Application of machine learning techniques to the flexible assessment and improvement of requirements quality

Author:

Moreno Valentín,Génova GonzaloORCID,Parra Eugenio,Fraga Anabel

Funder

ARTEMIS

ECSEL

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,Software

Reference87 articles.

1. Aceituna, D., Walia, G., Do, H., & Lee, S. W. (2014). Model-based requirements verification method: Conclusions from two controlled experiments. Information and Software Technology, 56(3), 321–334.

2. Alexander, I., Stevens, R. (2002). Writing better requirements. Addison-Wesley.

3. Ali, R., Dalpiaz, F., & Giorgini, P. (2013). Reasoning with contextual requirements: detecting inconsistency and conflicts. Information and Software Technology, 55(1), 35–57.

4. Belsis, P., Koutoumanos, A., & Sgouropoulou, C. (2014). PBURC: a patterns-based, unsupervised requirements clustering framework for distributed agile software development. Requirements Engineering, 19(2), 213–225.

5. Berry, D.M., Bucchiarone, A., Gnesi, S., Lami, G., Trentanni, G. (2006). A new quality model for natural language requirements specifications. Proceedings of the 12th International Working Conference on Requirements Engineering: Foundation of Software Quality (REFSQ-06). Luxembourg, June 5-6 2006. Held in conjunction with CAiSE’06.

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