This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
D. Tsoukalas, “Machine learning for technical debt identification,” IEEE Transactions on Software Engineering, p. 1, Jan. 2021, doi: 10.1109/tse.2021.3129355.TsoukalasD.“Machine learning for technical debt identification,”IEEE Transactions on Software Engineering1Jan.202110.1109/tse.2021.3129355Open DOISearch in Google Scholar
Y. Li, M. Soliman, and P. Avgeriou, “Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers,” 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 495–503, Aug. 2020, doi: 10.1109/seaa51224.2020.00083.LiY.SolimanM.AvgeriouP.“Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers,”46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)495503Aug.202010.1109/seaa51224.2020.00083Open DOISearch in Google Scholar
Z. Liu, Q. Huang, X. Xia, E. Shihab, D. Lo, and S. Li, “SATD detector,” 2018 IEEE/ACM 40th International Conference on Software Engineering, May 2018, doi: 10.1145/3183440.3183478.LiuZ.HuangQ.XiaX.ShihabE.LoD.LiS.“SATD detector,”2018 IEEE/ACM 40th International Conference on Software EngineeringMay201810.1145/3183440.3183478Open DOISearch in Google Scholar
J. Tan, D. Feitosa and P. Avgeriou, “The life-cycle of Technical Debt that manifests in both source code and issue trackers”, Information and Software Technology, Volume 159, 2023, 107216, ISSN 0950-5849, doi: 10.1016/j.infsof.2023.107216.TanJ.FeitosaD.AvgeriouP.“The life-cycle of Technical Debt that manifests in both source code and issue trackers”Information and Software Technology1592023107216, ISSN 0950-5849,10.1016/j.infsof.2023.107216Open DOISearch in Google Scholar
L. Xavier, F. Ferreira, R. Brito and M. Valente, “Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems,” in 2020 IEEE/ACM 17th International Conference on Mining Software Repositories (MSR), Seoul, Korea, Republic of, 2020 pp. 137–146. doi: 10.1145/3379597.3387459XavierL.FerreiraF.BritoR.ValenteM.“Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems,”in2020 IEEE/ACM 17th International Conference on Mining Software Repositories (MSR)Seoul, Korea, Republic of202013714610.1145/3379597.3387459Open DOISearch in Google Scholar
W. S. Tan, M. Wagner, and C. Treude, “Detecting outdated code element references in software repository documentation,” arXiv (Cornell University), Jan. 2022, doi: 10.48550/arxiv.2212.01479.TanW. S.WagnerM.TreudeC.“Detecting outdated code element references in software repository documentation,”arXiv (Cornell University),Jan.202210.48550/arxiv.2212.01479Open DOISearch in Google Scholar
Y. Li, M. Soliman, and P. Avgeriou, “Automatic identification of self-admitted technical debt from four different sources,” Empirical Software Engineering, vol. 28, no. 3, Apr. 2023, doi: 10.1007/s10664-023-10297-9.LiY.SolimanM.AvgeriouP.“Automatic identification of self-admitted technical debt from four different sources,”Empirical Software Engineering283Apr.202310.1007/s10664-023-10297-9Open DOISearch in Google Scholar
F. Zampetti, A. Serebrenik and M. Di Penta, “Automatically Learning Patterns for Self-Admitted Technical Debt Removal,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), London, ON, Canada, 2020 pp. 355–366. doi: 10.1109/SANER48275.2020.9054868ZampettiF.SerebrenikA.Di PentaM.“Automatically Learning Patterns for Self-Admitted Technical Debt Removal,”in2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)London, ON, Canada202035536610.1109/SANER48275.2020.9054868Open DOISearch in Google Scholar
