Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Replicated Experiment
CoRR(2024)
摘要
Context: It is commonly accepted that the quality of requirements
specifications impacts subsequent software engineering activities. However, we
still lack empirical evidence to support organizations in deciding whether
their requirements are good enough or impede subsequent activities. Objective:
We aim to contribute empirical evidence to the effect that requirements quality
defects have on a software engineering activity that depends on this
requirement. Method: We replicate a controlled experiment in which 25
participants from industry and university generate domain models from four
natural language requirements containing different quality defects. We evaluate
the resulting models using both frequentist and Bayesian data analysis.
Results: Contrary to our expectations, our results show that the use of passive
voice only has a minor impact on the resulting domain models. The use of
ambiguous pronouns, however, shows a strong effect on various properties of the
resulting domain models. Most notably, ambiguous pronouns lead to incorrect
associations in domain models. Conclusion: Despite being equally advised
against by literature and frequentist methods, the Bayesian data analysis shows
that the two investigated quality defects have vastly different impacts on
software engineering activities and, hence, deserve different levels of
attention. Our employed method can be further utilized by researchers to
improve reliable, detailed empirical evidence on requirements quality.
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