Search-based fairness testing for regression-based machine learning systems
Context Machine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been...
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| Vydané v: | Empirical software engineering : an international journal Ročník 27; číslo 3 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.05.2022
Springer Nature B.V |
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| ISSN: | 1382-3256, 1573-7616 |
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| Abstract | Context
Machine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems.
Method:
We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software.
Results:
We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches.
Conclusion:
These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it. |
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| AbstractList | ContextMachine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems.Method:We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software.Results:We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches.Conclusion:These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it. Context Machine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems. Method: We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software. Results: We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches. Conclusion: These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it. |
| ArticleNumber | 79 |
| Author | Aleti, Aldeida Turhan, Burak Kuhn, Lisa Walker, Katie Jiarpakdee, Jirayus Perera, Anjana Tantithamthavorn, Chakkrit |
| Author_xml | – sequence: 1 givenname: Anjana orcidid: 0000-0002-5080-9276 surname: Perera fullname: Perera, Anjana email: Anjana.Perera@monash.edu organization: Faculty of Information Technology, Monash University – sequence: 2 givenname: Aldeida surname: Aleti fullname: Aleti, Aldeida organization: Faculty of Information Technology, Monash University – sequence: 3 givenname: Chakkrit surname: Tantithamthavorn fullname: Tantithamthavorn, Chakkrit organization: Faculty of Information Technology, Monash University – sequence: 4 givenname: Jirayus surname: Jiarpakdee fullname: Jiarpakdee, Jirayus organization: Faculty of Information Technology, Monash University – sequence: 5 givenname: Burak surname: Turhan fullname: Turhan, Burak email: burak.turhan@oulu.fi organization: Faculty of Information Technology and Electrical Engineering, University of Oulu – sequence: 6 givenname: Lisa surname: Kuhn fullname: Kuhn, Lisa organization: Faculty of Medicine, Nursing and Health Sciences, Monash University – sequence: 7 givenname: Katie surname: Walker fullname: Walker, Katie organization: Faculty of Medicine, Nursing and Health Sciences, Monash University |
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| Cites_doi | 10.1109/RE.2019.00050 10.1016/j.hlc.2020.06.025 10.1007/978-3-030-32489-6_5 10.35680/2372-0247.1090 10.1007/s10115-011-0463-8 10.1002/stvr.1486 10.1016/j.jemermed.2011.01.018 10.1145/3238147.3238165 10.1007/s11739-014-1154-8 10.1109/ICST.2019.00022 10.1145/1068009.1068185 10.1109/MSPEC.2016.7473150 10.1109/TSE.2005.12 10.2307/4093259 10.1145/2931037.2931054 10.1145/3287560.3287598 10.1007/978-3-319-99241-9_1 10.1145/3097983.3098095 10.1145/3368089.3409704 10.1023/A:1008119029962 10.1186/s12889-016-3954-4 10.1145/3278721.3278779 10.1016/j.infsof.2012.03.009 10.1038/d41586-019-03228-6 10.1145/3468264.3468537 10.1145/2020408.2020488 10.1109/TIFS.2012.2214212 10.1109/ICSE43902.2021.00129 10.1111/1742-6723.13640 10.1145/3338906.3338937 10.1145/3106237.3106277 10.1016/S0140-6736(21)00684-X 10.1109/ICDMW.2011.83 10.1109/TSE.1980.230492 10.1609/aaai.v33i01.33017801 10.1109/ICST.2015.7102604 10.1016/j.ijcard.2016.06.244 10.1101/2021.03.19.21253921 10.1016/j.jacc.2017.05.024 10.18653/v1/W17-1606 10.1109/EuroSP.2017.29 10.1016/j.infsof.2018.08.009 10.1145/2090236.2090255 10.1007/978-3-540-24855-2_160 10.1145/2939672.2939778 10.5694/mja15.00812 10.1002/clc.22938 10.1016/j.annemergmed.2021.04.009 10.1145/2783258.2783311 10.1109/ICST.2013.51 10.1002/stvr.294 10.1145/3377811.3380331 10.1161/CIR.0000000000000381 10.3917/hori.005.0017 10.1161/CIR.0000000000000351 10.1145/3368089.3409697 10.1145/230538.230561 10.1145/3324884.3416612 10.1147/JRD.2019.2942287 10.1007/978-3-319-39931-7_9 |
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| Keywords | Software testing Search-based software testing Software fairness Fairness testing Bias Machine learning |
