Dimitrios Tzimos
ABSTRACT
Requirements Prioritization (RP) is an important activity in requirements engineering aiming to give priority and order to requirements for implementation in the next version of a software project. RP is applied iteratively, according to various prioritization criteria, by multiple project stakeholders who may have different roles, needs and knowledge. In large-scale software projects, where the set of candidate requirements is large, stakeholders may not be interested in evaluating all requirements and they may not have the expertise, the time or the willingness to consider all candidate requirements. Recommender Systems (RS) can be a useful solution to information overload when stakeholders have to evaluate a large number of alternatives. During evaluation of requirements, it is often practically impossible to ensure that all stakeholders have complete knowledge on all requirements. Thus, stakeholders may show some degree of uncertainty and hesitation, as it is difficult to precisely evaluate each requirement according to each prioritization criterion. The intuitionistic Fuzzy Sets (IFSs) are an extension of fuzzy sets which can deal with stakeholders’ uncertainty and hesitation regarding the prioritization criteria importance and requirements ratings. The aim of this paper is to present an RS approach based on the collaborative filtering technique to effectively provide suggestions to stakeholders while prioritizing requirements. The proposed RS approach is tested using a publicly available large dataset of software requirements and the results show an improved performance.
- A. Aurum and C. Wohlin, Eds., Engineering and managing software requirements. Berlin: Springer, 2005.Google Scholar
Cross Ref
- P. Achimugu, A. Selamat, R. Ibrahim, and M. N. Mahrin, “A systematic literature review of software requirements prioritization research,” Information and Software Technology, vol. 56, no. 6, pp. 568–585, Jun. 2014, doi: 10.1016/j.infsof.2014.02.001.Google ScholarDigital Library
- F. Hujainah, R. B. A. Bakar, M. A. Abdulgabber, and K. Z. Zamli, “Software Requirements Prioritisation: A Systematic Literature Review on Significance, Stakeholders, Techniques and Challenges,” IEEE Access, vol. 6, pp. 71497–71523, 2018, doi: 10.1109/ACCESS.2018.2881755.Google ScholarCross Ref
- P. Resnick and H. R. Varian, “Recommender systems,” Commun. ACM, vol. 40, no. 3, pp. 56–58, Mar. 1997, doi: 10.1145/245108.245121.Google ScholarDigital Library
- F. Ricci, L. Rokach, and B. Shapira, “Recommender Systems: Introduction and Challenges,” in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Boston, MA: Springer US, 2015, pp. 1–34. doi: 10.1007/978-1-4899-7637-6_1.Google ScholarCross Ref
- R.-X. Ding, X. Wang, K. Shang, B. Liu, and F. Herrera, “Sparse Representation-Based Intuitionistic Fuzzy Clustering Approach to Find the Group Intra-Relations and Group Leaders for Large-Scale Decision Making,” IEEE Trans. Fuzzy Syst., vol. 27, no. 3, pp. 559–573, Mar. 2019, doi: 10.1109/TFUZZ.2018.2864661.Google ScholarDigital Library
- V. C. Gerogiannis, E. Tsoni, C. Born, and O. Iatrellis, “Software Features Prioritization based on Stakeholders’ Satisfaction/Dissatisfaction and Hesitation,” in 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, Aug. 2020, pp. 265–271. doi: 10.1109/SEAA51224.2020.00052.Google ScholarCross Ref
- K. T. Atanassov, On Intuitionistic Fuzzy Sets Theory, vol. 283. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. doi: 10.1007/978-3-642-29127-2.Google ScholarCross Ref
- K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87–96, 1986, doi: https://doi.org/10.1016/S0165-0114(86)80034-3.Google ScholarCross Ref
- V. C. Gerogiannis, P. Fitsilis, G. Kakarontzas, and C. Born, “Handling vagueness and subjectivity in requirements prioritization,” in Proceedings of the 22nd Pan-Hellenic Conference on Informatics - PCI ’18, Athens, Greece, 2018, pp. 150–155. doi: 10.1145/3291533.3291574.Google ScholarDigital Library
- R. T. Ngan, L. H. Son, B. C. Cuong, and M. Ali, “H-max distance measure of intuitionistic fuzzy sets in decision making,” Applied Soft Computing, vol. 69, pp. 393–425, Aug. 2018, doi: 10.1016/j.asoc.2018.04.036.Google ScholarCross Ref
- Soo Ling Lim and A. Finkelstein, “StakeRare: Using Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation,” IIEEE Trans. Software Eng., vol. 38, no. 3, pp. 707–735, May 2012, doi: 10.1109/TSE.2011.36.Google ScholarDigital Library
- T. Chaira, Fuzzy set and its extension: the intuitionistic fuzzy set, First edition. Hoboken, NJ, USA: Wiley, 2019.Google ScholarCross Ref
- L. H. Son and N. T. Thong, “Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis,” Knowledge-Based Systems, vol. 74, pp. 133–150, Jan. 2015, doi: 10.1016/j.knosys.2014.11.012.Google ScholarDigital Library
- A. Felfernig, L. Boratto, M. Stettinger, and M. Tkalčič, “Evaluating Group Recommender Systems,” in Group Recommender Systems, Cham: Springer International Publishing, 2018, pp. 59–71. doi: 10.1007/978-3-319-75067-5_3.Google ScholarCross Ref
- P. G. Campos, F. Díez, and I. Cantador, “Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols,” User Model User-Adap Inter, vol. 24, no. 1–2, pp. 67–119, Feb. 2014, doi: 10.1007/s11257-012-9136-x.Google ScholarDigital Library
- C. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Clim. Res., vol. 30, pp. 79–82, 2005, doi: 10.3354/cr030079.Google ScholarCross Ref
Index Terms
- A Recommender System based on Intuitionistic Fuzzy Sets for Software Requirements Prioritization
Recommendations
Model-based prioritization in business-process-driven software development
RE '12: Proceedings of the 2012 IEEE 20th International Requirements Engineering Conference (RE)Requirements engineers in business-process-driven software development are faced with the challenge of letting stakeholders determine which requirements are actually relevant for early business success and should be considered first or even at all ...
Group Recommender User Interfaces for Improving Requirements Prioritization
UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and PersonalizationRequirements engineering is one of the most critical phases in the context of software development. Unclear textual specifications of requirements, hidden dependencies between requirements, and suboptimal prioritizations and release plans represent the ...
Multi-person decision-making for requirements prioritization using fuzzy AHP
The success of a system depends upon how effectively it accomplishes its intended purpose by meeting all stakeholders' concerns pertaining to conflicting requirements such as cost, size, time, performance etc. Conflicts occur because the requirements ...
Comments