Abstract
This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.
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Notes
- 1.
The dataset can be found at https://github.com/marcoortu/jira-social-repository, accessed 2021-12-17.
- 2.
ChatterBot can be found at http://chatterbot.readthedocs.io, accessed 2021-12-17.
References
Adamopoulou, E., Moussiades, L.: An overview of chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 584, pp. 373–383. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_31
Ahmed, M.: Knowledge base enhanced & user-centric dialogue design for OTF Computing. Master’s thesis, Paderborn University, Germany (2022)
Bäumer, F.S.: Indikatorbasierte Erkennung und Kompensation von ungenauen und unvollständig beschriebenen Softwareanforderungen [Indicator-based detection and compensation of inaccurate and incompletely described software requirements]. Ph.D. thesis, Paderborn University, Germany (2017)
Bäumer, F.S., Kersting, J., Geierhos, M.: Natural language processing in OTF Computing: challenges and the need for interactive approaches. Computers 8(1), 1–14 (2019). https://doi.org/10.3390/computers8010022
Bocklisch, T., Faulkner, J., Pawlowski, N., Nichol, A.: Rasa: open source language understanding and dialogue management. CoRR, pp. 2–9 (2017)
Bunk, T., Varshneya, D., Vlasov, V., Nichol, A.: DIET: lightweight language understanding for dialogue systems. CoRR (2020)
Casas, J., Tricot, M.O., Abou Khaled, O., Mugellini, E., Cudré-Mauroux, P.: Trends & methods in chatbot evaluation. In: Companion Publication of the ICMI 2020, pp. 280–286. ACM (2020). https://doi.org/10.1145/3395035.3425319
Collaborative Research Center 901: CRC 901 - On-The-Fly Computing - Subproject B1. https://sfb901.uni-paderborn.de/projects/project-area-b/subproject-b1
Cox, G.: ChatterBot (2021). https://chatterbot.readthedocs.io/en/stable/. Accessed 18 Oct 2021
Dollmann, M., Geierhos, M.: On- and off-topic classification and semantic annotation of user-generated software requirements. In: Su, J., Carreras, X., Duh, K. (eds.) Proceedings of EMNLP 2016, pp. 1807–1816. ACL (2016). https://doi.org/10.18653/v1/d16-1186
Friesen, E.: Requirements Engineering im OTF-Computing: Informationsextraktion und Unvollständigkeitskompensation mittels domänenspezifischer Wissensbasis [Requirements Engineering in OTF-Computing: Information Extraction and Incompleteness Compensation by means of a Domain-specific Knowledge Base]. Master’s thesis, Paderborn University, Germany (2019)
Friesen, E., Bäumer, F.S., Geierhos, M.: CORDULA: software requirements extraction utilizing chatbot as communication interface. In: Schmid, K., et al. (eds.) Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality. CEUR Workshop Proceedings, vol. 2075. CEUR-WS.org (2018)
Karl, H., Kundisch, D., Meyer auf der Heide, F., Wehrheim, H.: A case for a new IT ecosystem: On-The-Fly Computing. Bus. Inf. Syst. Eng. 62(6), 467–481 (2019). https://doi.org/10.1007/s12599-019-00627-x
Kersting, J., Bäumer, F.S.: Semantic tagging of requirement descriptions: a transformer-based approach. In: Proceedings of the 17th AC, pp. 119–123. IADIS (2020)
Ortu, M., Destefanis, G., Adams, B., Murgia, A., Marchesi, M., Tonelli, R.: The JIRA repository dataset: understanding social aspects of software development. In: Bener, A., Minku, L.L., Turhan, B. (eds.) Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015, pp. 1–4. ACM (2015). https://doi.org/10.1145/2810146.2810147
Shawar, B.A., Atwell, E.: ALICE chatbot: trials and outputs. Computación y Sistemas 19(4), 625–632 (2015). https://doi.org/10.13053/CyS-19-4-2326
Surana, C.S.R.K., Shriya Gupta, D.B., Shankar, S.P.: Intelligent chatbot for requirements elicitation and classification. In: 2019 4th International Conference on Recent Trends on Electronics, Information, Communication Technology (RTEICT), pp. 866–870. IEEE (2019). https://doi.org/10.1109/RTEICT46194.2019.9016907
Surendran, A., Murali, R., Babu, R.K.R.: Conversational AI - a retrieval based chatbot. EasyChair Preprint no. 4020 (2020)
Thorat, S., Jadhav, V.: A review on implementation issues of rule-based chatbot systems. In: Proceedings of the International Conference on Innovative Computing & Communications (ICICC 2020), pp. 1–6. Springer (2020)
Varghese, E., Pillai, M.T.R.: A standalone generative conversational interface using deep learning. In: 2018 2nd ICICCT, pp. 1915–1920. IEEE (2018). https://doi.org/10.1109/ICICCT.2018.8473211
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st NIPS, pp. 5998–6008. Curran (2017)
Zhang, Y., et al.: DialoGPT: large-scale generative pre-training for conversational response generation. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the ACL, pp. 270–278. ACL (2020)
Acknowledgements
This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center On-The-Fly Computing (CRC 901). We thank F. S. Bäumer, E. Friesen, and others for their support.
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Kersting, J., Ahmed, M., Geierhos, M. (2022). Chatbot-Enhanced Requirements Resolution for Automated Service Compositions. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_56
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