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| References | Zhang J M, Harman M (2021) ’ignorance and prejudice’in software fairness. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, pp 1436–1447 Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. Propublica López-IbánezMDubois-LacosteJCáceresLPBirattariMStützleTThe irace package: Iterated racing for automatic algorithm configurationOper Res Perspect2016343583579175 Bendick M (2007) Situation Testing for Employment Discrimination in the United States of America. [Online; accessed 29-November-2021]. [Online]. Available: https://www.cairn.info/revue-horizons-strategiques-2007-3-page-17.htmhttps://www.cairn.info/revue-horizons-strategiques-2007-3-page-17.htm Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv:1808.00023 Aggarwal A, Lohia P, Nagar S, Dey K, Saha D (2019) Black box fairness testing of machine learning models. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 625–635 Feldman M, Friedler S A, Moeller J, Scheidegger C, Venkatasubramanian S (2015) Certifying and removing disparate impact. In: proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 259–268 HardtMPriceESrebroNEquality of opportunity in supervised learningAdv Neural Inf Process Syst20162933153323 Ingold D, Soper S (2016) Amazon doesn’t consider the race of its customers. should it? [Online; accessed 14-October-2020]. [Online]. Available: https://www.bloomberg.com/graphics/2016-amazon-same-day Kamishima T, Akaho S, Sakuma J (2011) Fairness-aware learning through regularization approach. In: 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, pp 643–650 Ingold D, Soper S (2016) Amazon doesn’t consider the race of its customers. should it? Bloomberg News StricklandEDoc bot preps for the orIEEE Spectr2016536326010.1109/MSPEC.2016.7473150 Olson P (2011) CNN Money, The algorithm that beats your bank manager. http://www.forbes.com/sites/parmyolson/2011/03/15/the-algorithm-that-beats-your-bank-manager/#cd84e4f77ca8, [Accessed 9/11/2020] LakhotiaKHarmanMGrossHAustin: An open source tool for search based software testing of c programsInf Softw Technol201355111212510.1016/j.infsof.2012.03.009 McSweeneyJCRosenfeldAGAbelWMBraunLTBurkeLEDaughertySLFletcherGFGulatiMMehtaLSPetteyCReckelhoffJFPreventing and experiencing ischemic heart disease as a woman: State of the scienceCirculation2016133131302133110.1161/CIR.0000000000000381[Online]. Available: https://doi.org/10.1161/CIR.0000000000000381 BellamyRKEDeyKHindMHoffmanSCHoudeSKannanKLohiaPMartinoJMehtaSMojsilovićAAi fairness 360: An extensible toolkit for detecting and mitigating algorithmic biasIBM J Res Dev2019634/54110.1147/JRD.2019.2942287 Berk R, Heidari H, Jabbari S, Joseph M, Kearns M, Morgenstern J, Neel S, Roth A (2017) A convex framework for fair regression. arXiv:1706.02409 Binns R (2018) Fairness in machine learning: Lessons from political philosophy. In: Conference on Fairness, Accountability and Transparency. PMLR, pp 149–159 McMinnPSearch-based software test data generation: a surveySoftw Test Verif Reliab200414210515610.1002/stvr.294 Grgic-Hlaca N, Zafar M B, Gummadi K P, Weller A (2016) The case for process fairness in learning: Feature selection for fair decision making. In: NIPS Symposium on Machine Learning and the Law, vol 1, p 2 Mao K, Harman M, Jia Y (2016) Sapienz: Multi-objective automated testing for android applications. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp 94–105 Panichella A, Kifetew F M, Tonella P (2015) Reformulating branch coverage as a many-objective optimization problem. In: 2015 IEEE 8th international conference on software testing, verification and validation (ICST). IEEE, pp 1–10 Caton S, Haas C (2020) Fairness in machine learning: A survey Zhang L, Wu Y, Wu X (2016) Situation testing-based discrimination discovery: A causal inference approach DieterichWMendozaCBrennanTCompas risk scales: Demonstrating accuracy equity and predictive parityNorthpoint Inc201677.41 CortellessaVGoseva-PopstojanovaKAppukkuttyKGuedemARHassanAElnaggarRAbdelmoezWAmmarHHModel-based performance risk analysisIEEE Trans Softw Eng200531132010.1109/TSE.2005.12 Sharma A, Wehrheim H (2019) Testing machine learning algorithms for balanced data usage. In: 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST). IEEE, pp 125–135 Ledford H (2019) Millions of black people affected by racial bias in health-care algorithms. [Online; accessed 7-August-2021]. [Online]. Available: https://www.nature.com/articles/d41586-019-03228-6 Di SommaSPaladinoLVaughanLLalleIMagriniLMagnantiMOvercrowding in emergency department: an international issueInternal Emerg Med201510217117510.1007/s11739-014-1154-8[Online]. Available: https://doi.org/10.1007/s11739-014-1154-8 ArcuriABriandLA hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineeringSoftw Test Verif Reliab201424321925010.1002/stvr.1486 Barocas S, Hardt M, Narayanan A (2018) Fairness and machine learning. fairmlbook.org MehtaLSBeckieTMDeVonHAGrinesCLKrumholzHMJohnsonMNLindleyKJVaccarinoVWangTYWatsonKEWengerNKAcute myocardial infarction in womenCirculation2016133991694710.1161/CIR.0000000000000351[Online]. Available: