{
  "id": "2206.14669",
  "version": "v1",
  "published": "2022-06-29T14:14:54.000Z",
  "updated": "2022-06-29T14:14:54.000Z",
  "title": "Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews",
  "authors": [
    "Jialiang Wei",
    "Anne-Lise Courbis",
    "Thomas Lambolais",
    "Binbin Xu",
    "Pierre Louis Bernard",
    "Gérard Dray"
  ],
  "comment": "7th National Conference on Practical Applications of Artificial Intelligence, 2022. Saint-\\'Etienne, France. article in English and French, 4 pages each",
  "categories": [
    "cs.CL",
    "cs.SE"
  ],
  "abstract": "We are concerned by Data Driven Requirements Engineering, and in particular the consideration of user's reviews. These online reviews are a rich source of information for extracting new needs and improvement requests. In this work, we provide an automated analysis using CamemBERT, which is a state-of-the-art language model in French. We created a multi-label classification dataset of 6000 user reviews from three applications in the Health & Fitness field. The results are encouraging and suggest that it's possible to identify automatically the reviews concerning requests for new features. Dataset is available at: https://github.com/Jl-wei/APIA2022-French-user-reviews-classification-dataset.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-06-29T14:14:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "data-driven requirements engineering approach",
      "user reviews",
      "automatic analysis",
      "data driven requirements",
      "state-of-the-art language model"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}