[1] Sommerville, I. (2015). Software engineering. 10th. Book Software Engineering. 10th, Series Software Engineering.
[2] Zhang, Y., Xu, F., Zou, J., Petrosian, O. L., & Krinkin, K. V. (2021, June). XAI evaluation: evaluating black-box model explanations for prediction. In 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) (pp. 13-16). IEEE.
[3] Binkhonain, M., & Zhao, L. (2019). A review of machine learning algorithms for identification and classification of non-functional requirements. Expert Systems with Applications: X, 1, 100001.
[4] Khayashi, F., Jamasb, B., Akbari, R., & Shamsinejadbabaki, P. (2022). Deep Learning Methods for Software Requirement Classification: A Performance Study on the PURE dataset. arXiv preprint arXiv:2211.05286.
[5] Dias Canedo, E., & Cordeiro Mendes, B. (2020). Software requirements classification using machine learning algorithms. Entropy, 22(9), 1057.
[6] Rahimi, N., Eassa, F., & Elrefaei, L. (2021). One-and two-phase software requirement classification using ensemble deep learning. Entropy, 23(10), 1264.
[7] Quba, G. Y., Al Qaisi, H., Althunibat, A., & AlZu’bi, S. (2021, July). Software requirements classification using machine learning algorithm’s. In 2021 International Conference on Information Technology (ICIT) (pp. 685-690). IEEE.
[8] Jesus, S., Belém, C., Balayan, V., Bento, J., Saleiro, P., Bizarro, P., & Gama, J. (2021, March). How can I choose an explainer? An application-grounded evaluation of post-hoc explanations. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 805-815).
[9] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[10] Lundberg, S. M., & Lee, S. I. (2017, December). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4768-4777).
[11] Habiba, U. E., Bogner, J., & Wagner, S. (2022, August). Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements. In 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW) (pp. 162-165). IEEE.
[12] Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023). XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process. Machine Learning and Knowledge Extraction, 5(1), 78-108.
[13] Jiarpakdee, J., Tantithamthavorn, C. K., & Grundy, J. (2021, May). Practitioners’ perceptions of the goals and visual explanations of defect prediction models. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) (pp. 432-443). IEEE.
[14] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-115.
[15] Jesus, S., Belém, C., Balayan, V., Bento, J., Saleiro, P., Bizarro, P., & Gama, J. (2021, March). How can I choose an explainer? An application-grounded evaluation of post-hoc explanations. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 805-815).
[16] Kim, D., Seo, D., Cho, S., & Kang, P. (2019). Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Information Sciences, 477, 15-29.
[17] Jabri, S., Dahbi, A., Gadi, T., & Bassir, A. (2018, April). Ranking of text documents using TF-IDF weighting and association rules mining. In 2018 4th international conference on optimization and applications (ICOA) (pp. 1-6). IEEE.
[18] Ramadhani, D. A., Rochimah, S., & Yuhana, U. L. (2015). Classification of non-functional requirements using semantic-FSKNN based ISO/IEC 9126. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(4), 1456-1465.
[19] Diwali, A., Saeedi, K., Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2023). Sentiment analysis meets explainable artificial intelligence: A survey on explainable sentiment analysis. IEEE Transactions on Affective Computing.
[20] Mathews, S. M. (2019). Explainable artificial intelligence applications in NLP, biomedical, and malware classification: a literature review. In Intelligent Computing: Proceedings of the 2019 Computing Conference, Volume 2 (pp. 1269-1292). Springer International Publishing.
[21] Zahoor, K., & Bawany, N. Z. (2024). Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning. Interactive Learning Environments, 32(9), 5227-5252.
[22] Zhang, H., & Ogasawara, K. (2023). Grad-CAM-based explainable artificial intelligence related to medical text processing. Bioengineering, 10(9), 1070.
[23] Pospelova, N., Tarasova, A., Subbotina, N., Koroleva, N., Raimova, N., & Lydia, E. L. (2024). Explainable Artificial Intelligence and Natural Language Processing for Unraveling Deceptive Contents. Fusion: Practice & Applications, 14(2).
[24] Chazette, L., Brunotte, W., & Speith, T. (2022). Explainable software systems: from requirements analysis to system evaluation. Requirements Engineering, 27(4), 457-487.
[25] Magableh, A. A. (2023, August). Towards leveraging explainable artificial intelligent (XAI) in requirements engineering (RE) to identify aspect (crosscutting concern): a systematic literature review (SLR) and bibliometric analysis. In 2023 International Conference on Information Technology (ICIT) (pp. 319-326). IEEE.
[26] Bai, S., Shi, S., Han, C., Yang, M., Gupta, B. B., & Arya, V. (2024). Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps. Future Generation Computer Systems, 158, 167-182.