https://doi.org/10.1161/CIR.0000000000000351 Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining, pp 797–806 JuergensCPDabinBFrenchJKKritharidesLHyunKKilianJChewDerekPBBriegerDEnglish as a second language and outcomes of patients presenting with acute coronary syndromes: results from the concordance registryMed J Aust2016204623923910.5694/mja15.00812[Online]. Available: https://doi.org/10.5694/mja15.00812 FriedmanBNissenbaumHBias in computer systemsACM Trans Inf Syst199614333034710.1145/230538.230561 Horkoff J (2019) Non-functional requirements for machine learning: Challenges and new directions. In: 2019 IEEE 27th International Requirements Engineering Conference (RE). IEEE, pp 386–391 Mattioli D (2012) On Orbitz, Mac Users Steered to Pricier Hotels. [Online; accessed 9-January-2020]. [Online]. Available: http://www.wsj.com/articles/SB10001424052702304458604577488822667325882 Galhotra S, Brun Y, Meliou A (2017) Fairness testing: testing software for discrimination. In: Joint Meeting on Foundations of Software Engineering (FSE). ACM, pp 498–510 Chakraborty J, Majumder S, Yu Z, Menzies T (2020) Fairway: A way to build fair ml software. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 654–665 Zhang P, Wang J, Sun J, Dong G, Wang X, Wang X, Dong J S, Dai T (2020) White-box fairness testing through adversarial sampling. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, pp 949–960 RamamoorthyCVHoGSPerformance evaluation of asynchronous concurrent systems using petri netsIEEE Trans Softw Eng1980544044958537410.1109/TSE.1980.230492 Bishop P G, Bloomfield R E (2002) Worst case reliability prediction based on a prior estimate of residual defects. In: 13th International Symposium on Software Reliability Engineering, 2002. Proceedings. IEEE, pp 295–303 Ferral K Wisconsin supreme court allows state to continue using computer program to assist in sentencing. the capital times. [Online; accessed 9-January- 2020]. [Online]. Available: http://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.htmlhttp://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.htmlhttp://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.html Mahdawi A (2018) Is your friend getting a cheaper Uber fare than you are? [Online; accessed 7-August-2021]. [Online]. Available: https://www.theguardian.com/commentisfree/2018/apr/13/uber-lyft-prices-personalized-datahttps://www.theguardian.com/commentisfree/2018/apr/13/uber-lyft-prices-personalized-data Selbst A D, Boyd D, Friedler S A, Venkatasubramanian S, Vertesi J (2019) Fairness and abstraction in sociotechnical systems. In: Proceedings of the conference on fairness, accountability, and transparency, pp 59–68 Hardawar D (2012) Staples, home depot, and other online stores change prices based on your location. [Online; accessed 14-October-2020]. [Online]. Available: https://venturebeat.com/2012/12/24/staples-online-stores-price-changes/https://venturebeat.com/2012/12/24/staples-online-stores-price-changes Johnson B, Bartola J, Angell R, Keith K, Witty S, Giguere S J, Brun Y (2020) Fairkit, fairkit, on the wall, who’s the fairest of them all? supporting data scientists in training fair models. arXiv:2012.09951 Chiappa S (2019) Path-specific counterfactual fairness. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 7801–7808 PanichellaAKifetewFMTonellaPA large scale empirical comparison of state-of-the-art search-based test case generatorsInf Softw Technol201810423625610.1016/j.infsof.2018.08.009 Mullainathan S (2019) Biased algorithms are easier to fix than biased people, www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html, accessed: 10/12/2019. PuschnerPBurnsAGuest editorial: A review of worst-case execution-time analysisReal-Time Syst2000182-311512810.1023/A:1008119029962 Bairey MerzCNAndersenHSpragueEBurnsAKeidaMWalshMNGreenbergerPCampbellSPollinIMcCulloughCBrownNJenkinsMRedbergRJohnsonPRobinsonBKnowledge, attitudes, and beliefs regarding M López-Ibánez (10116_CR46) 2016; 3 S Shah (10116_CR65) 2015; 2 OA Soremekun (10116_CR69) 2011; 41 K Wechkunanukul (10116_CR82) 2016; 220 F Kamiran (10116_CR41) 2012; 33 K Lakhotia (10116_CR44) 2013; 55 V Cortellessa (10116_CR21) 2005; 31 10116_CR27 10116_CR28 10116_CR25 10116_CR26 10116_CR67 BF Klare (10116_CR43) 2012; 7 JC McSweeney (10116_CR52) 2016; 133 10116_CR22 10116_CR66 10116_CR63 10116_CR20 10116_CR64 10116_CR62 W Dieterich (10116_CR24) 2016; 7 J Sun (10116_CR73) 2017; 17 S Di Somma (10116_CR23) 2015; 10 10116_CR8 10116_CR9 CP Juergens (10116_CR40) 2016; 204 M Hardt (10116_CR34) 2016; 29 CV Ramamoorthy (10116_CR61) 1980; 5 LS Mehta (10116_CR54) 2016; 133 RKE Bellamy (10116_CR7) 2019; 63 10116_CR38 10116_CR39 A Vargha (10116_CR78) 2000; 25 10116_CR36 10116_CR37 10116_CR35 10116_CR79 CN Bairey Merz (10116_CR5) 2017; 70 10116_CR32 A Panichella (10116_CR58) 2018; 104 10116_CR33 10116_CR77 10116_CR30 10116_CR74 10116_CR31 10116_CR75 10116_CR72 10116_CR80 JA Udell (10116_CR76) 2018; 41 E Strickland (10116_CR71) 2016; 53 10116_CR49 10116_CR47 10116_CR48 10116_CR45 10116_CR87 10116_CR3 P McMinn (10116_CR51) 2004; 14 10116_CR88 10116_CR6 10116_CR85 10116_CR42 10116_CR86 10116_CR83 10116_CR84 10116_CR2 P Puschner (10116_CR60) 2000; 18 10116_CR81 10116_CR1 A Arcuri (10116_CR4) 2014; 24 RB Siegel (10116_CR68) 2003; 117 10116_CR18 10116_CR19 10116_CR16 10116_CR17 J Stehli (10116_CR70) 2021; 30 10116_CR14 10116_CR15 10116_CR59 10116_CR12 10116_CR56 10116_CR13 10116_CR57 10116_CR10 10116_CR11 10116_CR55 10116_CR53 10116_CR50 B Friedman (10116_CR29) 1996; 14 |
