Quality issues in machine learning software systems

Author:

Côté Pierre-OlivierORCID,Nikanjam Amin,Bouchoucha Rached,Basta Ilan,Abidi Mouna,Khomh Foutse

Funder

NSERC

Prompt

Publisher

Springer Science and Business Media LLC

Reference73 articles.

1. (2022) Tesla behind eight-vehicle crash was in full self-driving mode, says driver. URL https://www.theguardian.com/technology/2022/dec/22/tesla-crash-full-self-driving-mode-san-francisco

2. Alahdab M, Çalıklı G (2019) Empirical analysis of hidden technical debt patterns in machine learning software. In: International Conference on Product-Focused Software Process Improvement, Springer, pp 195–202

3. Amershi S, Begel A, Bird C, DeLine R, Gall H, Kamar E, Nagappan N, Nushi B, Zimmermann T (2019) Software engineering for machine learning: A case study. In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), IEEE, pp 291–300

4. Angwin J, Larson J, Kirchner L, Mattu S (2016) Machine bias. URL https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

5. Avgeriou P, Kruchten P, Ozkaya I, Seaman C (2016) Managing technical debt in software engineering (dagstuhl seminar 16162). In: Dagstuhl reports, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, vol 6

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