Journal
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
Volume 21, Issue 2, Pages 167-190Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218194011005219
Keywords
Requirement engineering; risk assessment; data mining; Bayesian networks classifiers
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Funding
- Spanish Ministry of Education and Science [TIN2010-20900-C04-02]
- Junta of Andalucia [TEP-06174]
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Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.
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