| References_xml | – reference: Zhang J M, Harman M (2021) ’ignorance and prejudice’in software fairness. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, pp 1436–1447 – reference: Biswas S, Rajan H (2020) Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. arXiv:2005.12379 – reference: Feldman M, Friedler S A, Moeller J, Scheidegger C, Venkatasubramanian S (2015) Certifying and removing disparate impact. In: proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 259–268 – reference: Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226 – reference: Walker K, Stephenson M, Loupis A, Ben-Meir M, Joe K, Stephenson M, Lowthian J, Yip B, Wu E, Hansen K et al (2020) Displaying emergency patient estimated wait times: A multi-centre, qualitative study of patient, community, paramedic and health administrator perspectives. Emergency Medicine Australasia – reference: Udeshi S, Arora P, Chattopadhyay S (2018) Automated directed fairness testing. In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp 98–108 – reference: SoremekunOATakayesuJKBohanSJFramework for analyzing wait times and other factors that impact patient satisfaction in the emergency departmentJ Emerg Med201141668669210.1016/j.jemermed.2011.01.018 – reference: Berk R, Heidari H, Jabbari S, Joseph M, Kearns M, Morgenstern J, Neel S, Roth A (2017) A convex framework for fair regression. arXiv:1706.02409 – reference: Perera A, Aleti A, Böhme M, Turhan B (2020) Defect prediction guided search-based software testing. In: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. ACM – reference: SiegelRBEquality talk: Antisubordination and anticlassification values in constitutional struggles over brownHarv L Rev2003117147010.2307/4093259 – reference: Sharma A, Wehrheim H (2019) Testing machine learning algorithms for balanced data usage. In: 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST). IEEE, pp 125–135 – reference: Aggarwal A, Lohia P, Nagar S, Dey K, Saha D (2019) Black box fairness testing of machine learning models. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 625–635 – reference: Hern A (2020) Twitter apologises for ’racist’ image-cropping algorithm. [Online; accessed 7-August-2021]. [Online]. Available: https://www.theguardian.com/technology/2020/sep/21/twitter-apologises-for-racist-image-cropping-algorithmhttps://www.theguardian.com/technology/2020/sep/21/twitter-apologises-for-racist-image-cropping-algorithm – reference: PuschnerPBurnsAGuest editorial: A review of worst-case execution-time analysisReal-Time Syst2000182-311512810.1023/A:1008119029962 – reference: Vogel B, Acevedo M, Appelman Y, Merz C N B, Chieffo A, Figtree G A, Guerrero M, Kunadian V, Lam CSP, Maas AHEM et al (2021) The lancet women and cardiovascular disease commission: reducing the global burden by 2030. The Lancet – reference: MehtaLSBeckieTMDeVonHAGrinesCLKrumholzHMJohnsonMNLindleyKJVaccarinoVWangTYWatsonKEWengerNKAcute myocardial infarction in womenCirculation2016133991694710.1161/CIR.0000000000000351[Online]. Available: https://doi.org/10.1161/CIR.0000000000000351 – reference: Sharkey A (2020) Care robots for the elderly are dangerous. [Online; accessed 14-October-2020]. [Online]. Available: https://www.telegraph.co.uk/science/2016/05/30/care-bots-for-the-elderly-are-dangerous-warns-artificial-intelli – reference: CortellessaVGoseva-PopstojanovaKAppukkuttyKGuedemARHassanAElnaggarRAbdelmoezWAmmarHHModel-based performance risk analysisIEEE Trans Softw Eng200531132010.1109/TSE.2005.12 – reference: Zhang B H, Lemoine B, Mitchell M (2018) Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp 335–340 – reference: DieterichWMendozaCBrennanTCompas risk scales: Demonstrating accuracy equity and predictive parityNorthpoint Inc201677.41 – reference: Tramer F, Atlidakis V, Geambasu R, Hsu D, Hubaux J-P, Humbert M, Juels A, Lin H (2017) Fairtest: Discovering unwarranted associations in data-driven applications. In: 2017 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, pp 401–416 – reference: Zhang L, Wu Y, Wu X (2016) Situation testing-based discrimination discovery: A causal inference approach – reference: Zhang P, Wang J, Sun J, Dong G, Wang X, Wang X, Dong J S, Dai T (2020) White-box fairness testing through adversarial sampling. