Rebot: An Automatic Multi-modal Requirements Review Bot
Ming Ye, Jicheng Cao, Shengyu Cheng · 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · 2022
Requirements review is the process that reviewers read documents, make suggestions, and help improve the quality of requirements, which is a major factor that contributes to the success or failure of software. However, manually reviewing is a time-consuming and challenging task that requires high domain knowledge and expertise. To address the problem, we developed a requirements review tool, called Rebot, which automates the requirements parsing, quality classification, and suggestions generation. The core of Rebot is a neural network-based quality model which fuses multi-modal information (visual and textual information) of requirements documents to classify their quality levels (high, medium, low). The model is trained and evaluated on a real industrial requirements documents dataset which is collected from ZTE corporation. The experiments show the model achieves 81.3% accuracy in classifying the quality into three levels. To further validate Rebot, we deployed it in a live software development project. We evaluated the correctness, usefulness, and feasibility of Rebot by conducting a questionnaire with the users. Around 76.5% of Rebot's users believe Rebot can support requirements review by providing reliable quality classification results with revision suggestions. Furthermore, Around 88% of the users believe Rebot helps reduce the workload of reviewers and increase the development efficiency.