzFSJvsVEfjK4yV_W8wXatYkGjUZuH8GKdFDHpzMgyiQIhUovqW4KIMPOVT1ahKfIfABfkbik9LEtU2LjaJIg0bNrl6ZqkmVVPQdzj5kEnOwXh_0C_ngSSO68B375QVNtWIfIyH9QY3CcuF4y1o17temMjI7oVBroqGS9ojpUCkFAEphWzB6vM31xUfybtvL9bwKKJtGhYNNlqwVgOsefz2aD_eH-0nfEaMFUcH3cMF-MJ8m0io1VuE8dHNbbkEk-ZudDm8WQ4HhcCfj8baE7AWUyA
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%3Ajournal&rft.genre=article&rft.atitle=Test+case+selection+and+prioritization+using+machine+learning%3A+a+systematic+literature+review&rft.jtitle=Empirical+software+engineering+%3A+an+international+journal&rft.au=Pan%2C+Rongqi&rft.au=Bagherzadeh%2C+Mojtaba&rft.au=Ghaleb%2C+Taher+A.&rft.au=Briand%2C+Lionel&rft.date=2022-03-01&rft.pub=Springer+US&rft.issn=1382-3256&rft.eissn=1573-7616&rft.volume=27&rft.issue=2&rft_id=info:doi/10.1007%2Fs10664-021-10066-6&rft.externalDocID=10_1007_s10664_021_10066_6
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1382-3256&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1382-3256&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1382-3256&client=summon