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, pp 949–960 – reference: PanichellaAKifetewFMTonellaPA large scale empirical comparison of state-of-the-art search-based test case generatorsInf Softw Technol201810423625610.1016/j.infsof.2018.08.009 – reference: FriedmanBNissenbaumHBias in computer systemsACM Trans Inf Syst199614333034710.1145/230538.230561 – reference: Del Grosso C, Antoniol G, Di Penta M, Galinier P, Merlo E (2005) Improving network applications security: a new heuristic to generate stress testing data. In: Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp 1037–1043 – reference: Tatman R (2017) Gender and dialect bias in youtube’s automatic captions. In: Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pp 53–59 – reference: Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining, pp 797–806 – reference: Ledford H (2019) Millions of black people affected by racial bias in health-care algorithms. [Online; accessed 7-August-2021]. [Online]. Available: https://www.nature.com/articles/d41586-019-03228-6 – reference: Mahdawi A (2018) Is your friend getting a cheaper Uber fare than you are? [Online; accessed 7-August-2021]. [Online]. Available: https://www.theguardian.com/commentisfree/2018/apr/13/uber-lyft-prices-personalized-datahttps://www.theguardian.com/commentisfree/2018/apr/13/uber-lyft-prices-personalized-data – reference: Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. Propublica – reference: Ferral K Wisconsin supreme court allows state to continue using computer program to assist in sentencing. the capital times. [Online; accessed 9-January- 2020]. [Online]. Available: http://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.htmlhttp://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.htmlhttp://host.madison.com/ct/news/local/govt-and-politics/wisconsin-supreme-court-allows-state-to-continue-using-computer-program/article7eb67874-bf40-59e3-b62a-923d1626fa0f.html – reference: KamiranFCaldersTData preprocessing techniques for classification without discriminationKnowl Inf Syst201233113310.1007/s10115-011-0463-8 – reference: BellamyRKEDeyKHindMHoffmanSCHoudeSKannanKLohiaPMartinoJMehtaSMojsilovićAAi fairness 360: An extensible toolkit for detecting and mitigating algorithmic biasIBM J Res Dev2019634/54110.1147/JRD.2019.2942287 – reference: Bendick M (2007) Situation Testing for Employment Discrimination in the United States of America. [Online; accessed 29-November-2021]. [Online]. Available: https://www.cairn.info/revue-horizons-strategiques-2007-3-page-17.htmhttps://www.cairn.info/revue-horizons-strategiques-2007-3-page-17.htm – reference: Ingold D, Soper S (2016) Amazon doesn’t consider the race of its customers. should it? Bloomberg News – reference: VarghaADelaneyHDA critique and improvement of the cl common language effect size statistics of mcgraw and wongJ Educ Behav Stat2000252101132 – reference: Barocas S, Hardt M, Narayanan A (2018) Fairness and machine learning. fairmlbook.org – reference: Walker K, Jiarpakdee J, Loupis A, Tantithamthavorn C, Joe K, Ben-Meir M, Akhlaghi H, Hutton J, Wang W, Stephenson M, Blecher G, Buntine P, Sweeny A, Turhan B (2021) On behalf of the Australasian College for Emergency Medicine, Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study. medRxiv, [Online]. Available: https://www.medrxiv.org/content/early/2021/03/24/2021.03.19.21253921 – reference: Di SommaSPaladinoLVaughanLLalleIMagriniLMagnantiMOvercrowding in emergency department: an international issueInternal Emerg Med201510217117510.1007/s11739-014-1154-8[Online]. Available: https://doi.org/10.1007/s11739-014-1154-8 – reference: StehliJDuffySJBurgessSKuhnLGulatiMChowCZamanSSex disparities in myocardial infarction: biology or bias?Heart Lung Circul2021301182610.1016/j.hlc.2020.06.025 – reference: Runeson P, Engström E, Storey M-A (2020) The design science paradigm as a frame for empirical software engineering. In: Contemporary empirical methods in software engineering. Springer, pp 127–147 – reference: Panichella A, Kifetew F M, Tonella P (2015) Reformulating branch coverage as a many-objective optimization problem. In: 2015 IEEE 8th international conference on software testing, verification and validation (ICST). IEEE, pp 1–10 – reference: Mao K, Harman M, Jia Y (2016) Sapienz: Multi-objective automated testing for android applications. