Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma
<|reference_start|>Designing NLP-based solutions for requirements variability management: experiences from a design science study at Visma: Context and motivation: In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/problem: Following the design science paradigm, we studied the problem of requirements analysis and tracing in the context of contractual documents, with a specific focus on managing requirements variability. This paper reports on the lessons learned from that experience, highlighting the strategies and insights gained in the realm of requirements variability management. Principal ideas/results: This experience report outlines the insights gained from applying design science in requirements engineering research in industry. We show and evaluate various strategies to tackle the issue of requirement variability. Contribution: We report on the iterations and how the solution development evolved in parallel with problem understanding. From this process, we derive five key lessons learned to highlight the effectiveness of design science in exploring solutions for requirement variability in contract-based environments.<|reference_end|>
@article{elahidoost2024designing,
    title={Designing NLP-based solutions for requirements variability management:
  experiences from a design science study at Visma},
    author={Parisa Elahidoost, Michael Unterkalmsteiner, Davide Fucci, Peter
  Liljenberg, Jannik Fischbach},
    journal={arXiv preprint arXiv:2402.07145},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07145},
    primaryClass={cs.SE}
}