A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment
<|reference_start|>A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment: The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too often superficial as studies abstract from the phenomena under investigation too much. Two of these abstractions are caused by the lack of frameworks for causal inference and frequentist methods which reduce complex data to binary results. In this study, we aim to demonstrate (1) the use of a causal framework and (2) contrast frequentist methods with more sophisticated Bayesian statistics for causal inference. To this end, we reanalyze the only known controlled experiment investigating the impact of passive voice on the subsequent activity of domain modeling. We follow a framework for statistical causal inference and employ Bayesian data analysis methods to re-investigate the hypotheses of the original study. Our results reveal that the effects observed by the original authors turned out to be much less significant than previously assumed. This study supports the recent call to action in SE research to adopt Bayesian data analysis, including causal frameworks and Bayesian statistics, for more sophisticated causal inference.<|reference_end|>
@article{frattini2024a,
    title={A Second Look at the Impact of Passive Voice Requirements on Domain
  Modeling: Bayesian Reanalysis of an Experiment},
    author={Julian Frattini, Davide Fucci, Richard Torkar, Daniel Mendez},
    journal={arXiv preprint arXiv:2402.10800},
    year={2024},
    doi={10.1145/3643664.3648211},
    archivePrefix={arXiv},
    eprint={2402.10800},
    primaryClass={cs.SE}
}