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp 94–105 – reference: Chakraborty J, Majumder S, Menzies T (2021) Bias in machine learning software: Why? how? what to do? arXiv:2105.12195 – reference: Selbst A D, Boyd D, Friedler S A, Venkatasubramanian S, Vertesi J (2019) Fairness and abstraction in sociotechnical systems. In: Proceedings of the conference on fairness, accountability, and transparency, pp 59–68 – reference: Galhotra S, Brun Y, Meliou A (2017) Fairness testing: testing software for discrimination. In: Joint Meeting on Foundations of Software Engineering (FSE). ACM, pp 498–510 – reference: McMinnPSearch-based software test data generation: a surveySoftw Test Verif Reliab200414210515610.1002/stvr.294 – reference: JuergensCPDabinBFrenchJKKritharidesLHyunKKilianJChewDerekPBBriegerDEnglish as a second language and outcomes of patients presenting with acute coronary syndromes: results from the concordance registryMed J Aust2016204623923910.5694/mja15.00812[Online]. Available: https://doi.org/10.5694/mja15.00812 – reference: Wegener J, Bühler O (2004) Evaluation of different fitness functions for the evolutionary testing of an autonomous parking system. In: Genetic and Evolutionary Computation Conference. Springer, pp 1400–1412 – reference: Ghaffary S (2019) The algorithms that detect hate speech online are biased against black people. [Online; accessed 14-October-2020]. [Online]. Available: https://www.vox.com/recode/2019/8/15/20806384/social-media-hate-speech-bias-black-african-american-facebook-twitterhttps://www.vox.com/recode/2019/8/15/20806384/social-media-hate-speech-bias-black-african-american-facebook-twitter – reference: Ribeiro M T, Singh S, Guestrin C (2016) ”why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135–1144 – reference: Chiappa S (2019) Path-specific counterfactual fairness. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 7801–7808 – reference: Hardawar D (2012) Staples, home depot, and other online stores change prices based on your location. [Online; accessed 14-October-2020]. [Online]. Available: https://venturebeat.com/2012/12/24/staples-online-stores-price-changes/https://venturebeat.com/2012/12/24/staples-online-stores-price-changes/ – reference: Calmon F, Wei D, Vinzamuri B, Ramamurthy K N, Varshney K R (2017) Optimized pre-processing for discrimination prevention. In: Advances in Neural Information Processing Systems, pp 3992–4001 – reference: Alshahwan N, Gao X, Harman M, Jia Y, Mao K, Mols A, Tei T, Zorin I (2018) Deploying search based software engineering with sapienz at facebook. In: International Symposium on Search Based Software Engineering. Springer, pp 3–45 – reference: Luong B T, Ruggieri S, Turini F (2011) k-nn as an implementation of situation testing for discrimination discovery and prevention. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 502–510 – reference: Zhang J M, Harman M, Ma L, Liu Y (2020) Machine learning testing: Survey, landscapes and horizons. IEEE Trans Softw Eng – reference: Mattioli D (2012) On Orbitz, Mac Users Steered to Pricier Hotels. [Online; accessed 9-January-2020]. [Online]. Available: http://www.wsj.com/articles/SB10001424052702304458604577488822667325882 – reference: Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2019) A survey on bias and fairness in machine learning. arXiv:1908.09635 – reference: Chakraborty J, Majumder S, Yu Z, Menzies T (2020) Fairway: A way to build fair ml software. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 654–665 – reference: Chouldechova A, Benavides-Prado D, Fialko O, Vaithianathan R (2018) A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In: Conference on Fairness, Accountability and Transparency. PMLR, pp 134–148 – reference: Ingold D, Soper S (2016) Amazon doesn’t consider the race of its customers. should it? [Online; accessed 14-October-2020]. [Online]. Available: https://www.bloomberg.com/graphics/2016-amazon-same-day/ – reference: RamamoorthyCVHoGSPerformance evaluation of asynchronous concurrent systems using petri netsIEEE Trans Softw Eng1980544044958537410.1109/TSE.1980.230492 – reference: WechkunanukulKGranthamHTeubnerDHyunKKClarkRAPresenting characteristics and processing times for culturally and linguistically diverse (cald) patients with chest pain in an emergency department: Time, ethnicity, and delay (ted) study iiInt J Cardiol201622090190810.1016/j.ijcard.2016.06.244 – reference: Strobel S, Ren K Y, Dragoman A, Pettit C, Stancati A, Kallergis D, Smith M, Sidhu K, Rutledge G, Mondoux S (2021) Do patients respond to posted emergency department wait times: Time-series evidence from the implementation of a wait time publication system in hamilton, canada. Ann Emerg Med – reference: Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv:1808.00023 – reference: Fraser G, Arcuri A (2013) Evosuite: On the challenges of test case generation in the real world. In: 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation. IEEE, pp 362–369 – reference: SunJLinQZhaoPZhangQXuKChenHHuCJStuntzMLiHLiuYReducing waiting time and raising outpatient satisfaction in a chinese public tertiary general hospital-an interrupted time series