APA (7th ed.) Citation

Siavvas, M., Tsoukalas, D., Jankovic, M., Kehagias, D., & Tzovaras, D. (2022). Technical debt as an indicator of software security risk: A machine learning approach for software development enterprises. Enterprise Information Systems, 16(5), 1. https://doi.org/10.1080/17517575.2020.1824017

Chicago Style (17th ed.) Citation

Siavvas, Miltiadis, Dimitrios Tsoukalas, Marija Jankovic, Dionysios Kehagias, and Dimitrios Tzovaras. "Technical Debt as an Indicator of Software Security Risk: A Machine Learning Approach for Software Development Enterprises." Enterprise Information Systems 16, no. 5 (2022): 1. https://doi.org/10.1080/17517575.2020.1824017.

MLA (9th ed.) Citation

Siavvas, Miltiadis, et al. "Technical Debt as an Indicator of Software Security Risk: A Machine Learning Approach for Software Development Enterprises." Enterprise Information Systems, vol. 16, no. 5, 2022, p. 1, https://doi.org/10.1080/17517575.2020.1824017.