Becha, Rahma, Sellami, Asma, Bouassida, Nadia, Idri, Ali et Abran, Alain.
2024.
« BotCFP: A machine learning based tool for COSMIC chatbots sizing ».
In IWSM-MENSURA 2024 : Joint Proceedings IWSM and Mensura 2024 (Montréal, QC, Canada, Sept. 30-Oct. 04, 2024)
Coll. « CEUR Workshop Proceedings », vol. 3852.
CEUR-WS.
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Résumé
Chatbots are becoming more popular due to the number of functionalities they provide, the time savings and rapid responses in real time. Developing a chatbot requires defining a list of functional requirements upfront. Some of these requirements can be derived from other chatbots to discover and provide the required functionality, while the acquisition of other requirements is time-consuming and costly. Applying a standardized functional size measurement method, such as COSMIC Function Points – ISO 19761, to chatbots requirements is helpful in estimating the related project development effort and duration. This paper proposes an automated tool named BotCFP for generating the chatbots’ sizes using the use-cases.csv dataset. Three Machine Learning techniques (Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM)) were used to determine the chatbots’ sizes based on their functional processes (FP) name using three different text vectorization methods: TF-IDF, Word2Vec, and Bag of words. The best measurement results were provided by Random Forest using TF-IDF text vectorization method, deployed and used as an API in the BotCFP tool. The proposed tool allows users (project managers and developers) to determine the chatbot size from its FPs names before starting the development process.
| Type de document: | Compte rendu de conférence |
|---|---|
| Professeur: | Professeur Abran, Alain |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 09 janv. 2025 19:54 |
| Dernière modification: | 29 janv. 2025 19:48 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30418 |
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