studyBMC Public Health201717111110.1186/s12889-016-3954-4 – reference: StricklandEDoc bot preps for the orIEEE Spectr2016536326010.1109/MSPEC.2016.7473150 – reference: KlareBFBurgeMJKlontzJCBrueggeRWVJainAKFace recognition performance: Role of demographic informationIEEE Trans Inf Forensic Secur2012761789180110.1109/TIFS.2012.2214212 – reference: McSweeneyJCRosenfeldAGAbelWMBraunLTBurkeLEDaughertySLFletcherGFGulatiMMehtaLSPetteyCReckelhoffJFPreventing and experiencing ischemic heart disease as a woman: State of the scienceCirculation2016133131302133110.1161/CIR.0000000000000381[Online]. Available: https://doi.org/10.1161/CIR.0000000000000381 – reference: López-IbánezMDubois-LacosteJCáceresLPBirattariMStützleTThe irace package: Iterated racing for automatic algorithm configurationOper Res Perspect2016343583579175 – reference: UdellJAFonarowGCMaddoxTMCannonCPFrank PeacockWLaskeyWKGrau-SepulvedaMVSmithEEHernandezAFPetersonEDSustained sex-based treatment differences in acute coronary syndrome care: insights from the american heart association get with the guidelines coronary artery disease registryClin Cardiol201841675876810.1002/clc.22938 – reference: Bishop P G, Bloomfield R E (2002) Worst case reliability prediction based on a prior estimate of residual defects. In: 13th International Symposium on Software Reliability Engineering, 2002. Proceedings. IEEE, pp 295–303 – reference: Bairey MerzCNAndersenHSpragueEBurnsAKeidaMWalshMNGreenbergerPCampbellSPollinIMcCulloughCBrownNJenkinsMRedbergRJohnsonPRobinsonBKnowledge, attitudes, and beliefs regarding cardiovascular disease in women: The women’s heart allianceJ Am Coll Cardiol201770212313210.1016/j.jacc.2017.05.024[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0735109717374077 – reference: LakhotiaKHarmanMGrossHAustin: An open source tool for search based software testing of c programsInf Softw Technol201355111212510.1016/j.infsof.2012.03.009 – reference: Kamishima T, Akaho S, Sakuma J (2011) Fairness-aware learning through regularization approach. In: 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, pp 643–650 – reference: Mullainathan S (2019) Biased algorithms are easier to fix than biased people, www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html, accessed: 10/12/2019. – reference: Olson P (2011) CNN Money, The algorithm that beats your bank manager. http://www.forbes.com/sites/parmyolson/2011/03/15/the-algorithm-that-beats-your-bank-manager/#cd84e4f77ca8, [Accessed 9/11/2020] – reference: ArcuriABriandLA hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineeringSoftw Test Verif Reliab201424321925010.1002/stvr.1486 – reference: Caton S, Haas C (2020) Fairness in machine learning: A survey – reference: HardtMPriceESrebroNEquality of opportunity in supervised learningAdv Neural Inf Process Syst20162933153323 – reference: Horkoff J (2019) Non-functional requirements for machine learning: Challenges and new directions. In: 2019 IEEE 27th International Requirements Engineering Conference (RE). IEEE, pp 386–391 – reference: Johnson B, Bartola J, Angell R, Keith K, Witty S, Giguere S J, Brun Y (2020) Fairkit, fairkit, on the wall, who’s the fairest of them all? supporting data scientists in training fair models. arXiv:2012.09951 – reference: ShahSPatelARumoroDPHohmannSFullamFManaging patient expectations at emergency department triagePatient Exper J201522314410.35680/2372-0247.1090 – reference: Binns R (2018) Fairness in machine learning: Lessons from political philosophy. In: Conference on Fairness, Accountability and Transparency. PMLR, pp 149–159 – reference: Grgic-Hlaca N, Zafar M B, Gummadi K P, Weller A (2016) The case for process fairness in learning: Feature selection for fair decision making. In: NIPS Symposium on Machine Learning and the Law, vol 1, p 2 – ident: 10116_CR36 doi: 10.1109/RE.2019.00050 – ident: 10116_CR48 – volume: 30 start-page: 18 issue: 1 year: 2021 ident: 10116_CR70 publication-title: Heart Lung Circul doi: 10.1016/j.hlc.2020.06.025 – ident: 10116_CR63 doi: 10.1007/978-3-030-32489-6_5 – ident: 10116_CR86 – volume: 2 start-page: 31 issue: 2 year: 2015 ident: 10116_CR65 publication-title: Patient Exper J doi: 10.35680/2372-0247.1090 – volume: 33 start-page: 1 issue: 1 year: 2012 ident: 10116_CR41 publication-title: Knowl Inf Syst doi: 10.1007/s10115-011-0463-8 – volume: 24 start-page: 219 issue: 3 year: 2014 ident: 10116_CR4 publication-title: Softw Test Verif Reliab doi: 10.1002/stvr.1486 – volume: 41 start-page: 686 issue: 6 year: 2011 ident: 10116_CR69 publication-title: J Emerg Med doi: 10.1016/j.jemermed.2011.01.018 – ident: 10116_CR77 doi: 10.1145/3238147.3238165 – ident: 10116_CR3 – ident: 10116_CR11 – volume: 10 start-page: 171 issue: 2 year: 2015 ident: 10116_CR23 publication-title: Internal Emerg Med doi: 10.1007/s11739-014-1154-8 – ident: 