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Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development.
S. Keele et al. , “Guidelines for performing systematic literature reviews in software engineering,” Technical report, Ver. 2.3 EBSE Technical Report. EBSE, Tech. Rep., 2007
2007
Earlier work this paper cites.
M. Götz, M. Book, C. Bodenstein, and M. Riedel, “Supporting software engineering practices in the development of data-intensive hpc applications with the juml framework,” in Proceedings of the 1st International Workshop on Software Engineering for High Performance Computing in Computational and Data-Enabled Science and Engineering , ser. SE-CoDeSE’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 1–8. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3144763.3144765
2017
Earlier work this paper cites.
N. Nascimento, P. Alencar, C. Lucena, and D. Cowan, “Toward human-in-the-loop collaboration between software engineers and machine learning algorithms,” in 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 2018, pp. 3534–3540
2018
Earlier work this paper cites.
K. Singla, J. Bose, and C. Naik, “Analysis of software engineering for agile machine learning projects,” in 2018 15th IEEE India Council International Conference (INDICON) . IEEE, 2018, pp. 1–5
2018
Earlier work this paper cites.
F. Khomh, B. Adams, J. Cheng, M. Fokaefs, and G. Antoniol, “Software engineering for machine-learning applications: The road ahead,” IEEE Software , vol. 35, no. 5, pp. 81–84, Sep. 2018
2018
Earlier work this paper cites.
J. Henriksson, M. Borg, and C. Englund, “Automotive safety and machine learning: Initial results from a study on how to adapt the iso 26262 safety standard,” in Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems , ser. SEFAIS ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 47–49. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3194085.3194090
2018
Earlier work this paper cites.
S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi, and T. Zimmermann, “Software engineering for machine learning: A case study,” in 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) , 2019, pp. 291–300
2019
Earlier work this paper cites.
T. Zhang, C. Gao, L. Ma, M. Lyu, and M. Kim, “An empirical study of common challenges in developing deep learning applications,” in 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2019, pp. 104–115
2019
Earlier work this paper cites.
M. Hesenius, N. Schwenzfeier, O. Meyer, W. Koop, and V. Gruhn, “Towards a software engineering process for developing data-driven applications,” in 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) . IEEE, 2019, pp. 35–41
2019
Earlier work this paper cites.
H. Washizaki, H. Uchida, F. Khomh, and Y.-G. Guéhéneuc, “Studying software engineering patterns for designing machine learning systems,” in 2019 10th International Workshop on Empirical Software Engineering in Practice (IWESEP) . IEEE, 2019, pp. 49–495
2019
Earlier work this paper cites.
E. de Souza Nascimento, I. Ahmed, E. Oliveira, M. P. Palheta, I. Steinmacher, and T. Conte, “Understanding development process of machine learning systems: Challenges and solutions,” in 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) . IEEE, 2019, pp. 1–6
2019
Earlier work this paper cites.
M. Ole and G. Volker, “Towards concept based software engineering for intelligent agents,” in 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) , May 2019, pp. 42–48
2019
Earlier work this paper cites.
Z. Wan, X. Xia, D. Lo, and G. C. Murphy, “How does machine learning change software development practices?” IEEE Transactions on Software Engineering , pp. 1–1, 2019
2019
Cited alongside, same era.
H. Foidl and M. Felderer, “Risk-based data validation in machine learning-based software systems,” in Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation , ser. MaLTeSQuE 2019. New York, NY, USA: Association for Computing Machinery, 2019, p. 13–18. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3340482.3342743
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. J. Simmons, S. Barnett, J. Rivera-Villicana, A. Bajaj, and R. Vasa, “A large-scale comparative analysis of coding standard conformance in open-source data science projects,” in Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) , ser. ESEM ’20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3382494.3410680
2020
Later among the works it cites.
M. Kim, “Software engineering for data analytics,” IEEE Software , vol. 37, no. 4, pp. 36–42, July 2020
2020
Later among the works it cites.
A. Banimustafa and N. Hardy, “A scientific knowledge discovery and data mining process model for metabolomics,” IEEE Access , vol. 8, pp. 209 964–210 005, 2020
2020
Later among the works it cites.
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2019
Cited alongside, same era.
F. Ishikawa and N. Yoshioka, “How do engineers perceive difficulties in engineering of machine-learning systems? questionnaire survey,” in Proceedings of the Joint 7th International Workshop on Conducting Empirical Studies in Industry and 6th International Workshop on Software Engineering Research and Industrial Practice , ser. CESSER-IP ’19. IEEE Press, 2019, p. 2–9. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1109/CESSER-IP.2019.00009
2019
Cited alongside, same era.
I. Gerostathopoulos, S. Kugele, C. Segler, T. Bures, and A. Knoll, “Automated trainability evaluation for smart software functions,” in Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’19. IEEE Press, 2019, p. 998–1001. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1109/ASE.2019.00096
2019
Cited alongside, same era.
J. L. Correia, J. A. Pereira, R. de Mello, A. Garcia, B. Fonseca, M. Ribeiro, R. Gheyi, W. Tiengo, M. Kalinowski, and R. Cerqueira, “Brazilian data scientists: Revealing their challenges and practices on machine learning model development,” in Proceedings of the 19th Brazilian Symposium on Software Quality (SBQS) , 2020, pp. 1–10
2020
Cited alongside, same era.
A. Serban, K. van der Blom, H. Hoos, and J. Visser, “Adoption and effects of software engineering best practices in machine learning,” in Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) , 2020, pp. 1–12
2020
Cited alongside, same era.
L. Reimann and G. Kniesel-Wünsche, “Achieving guidance in applied machine learning through software engineering techniques,” in Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming , 2020, pp. 7–12
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. T. Wolf and D. Paine, “Sensemaking practices in the everyday work of ai/ml software engineering,” in Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops , ser. ICSEW’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 86–92. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3387940.3391496
2020
Cited alongside, same era.
J. Tsay, A. Braz, M. Hirzel, A. Shinnar, and T. Mummert, “Aimmx: Artificial intelligence model metadata extractor,” in Proceedings of the 17th International Conference on Mining Software Repositories , ser. MSR ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 81–92. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3379597.3387448
2020
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Shafiq, A. Mashkoor, C. Mayr-Dorn, and A. Egyed, “Machine learning for software engineering: A systematic mapping,” 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Scheerer, J. Klamroth, R. Reussner, and B. Beckert, “Towards classes of architectural dependability assurance for machine-learning-based systems,” in Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems , ser. SEAMS ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 31–37. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3387939.3388613
2020
Later among the works it cites.
A. Cummaudo, R. Vasa, S. Barnett, J. Grundy, and M. Abdelrazek, “Interpreting cloud computer vision pain-points: A mining study of stack overflow,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , ser. ICSE ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1584–1596. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3377811.3380404
2020
Later among the works it cites.
S. Gerasimou, H. F. Eniser, A. Sen, and A. Cakan, “Importance-driven deep learning system testing,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , ser. ICSE ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 702–713. [Online]. Available: https://doi-org.proxy.lib.uwaterloo.ca/10.1145/3377811.3380391
2020
Later among the works it cites.
R. Akkiraju, V. Sinha, A. Xu, J. Mahmud, P. Gundecha, Z. Liu, X. Liu, and J. Schumacher, “Characterizing machine learning processes: A maturity framework,” in International Conference on Business Process Management . Springer, 2020, pp. 17–31
2020
Later among the works it cites.