{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:33:31Z","timestamp":1775266411404,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001777","name":"The University of Wollongong","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001777","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35, 59.36, 31.78, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0\u20135 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.<\/jats:p>","DOI":"10.1007\/s10515-022-00364-8","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T10:04:06Z","timestamp":1664791446000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A framework for conditional statement technical debt identification and description"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6162-6064","authenticated-orcid":false,"given":"Abdulaziz","family":"Alhefdhi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoa Khanh","family":"Dam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuf Sulistyo","family":"Nugroho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideaki","family":"Hata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Ishio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Ghose","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,3]]},"reference":[{"key":"364_CR1","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Man\u00e9, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Vi\u00e9gas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https:\/\/www.tensorflow.org\/"},{"key":"364_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-3223-4","volume-title":"Mining Text Data","author":"CC Aggarwal","year":"2012","unstructured":"Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, Berlin (2012)"},{"issue":"4","key":"364_CR3","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1007\/s10664-017-9540-2","volume":"23","author":"M Aniche","year":"2018","unstructured":"Aniche, M., Bavota, G., Treude, C., Gerosa, M.A., Deursen, A.: Code smells for model-view-controller architectures. Emp. Softw. Eng. 23(4), 2121\u20132157 (2018)","journal-title":"Emp. Softw. Eng."},{"key":"364_CR4","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"364_CR5","doi-asserted-by":"crossref","unstructured":"Bavota, G., Russo, B.: A large-scale empirical study on self-admitted technical debt. In: 2016 IEEE\/ACM 13th Working Conference On Mining Software Repositories (MSR), pp. 315\u2013326 (2016). IEEE","DOI":"10.1145\/2901739.2901742"},{"key":"364_CR6","doi-asserted-by":"crossref","unstructured":"Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245\u2013250 (2001)","DOI":"10.1145\/502512.502546"},{"issue":"1","key":"364_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"364_CR8","doi-asserted-by":"crossref","unstructured":"Brown, N., Cai, Y., Guo, Y., Kazman, R., Kim, M., Kruchten, P., Lim, E., MacCormack, A., Nord, R., Ozkaya, I., et al.: Managing technical debt in software-reliant systems. In: Proceedings of the FSE\/SDP Workshop on Future of Software Engineering Research, pp. 47\u201352 (2010). ACM","DOI":"10.1145\/1882362.1882373"},{"key":"364_CR9","doi-asserted-by":"crossref","unstructured":"Cho, K., Van\u00a0Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"364_CR10","doi-asserted-by":"crossref","unstructured":"Choetkiertikul, M., Dam, H.K., Tran, T., Pham, T.T.M., Ghose, A., Menzies, T.: A deep learning model for estimating story points. IEEE Trans. Softw. Eng. (2018)","DOI":"10.1109\/TSE.2018.2792473"},{"key":"364_CR11","unstructured":"Chollet, F.: A Ten-minute Introduction to Sequence-to-sequence Learning in Keras. https:\/\/blog.keras.io\/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html Accessed 2022-01-06"},{"issue":"2","key":"364_CR12","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1145\/157710.157715","volume":"4","author":"W Cunningham","year":"1993","unstructured":"Cunningham, W.: The wycash portfolio management system. ACM SIGPLAN OOPS Messenger 4(2), 29\u201330 (1993)","journal-title":"ACM SIGPLAN OOPS Messenger"},{"issue":"11","key":"364_CR13","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1109\/TSE.2017.2654244","volume":"43","author":"E da Silva Maldonado","year":"2017","unstructured":"da Silva Maldonado, E., Shihab, E., Tsantalis, N.: Using natural language processing to automatically detect self-admitted technical debt. IEEE Trans. Softw. Eng. 43(11), 1044\u20131062 (2017)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"364_CR14","doi-asserted-by":"crossref","unstructured":"de Freitas Farias, M.A., de Mendon\u00e7a\u00a0Neto, M.G., da Silva, A.B., Sp\u00ednola, R.O.: A contextualized vocabulary model for identifying technical debt on code comments. In: 2015 IEEE 7th International Workshop On Managing Technical Debt (MTD), pp. 25\u201332 (2015). IEEE","DOI":"10.1109\/MTD.2015.7332621"},{"key":"364_CR15","doi-asserted-by":"crossref","unstructured":"de Freitas Farias, M.A., Santos, J.A.M., da Silva, A.B., Kalinowski, M., Mendon\u00e7a, M.G., Sp\u00ednola, R.O.: Investigating the use of a contextualized vocabulary in the identification of technical debt: a controlled experiment. In: ICEIS (1), pp. 369\u2013378 (2016)","DOI":"10.5220\/0005914503690378"},{"key":"364_CR16","doi-asserted-by":"crossref","unstructured":"Freitag, M., Al-Onaizan, Y.: Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806 (2017)","DOI":"10.18653\/v1\/W17-3207"},{"key":"364_CR17","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019\u20131027 (2016)"},{"key":"364_CR18","doi-asserted-by":"crossref","unstructured":"Gousios, G.: The ghtorent dataset and tool suite. In: Proceedings of the Working Conference on Mining Software Repositories, pp. 233\u2013236 (2013)","DOI":"10.1109\/MSR.2013.6624034"},{"key":"364_CR19","doi-asserted-by":"crossref","unstructured":"Hata, H., Treude, C., Kula, R.G., Ishio, T.: 9.6 million links in source code comments:pPurpose, evolution, and decay. In: Proceedings of 41st ACM\/IEEE International Conference on Software Engineering (ICSE 2019) (to appear)","DOI":"10.1109\/ICSE.2019.00123"},{"issue":"8","key":"364_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"364_CR21","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, G., Xia, X., Lo, D., Jin, Z.: Deep code comment generation. In: Proceedings of the 26th Conference on Program Comprehension, pp. 200\u2013210 (2018). ACM","DOI":"10.1145\/3196321.3196334"},{"issue":"1","key":"364_CR22","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/s10664-017-9522-4","volume":"23","author":"Q Huang","year":"2018","unstructured":"Huang, Q., Shihab, E., Xia, X., Lo, D., Li, S.: Identifying self-admitted technical debt in open source projects using text mining. Emp. Softw. Eng. 23(1), 418\u2013451 (2018)","journal-title":"Emp. Softw. Eng."},{"key":"364_CR23","doi-asserted-by":"crossref","unstructured":"Joachims, T.: A statistical learning learning model of text classification for support vector machines. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 128\u2013136 (2001)","DOI":"10.1145\/383952.383974"},{"key":"364_CR24","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning, pp. 137\u2013142 (1998). Springer","DOI":"10.1007\/BFb0026683"},{"issue":"4","key":"364_CR25","first-page":"15","volume":"6","author":"S Kaufman","year":"2012","unstructured":"Kaufman, S., Rosset, S., Perlich, C., Stitelman, O.: Leakage in data mining: formulation, detection, and avoidance. ACM Trans. Knowl. Dis. Data (TKDD) 6(4), 15 (2012)","journal-title":"ACM Trans. Knowl. Dis. Data (TKDD)"},{"key":"364_CR26","doi-asserted-by":"crossref","unstructured":"Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial naive bayes for text categorization revisited. In: Australasian Joint Conference on Artificial Intelligence, pp. 488\u2013499 (2004). Springer","DOI":"10.1007\/978-3-540-30549-1_43"},{"key":"364_CR27","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"364_CR28","unstructured":"Kruchten, P., Nord, R., Ozkaya, I.: Managing Technical Debt: Reducing Friction in Software Development. Addison-Wesley Professional (2019)"},{"issue":"6","key":"364_CR29","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MS.2012.130","volume":"29","author":"E Lim","year":"2012","unstructured":"Lim, E., Taksande, N., Seaman, C.: A balancing act: What software practitioners have to say about technical debt. IEEE Softw. 29(6), 22\u201327 (2012)","journal-title":"IEEE Softw."},{"key":"364_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Xia, X., Hassan, A.E., Lo, D., Xing, Z., Wang, X.: Neural-machine-translation-based commit message generation: how far are we? In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 373\u2013384 (2018)","DOI":"10.1145\/3238147.3238190"},{"key":"364_CR31","doi-asserted-by":"crossref","unstructured":"Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"364_CR32","doi-asserted-by":"publisher","unstructured":"Maipradit, R., Lin, B., Nagy, C., Bavota, G., Lanza, M., Hata, H., Matsumoto, K.: Automated identification of on-hold self-admitted technical debt. In: 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 54\u201364 (2020). https:\/\/doi.org\/10.1109\/SCAM51674.2020.00011","DOI":"10.1109\/SCAM51674.2020.00011"},{"issue":"5","key":"364_CR33","doi-asserted-by":"publisher","first-page":"3770","DOI":"10.1007\/s10664-020-09854-3","volume":"25","author":"R Maipradit","year":"2020","unstructured":"Maipradit, R., Treude, C., Hata, H., Matsumoto, K.: Wait for it: identifying on-hold self-admitted technical debt. Emp. Softw. Eng. 25(5), 3770\u20133798 (2020). https:\/\/doi.org\/10.1007\/s10664-020-09854-3","journal-title":"Emp. Softw. Eng."