10116_CR67 doi: 10.1109/ICST.2019.00022 – ident: 10116_CR38 – volume: 3 start-page: 43 year: 2016 ident: 10116_CR46 publication-title: Oper Res Perspect – ident: 10116_CR19 – ident: 10116_CR22 doi: 10.1145/1068009.1068185 – ident: 10116_CR53 – volume: 53 start-page: 32 issue: 6 year: 2016 ident: 10116_CR71 publication-title: IEEE Spectr doi: 10.1109/MSPEC.2016.7473150 – volume: 31 start-page: 3 issue: 1 year: 2005 ident: 10116_CR21 publication-title: IEEE Trans Softw Eng doi: 10.1109/TSE.2005.12 – volume: 117 start-page: 1470 year: 2003 ident: 10116_CR68 publication-title: Harv L Rev doi: 10.2307/4093259 – ident: 10116_CR49 doi: 10.1145/2931037.2931054 – ident: 10116_CR64 doi: 10.1145/3287560.3287598 – volume: 25 start-page: 101 issue: 2 year: 2000 ident: 10116_CR78 publication-title: J Educ Behav Stat – ident: 10116_CR66 – ident: 10116_CR2 doi: 10.1007/978-3-319-99241-9_1 – ident: 10116_CR20 doi: 10.1145/3097983.3098095 – ident: 10116_CR12 doi: 10.1145/3368089.3409704 – volume: 18 start-page: 115 issue: 2-3 year: 2000 ident: 10116_CR60 publication-title: Real-Time Syst doi: 10.1023/A:1008119029962 – volume: 17 start-page: 1 issue: 1 year: 2017 ident: 10116_CR73 publication-title: BMC Public Health doi: 10.1186/s12889-016-3954-4 – ident: 10116_CR84 doi: 10.1145/3278721.3278779 – volume: 55 start-page: 112 issue: 1 year: 2013 ident: 10116_CR44 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2012.03.009 – ident: 10116_CR45 doi: 10.1038/d41586-019-03228-6 – ident: 10116_CR31 – ident: 10116_CR56 – ident: 10116_CR15 doi: 10.1145/3468264.3468537 – ident: 10116_CR47 doi: 10.1145/2020408.2020488 – ident: 10116_CR10 – ident: 10116_CR39 – volume: 7 start-page: 1789 issue: 6 year: 2012 ident: 10116_CR43 publication-title: IEEE Trans Inf Forensic Secur doi: 10.1109/TIFS.2012.2214212 – ident: 10116_CR85 doi: 10.1109/ICSE43902.2021.00129 – ident: 10116_CR14 – ident: 10116_CR18 – ident: 10116_CR35 – ident: 10116_CR9 – ident: 10116_CR81 doi: 10.1111/1742-6723.13640 – ident: 10116_CR1 doi: 10.1145/3338906.3338937 – ident: 10116_CR30 doi: 10.1145/3106237.3106277 – ident: 10116_CR79 doi: 10.1016/S0140-6736(21)00684-X – ident: 10116_CR42 doi: 10.1109/ICDMW.2011.83 – volume: 7 start-page: 1 issue: 7.4 year: 2016 ident: 10116_CR24 publication-title: Northpoint Inc – volume: 5 start-page: 440 year: 1980 ident: 10116_CR61 publication-title: IEEE Trans Softw Eng doi: 10.1109/TSE.1980.230492 – ident: 10116_CR17 doi: 10.1609/aaai.v33i01.33017801 – ident: 10116_CR27 – ident: 10116_CR57 doi: 10.1109/ICST.2015.7102604 – volume: 220 start-page: 901 year: 2016 ident: 10116_CR82 publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2016.06.244 – ident: 10116_CR80 doi: 10.1101/2021.03.19.21253921 – volume: 70 start-page: 123 issue: 2 year: 2017 ident: 10116_CR5 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2017.05.024 – ident: 10116_CR55 – ident: 10116_CR74 doi: 10.18653/v1/W17-1606 – ident: 10116_CR75 doi: 10.1109/EuroSP.2017.29 – volume: 104 start-page: 236 year: 2018 ident: 10116_CR58 publication-title: Inf Softw Technol doi: 10.1016/j.infsof.2018.08.009 – ident: 10116_CR25 doi: 10.1145/2090236.2090255 – ident: 10116_CR83 doi: 10.1007/978-3-540-24855-2_160 – ident: 10116_CR13 – ident: 10116_CR62 doi: 10.1145/2939672.2939778 – volume: 204 start-page: 239 issue: 6 year: 2016 ident: 10116_CR40 publication-title: Med J Aust doi: 10.5694/mja15.00812 – ident: 10116_CR32 – volume: 41 start-page: 758 issue: 6 year: 2018 ident: 10116_CR76 publication-title: Clin Cardiol doi: 10.1002/clc.22938 – ident: 10116_CR72 doi: 10.1016/j.annemergmed.2021.04.009 – ident: 10116_CR26 doi: 10.1145/2783258.2783311 – ident: 10116_CR28 doi: 10.1109/ICST.2013.51 – volume: 14 start-page: 105 issue: 2 year: 2004 ident: 10116_CR51 publication-title: Softw Test Verif Reliab doi: 10.1002/stvr.294 – ident: 10116_CR88 doi: 10.1145/3377811.3380331 – volume: 133 start-page: 1302 issue: 13 year: 2016 ident: 10116_CR52 publication-title: Circulation doi: 10.1161/CIR.0000000000000381 – ident: 10116_CR8 doi: 10.3917/hori.005.0017 – volume: 133 start-page: 916 issue: 9 year: 2016 ident: 10116_CR54 publication-title: Circulation doi: 10.1161/CIR.0000000000000351 – ident: 10116_CR16 doi: 10.1145/3368089.3409697 – volume: 14 start-page: 330 issue: 3 year: 1996 ident: 10116_CR29 publication-title: ACM Trans Inf Syst doi: 10.1145/230538.230561 – ident: 10116_CR59 doi: 10.1145/3324884.3416612 – volume: 63 start-page: 4 issue: 4/5 year: 2019 ident: 10116_CR7 publication-title: IBM J Res Dev doi: 10.1147/JRD.2019.2942287 – volume: 29 start-page: 3315 year: 2016 ident: 10116_CR34 publication-title: Adv Neural Inf Process Syst – ident: 10116_CR87 doi: 10.1007/978-3-319-39931-7_9 – ident: 10116_CR6 – ident: 10116_CR33 – ident: 10116_CR37 – ident: 10116_CR50 |
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