Abstract
[Context & motivation] Bidding processes are a usual requirement elicitation instrument for large IT or infrastructure projects. An organization or agency issues a Request for Proposal (RFP) and interested companies may submit compliant offers. [Problem] Such RFPs comprise natural language documents of several hundreds of pages with requirements of various kinds mixed with other information. The analysis of that huge amount of information is very time consuming and cumbersome because bidding companies should not disregard any requirement stated in the RFP. [Principal ideas/results] This research preview paper presents a first version of a classification component, OpenReq Classification Service (ORCS), which extracts requirements from RFP documents while discarding irrelevant text. ORCS is based on the use of Naïve Bayes classifiers. We have trained ORCS with 6 RFPs and then tested the component with 4 other RFPs, all of them from the railway safety domain. [Contribution] ORCS paves the way to improved productivity by reducing the manual effort needed to identify requirements from natural language RFPs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Berry, D.M.: Evaluation of tools for hairy requirements and software engineering tasks. In: REW 2017 (2017)
Palomares, C., Franch, X., Fucci, D.: Personal recommendations in requirements engineering: the OpenReq approach. In: Kamsties, E., Horkoff, J., Dalpiaz, F. (eds.) REFSQ 2018. LNCS, vol. 10753, pp. 297–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77243-1_19
Shalev, S., Ben, S.: Understanding Machine Learning. Cambridge University Press, Cambridge (2014)
Brink, H., et al.: Real-World Machine Learning. Manning Publications, New York (2016)
Rennie, J., et al.: Tackling the poor assumptions of Naive Bayes text classifiers. In: ICML 2003 (2003)
Quer, C., et al.: Reconciling practice and rigour in ontology-based heterogeneous information systems construction. In: Buchmann, R.A., Karagiannis, D., Kirikova, M. (eds.) PoEM 2018. LNBIP, vol. 335, pp. 205–220. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02302-7_13
Yang, Y.: An evaluation of statistical approaches to text categorization. J. Inf. Retrieval 1, 69–90 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Falkner, A., Palomares, C., Franch, X., Schenner, G., Aznar, P., Schoerghuber, A. (2019). Identifying Requirements in Requests for Proposal: A Research Preview. In: Knauss, E., Goedicke, M. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2019. Lecture Notes in Computer Science(), vol 11412. Springer, Cham. https://doi.org/10.1007/978-3-030-15538-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-15538-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15537-7
Online ISBN: 978-3-030-15538-4
eBook Packages: Computer ScienceComputer Science (R0)