},{"key":"364_CR34","doi-asserted-by":"crossref","unstructured":"Maldonado, E.d.S., Abdalkareem, R., Shihab, E., Serebrenik, A.: An empirical study on the removal of self-admitted technical debt. In: 2017 IEEE International Conference On Software Maintenance and Evolution (ICSME), pp. 238\u2013248 (2017). IEEE","DOI":"10.1109\/ICSME.2017.8"},{"key":"364_CR35","doi-asserted-by":"crossref","unstructured":"Maldonado, E.d.S., Shihab, E.: Detecting and quantifying different types of self-admitted technical debt. In: 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), pp. 9\u201315 (2015). IEEE","DOI":"10.1109\/MTD.2015.7332619"},{"key":"364_CR36","doi-asserted-by":"crossref","unstructured":"Marcilio, D., Bonif\u00e1cio, R., Monteiro, E., Canedo, E., Luz, W., Pinto, G.: Are static analysis violations really fixed? a closer look at realistic usage of sonarqube. In: 2019 IEEE\/ACM 27th International Conference on Program Comprehension (ICPC), pp. 209\u2013219 (2019). IEEE","DOI":"10.1109\/ICPC.2019.00040"},{"issue":"1","key":"364_CR37","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/s10664-013-9282-8","volume":"20","author":"M Martinez","year":"2015","unstructured":"Martinez, M., Monperrus, M.: Mining software repair models for reasoning on the search space of automated program fixing. Emp. Softw. Eng. 20(1), 176\u2013205 (2015). https:\/\/doi.org\/10.1007\/s10664-013-9282-8","journal-title":"Emp. Softw. Eng."},{"issue":"1","key":"364_CR38","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/272991.272995","volume":"8","author":"M Matsumoto","year":"1998","unstructured":"Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. (TOMACS) 8(1), 3\u201330 (1998)","journal-title":"ACM Trans. Model. Comput. Simul. (TOMACS)"},{"key":"364_CR39","unstructured":"McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41\u201348 (1998). Citeseer"},{"issue":"6","key":"364_CR40","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.1007\/s10664-017-9512-6","volume":"22","author":"N Munaiah","year":"2017","unstructured":"Munaiah, N., Kroh, S., Cabrey, C., Nagappan, M.: Curating github for engineered software projects. Emp. Softw. Eng. 22(6), 3219\u20133253 (2017)","journal-title":"Emp. Softw. Eng."},{"key":"364_CR41","doi-asserted-by":"crossref","unstructured":"Oda, Y., Fudaba, H., Neubig, G., Hata, H., Sakti, S., Toda, T., Nakamura, S.: Learning to generate pseudo-code from source code using statistical machine translation (t). In: Automated Software Engineering (ASE), 2015 30th IEEE\/ACM International Conference On, pp. 574\u2013584 (2015). IEEE","DOI":"10.1109\/ASE.2015.36"},{"key":"364_CR42","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311\u2013318 (2002). Association for Computational Linguistics","DOI":"10.3115\/1073083.1073135"},{"key":"364_CR43","doi-asserted-by":"crossref","unstructured":"Potdar, A., Shihab, E.: An exploratory study on self-admitted technical debt. In: 2014 IEEE International Conference On Software Maintenance and Evolution (ICSME), pp. 91\u2013100 (2014). IEEE","DOI":"10.1109\/ICSME.2014.31"},{"issue":"3","key":"364_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3324916","volume":"28","author":"X Ren","year":"2019","unstructured":"Ren, X., Xing, Z., Xia, X., Lo, D., Wang, X., Grundy, J.: Neural network-based detection of self-admitted technical debt: from performance to explainability. ACM Trans. Softw. Eng. Methodol. (TOSEM) 28(3), 1\u201345 (2019)","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"},{"key":"364_CR45","unstructured":"Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 616\u2013623 (2003)"},{"key":"364_CR46","unstructured":"Russell, S., Norvig, P.: Artificial Intelligence: a Modern Approach, pp. 125\u2013126. Pearson (2002)"},{"issue":"11","key":"364_CR47","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1145\/361219.361220","volume":"18","author":"G Salton","year":"1975","unstructured":"Salton, G., Wong, A., Yang, C.-S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613\u2013620 (1975)","journal-title":"Commun. ACM"},{"key":"364_CR48","unstructured":"scikit-learn: Cross-validation: Evaluating Estimator Performance. https:\/\/scikit-learn.org\/stable\/modules\/cross_validation.html#stratified-k-fold Accessed 2022-01-06"},{"issue":"1","key":"364_CR49","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"364_CR50","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104\u20133112 (2014)"},{"issue":"6","key":"364_CR51","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1016\/j.jss.2012.12.052","volume":"86","author":"E Tom","year":"2013","unstructured":"Tom, E., Aurum, A., Vidgen, R.: An exploration of technical debt. J. Syst. Softw. 86(6), 1498\u20131516 (2013)","journal-title":"J. Syst. Softw."