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A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment

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Title: A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment
Authors: Frattini, Julian, 1995, Fucci, Davide, Torkar, Richard, 1971, Mendez, Daniel
Source: International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE), Lisbon, Portugal PROCEEDINGS OF THE 2024 IEEE/ACM INTERNATIONAL WORKSHOP ON METHODOLOGICAL ISSUES WITH EMPIRICAL STUDIES IN SOFTWARE ENGINEERING, WSESE 2024. :27-33
Subject Terms: Requirements Engineering, Bayesian Data Analysis, Controlled experiment, Requirements Quality
Description: 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. 1Wo 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.
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Access URL: https://research.chalmers.se/publication/542995
https://dl.acm.org/doi/10.1145/3643664.3648211
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  Data: A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment
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  Data: <searchLink fieldCode="AR" term="%22Frattini%2C+Julian%22">Frattini, Julian</searchLink>, 1995<br /><searchLink fieldCode="AR" term="%22Fucci%2C+Davide%22">Fucci, Davide</searchLink><br /><searchLink fieldCode="AR" term="%22Torkar%2C+Richard%22">Torkar, Richard</searchLink>, 1971<br /><searchLink fieldCode="AR" term="%22Mendez%2C+Daniel%22">Mendez, Daniel</searchLink>
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  Data: <i>International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE), Lisbon, Portugal PROCEEDINGS OF THE 2024 IEEE/ACM INTERNATIONAL WORKSHOP ON METHODOLOGICAL ISSUES WITH EMPIRICAL STUDIES IN SOFTWARE ENGINEERING, WSESE 2024</i>. :27-33
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  Data: <searchLink fieldCode="DE" term="%22Requirements+Engineering%22">Requirements Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Data+Analysis%22">Bayesian Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Controlled+experiment%22">Controlled experiment</searchLink><br /><searchLink fieldCode="DE" term="%22Requirements+Quality%22">Requirements Quality</searchLink>
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  Data: 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. 1Wo 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.
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