Requirements Classification for Smart Allocation: A Case Study in the Railway Industry
Sarmad Bashir, Muhammad Abbas, Alessio Ferrari , Mehrdad Saadatmand , Pernilla Lindberg
In 31st IEEE International Requirements Engineering Conference
Abstract:
Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.
Full Text:
Bibtex:
@INPROCEEDINGS{10260910,
author={Bashir, Sarmad and Abbas, Muhammad and Ferrari, Alessio and Saadatmand, Mehrdad and Lindberg, Pernilla},
booktitle={2023 IEEE 31st International Requirements Engineering Conference (RE)},
title={Requirements Classification for Smart Allocation: A Case Study in the Railway Industry},
year={2023},
volume={},
number={},
pages={201-211},
doi={10.1109/RE57278.2023.00028}}