P. Bagane, C. Sengar, S. Dongre, S. Prabhakar, S. Baldua, and S. Gurav, ‘Total Electron Content Forecasting in Low Latitude Regions of India: Machine and Deep Learning Synergy’, Communications in Computer and Information Science, vol. 2054 CCIS, pp. 104–119, 2024. doi: 10.1007/978-3-031-56703-2_9BaganeP.SengarC.DongreS.PrabhakarS.BalduaS.GuravS.‘Total Electron Content Forecasting in Low Latitude Regions of India: Machine and Deep Learning Synergy’Communications in Computer and Information Sciencevol. 2054 CCIS,104119202410.1007/978-3-031-56703-2_9Open DOISearch in Google Scholar
P. Bagane, M. Thawani, P. Singh, R. Ahmad, R. Mital, and O. A. Jebessa, ‘Breaking the Silence: An innovative ASL to Text Conversion System Leveraging Computer Vision & Machine Learning for Enhanced Communication’, International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 14s, pp. 246–255, 2024.BaganeP.ThawaniM.SinghP.AhmadR.MitalR.JebessaO. A.‘Breaking the Silence: An innovative ASL to Text Conversion System Leveraging Computer Vision & Machine Learning for Enhanced Communication’International Journal of Intelligent Systems and Applications in Engineering1214s2462552024Search in Google Scholar
E. Gama, S. Freire, M. Mendonça, R. O. Spínola, M. Paixao, and M. I. Cortés, ‘Using Stack Overflow to Assess Technical Debt Identification on Software Projects’. In Proceedings of the XXXIV Brazilian Symposium on Software Engineering (SBES ‘20). Association for Computing Machinery, New York, NY, USA, 2020, pp. 730–739. doi: 10.1145/3422392.3422429GamaE.FreireS.MendonçaM.SpínolaR. O.PaixaoM.CortésM. I.‘Using Stack Overflow to Assess Technical Debt Identification on Software Projects’InProceedings of the XXXIV Brazilian Symposium on Software Engineering (SBES ‘20)Association for Computing Machinery, New York, NY, USA202073073910.1145/3422392.3422429Open DOISearch in Google Scholar
F. Bi, B. Vogel-Heuser, Z. Huang, F. Ocker ‘Characteristics, causes, and consequences of technical debt in the automation domain’, Journal of Systems and Software, vol. 204, 2023. doi: 10.1016/j.jss.2023.111725BiF.Vogel-HeuserB.HuangZ.OckerF.‘Characteristics, causes, and consequences of technical debt in the automation domain’Journal of Systems and Software204202310.1016/j.jss.2023.111725Open DOISearch in Google Scholar
C. Jaspan and C. Green, “Defining, Measuring, and Managing Technical Debt,” in IEEE Software, vol. 40, no. 3, pp. 15–19, May–June 2023, doi: 10.1109/MS.2023.3242137.JaspanC.GreenC.“Defining, Measuring, and Managing Technical Debt,”inIEEE Software4031519May–June202310.1109/MS.2023.3242137Open DOISearch in Google Scholar
D. Pina, A. Goldman and G. Tonin, “Technical Debt Prioritization: Taxonomy, Methods Results, and Practical Characteristics,” 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Palermo, Italy, 2021, pp. 206–213, doi: 10.1109/SEAA53835.2021.00034.PinaD.GoldmanA.ToninG.“Technical Debt Prioritization: Taxonomy, Methods Results, and Practical Characteristics,”2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)Palermo, Italy202120621310.1109/SEAA53835.2021.00034Open DOISearch in Google Scholar
J. S. De Jesus and A. C. V. De Melo, “Technical Debt and the Software Project Characteristics. A Repository-Based Exploratory Analysis,” 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece, pp. 444–453, Jul. 2017, doi: 10.1109/cbi.2017.62.De JesusJ. S.De MeloA. C. V.“Technical Debt and the Software Project Characteristics. A Repository-Based Exploratory Analysis,”2017 IEEE 19th Conference on Business Informatics (CBI)Thessaloniki, Greece444453Jul.201710.1109/cbi.2017.62Open DOISearch in Google Scholar