},{"issue":"5","key":"364_CR52","first-page":"360","volume":"37","author":"AJ Viera","year":"2005","unstructured":"Viera, A.J., Garrett, J.M., et al.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360\u2013363 (2005)","journal-title":"Fam. Med."},{"key":"364_CR53","doi-asserted-by":"crossref","unstructured":"Wan, Y., Zhao, Z., Yang, M., Xu, G., Ying, H., Wu, J., Yu, P.S.: Improving automatic source code summarization via deep reinforcement learning. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 397\u2013407 (2018)","DOI":"10.1145\/3238147.3238206"},{"key":"364_CR54","doi-asserted-by":"publisher","unstructured":"Wattanakriengkrai, S., Maipradit, R., Hata, H., Choetkiertikul, M., Sunetnanta, T., Matsumoto, K.: Identifying design and requirement self-admitted technical debt using n-gram idf. In: 2018 9th International Workshop on Empirical Software Engineering in Practice (IWESEP), pp. 7\u201312 (2018). https:\/\/doi.org\/10.1109\/IWESEP.2018.00010","DOI":"10.1109\/IWESEP.2018.00010"},{"key":"364_CR55","doi-asserted-by":"publisher","unstructured":"Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., Matsumoto, K.: Predicting defective lines using a model-agnostic technique. IEEE Trans. Softw. Eng. (01), 1\u20131 (5555). https:\/\/doi.org\/10.1109\/TSE.2020.3023177","DOI":"10.1109\/TSE.2020.3023177"},{"key":"364_CR56","doi-asserted-by":"crossref","unstructured":"Wehaibi, S., Shihab, E., Guerrouj, L.: Examining the impact of self-admitted technical debt on software quality. In: 2016 IEEE 23rd International Conference On Software Analysis, Evolution, and Reengineering (SANER), vol. 1, pp. 179\u2013188 (2016). IEEE","DOI":"10.1109\/SANER.2016.72"},{"issue":"12","key":"364_CR57","first-page":"2913","volume":"7","author":"B Xu","year":"2012","unstructured":"Xu, B., Guo, X., Ye, Y., Cheng, J.: An improved random forest classifier for text categorization. JCP 7(12), 2913\u20132920 (2012)","journal-title":"JCP"},{"issue":"1","key":"364_CR58","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TSE.2016.2560811","volume":"43","author":"J Xuan","year":"2017","unstructured":"Xuan, J., Martinez, M., DeMarco, F., Clement, M., Marcote, S.L., Durieux, T., Le Berre, D., Monperrus, M.: Nopol: Automatic repair of conditional statement bugs in java programs. IEEE Trans. Softw. Eng. 43(1), 34\u201355 (2017). https:\/\/doi.org\/10.1109\/TSE.2016.2560811","journal-title":"IEEE Trans. Softw. Eng."},{"key":"364_CR59","doi-asserted-by":"crossref","unstructured":"Yan, M., Xia, X., Shihab, E., Lo, D., Yin, J., Yang, X.: Automating change-level self-admitted technical debt determination. IEEE Trans. Softw. Eng. (2018)","DOI":"10.1109\/TSE.2018.2831232"},{"key":"364_CR60","unstructured":"Yoav, G., Graeme, H.: Neural network methods in natural language processing. Morgan & Claypool: San Rafael, SR, USA, 227 (2017)"},{"key":"364_CR61","doi-asserted-by":"crossref","unstructured":"Zampetti, F., Noiseux, C., Antoniol, G., Khomh, F., Di\u00a0Penta, M.: Recommending when design technical debt should be self-admitted. In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 216\u2013226 (2017). IEEE","DOI":"10.1109\/ICSME.2017.44"},{"key":"364_CR62","doi-asserted-by":"crossref","unstructured":"Zampetti, F., Serebrenik, A., Di\u00a0Penta, M.: Automatically learning patterns for self-admitted technical debt removal. In: 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 355\u2013366 (2020). IEEE","DOI":"10.1109\/SANER48275.2020.9054868"},{"key":"364_CR63","doi-asserted-by":"crossref","unstructured":"Zampetti, F., Serebrenik, A., Di\u00a0Penta, M.: Was self-admitted technical debt removal a real removal? an in-depth perspective. In: 2018 IEEE\/ACM 15th International Conference on Mining Software Repositories (MSR), pp. 526\u2013536 (2018). IEEE","DOI":"10.1145\/3196398.3196423"},{"key":"364_CR64","doi-asserted-by":"crossref","unstructured":"Zazworka, N., Shaw, M.A., Shull, F., Seaman, C.: Investigating the impact of design debt on software quality. In: Proceedings of the 2nd Workshop on Managing Technical Debt, pp. 17\u201323 (2011). ACM","DOI":"10.1145\/1985362.1985366"}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00364-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-022-00364-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00364-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T11:30:11Z","timestamp":1666956611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-022-00364-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,3]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["364"],"URL":"https:\/\/doi.org\/10.1007\/s10515-022-00364-8","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,3]]},"assertion":[{"value":"9 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"60"}}