Advertisement

An Approach for Prioritizing Software Features Based on Node Centrality in Probability Network

  • Zhenlian Peng
  • Jian WangEmail author
  • Keqing He
  • Hongtao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9679)

Abstract

Due to the increasing complexity of software products as well as the restriction of the development budget and time, requirements prioritization, i.e., selecting more crucial requirements to be designed and developed firstly, has become increasingly important in the software development lifetime. Considering the fact that a feature in a feature model can be viewed as a set of closely related requirements, feature prioritization will contribute to requirements prioritization to a large extent. Therefore, how to measure the priority of features within a feature model becomes an important issue in requirements analysis. In this paper, a software feature prioritization approach is proposed, which utilizes the dependencies between features to build a feature probability network and measures feature prioritization through the nodes centrality in the network. Experiments conducted on real world feature models show that the proposed approach can accurately prioritize features in feature models.

Keywords

Feature prioritization Feature model Feature probability network Centrality 

Notes

Acknowledgments

The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, and the National Natural Science Foundation of China under Nos. 61373037, 61272111, 61572186 and 61562073. The authors would like to thank anonymous reviewers for their valuable suggestions. Jian Wang is the corresponding author.

References

  1. 1.
    Tong, Z., Zhuang, Q., Guo, Q., Ma, P.: Research on technologies of software requirements prioritization. In: Yuan, Y., Wu, X., Lu, Y. (eds.) ISCTCS 2013. CCIS, vol. 426, pp. 9–21. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Hofmann, H.F., Lehner, F.: Requirements engineering as a success factor in software projects. IEEE Softw. 4, 58–66 (2001)CrossRefGoogle Scholar
  3. 3.
    Perini, A., Susi, A., Avesani, P.: A machine learning approach to software requirements prioritization. IEEE Trans. Softw. Eng. 39(4), 445–461 (2013)CrossRefGoogle Scholar
  4. 4.
    Peter, H., Olson, D., Rodgers, T.: Multi-criteria preference analysis for systematic requirements negotiation. In: 26th Annual International Conference on Computer Software and Applications, pp. 887–892. IEEE Press, New York (2002)Google Scholar
  5. 5.
    Saaty, R.W.: The analytic hierarchy process: what it is and how it is used. Math. Model. 9(3), 161–176 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Tonella, P., Susi, A., Palma, F.: Interactive requirements prioritization using a genetic algorithm. Inf. Softw. Technol. 55(1), 173–187 (2013)CrossRefGoogle Scholar
  7. 7.
    Harker, P.T.: Incomplete pairwise comparisons in the analytic hierarchy process. Math. Model. 9(11), 837–848 (1987)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Easmin, R., Gias, A.U., Khaled, S.M.: A partial order assimilation approach for software requirements prioritization. In: 3rd International Conference on Informatics, Electronics and Vision (ICIEV), pp. 1–5. IEEE Press, New York (2014)Google Scholar
  9. 9.
    Khari, M., Kumar, N.: Comparison of six prioritization techniques for software requirements. J. Glob. Res. Comput. Sci. 4(1), 38–43 (2013)Google Scholar
  10. 10.
    Achimugu, P., Selamat, A., Ibrahim, R., Mahrin, M.N.: A systematic literature review of software requirements prioritization research. Inf. Softw. Technol. 56(6), 568–585 (2014)CrossRefGoogle Scholar
  11. 11.
    Kang, K.C., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, A.S.: Feature-oriented domain analysis (FODA) feasibility study. Technical report, Carnegie Mellon University (1990)Google Scholar
  12. 12.
    Radatz, J., Geraci, A., Katki, F.: IEEE standard glossary of software engineering terminology. IEEE Stand. 610121990(121990), 3 (1990)Google Scholar
  13. 13.
    Zhang, W., Yan, H., Zhao, H., Jin, Z.: A BDD-based approach to verifying clone-enabled feature models’ constraints and customization. In: Mei, H. (ed.) ICSR 2008. LNCS, vol. 5030, pp. 186–199. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Czarnecki, K., Helsen, S., Eisenecker, U.: Staged configuration using feature models. In: Nord, R.L. (ed.) SPLC 2004. LNCS, vol. 3154, pp. 266–283. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    White, J., Dougherty, B., Schmidt, D.C., Benavides, D.: Automated reasoning for multi-step feature model configuration problems. In: 13th International Conference on Software Product Line, pp. 11–20. Carnegie Mellon University (2009)Google Scholar
  16. 16.
    Stoiber, R., Glinz, M.: Supporting stepwise, incremental product derivation in product line requirements engineering. In: 4th International Workshop on Variability Modelling of Software-Intensive Systems. ICB-Research report, pp. 77–84. University Duisburg-Essen (2010)Google Scholar
  17. 17.
    Bagheri, E., Asadi, M., Gasevic, D., Soltani, S.: Stratified analytic hierarchy process: prioritization and selection of software features. In: Bosch, J., Lee, J. (eds.) SPLC 2010. LNCS, vol. 6287, pp. 300–315. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Acher, M., Cleve, A., Perrouin, G., Heymans, P., Vanbeneden, C., Collet, P., Lahire, P.: On extracting feature models from product descriptions. In: 6th International Workshop on Variability Modeling of Software-Intensive Systems, pp. 45–54. ACM, New York (2012)Google Scholar
  19. 19.
    Benavides, D., Segura, S., Ruiz-Corts, A.: Automated analysis of feature models 20 years later: a literature review. Inf. Syst. 35(6), 615–636 (2010)CrossRefGoogle Scholar
  20. 20.
    Davril, J.M., Delfosse, E., Hariri, N., Acher, M., Cleland-Huang, J., Heymans, P.: Feature model extraction from large collections of informal product descriptions. In: 9th Joint Meeting on Foundations of Software Engineering, pp. 290–300. ACM, New York (2013)Google Scholar
  21. 21.
    Rincn, L.F., Giraldo, G.L., Mazo, R., Salinesi, C.: An ontological rule-based approach for analyzing dead and false optional features in feature models. Electron. Notes Theoret. Comput. Sci. 302, 111–132 (2014)CrossRefGoogle Scholar
  22. 22.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  23. 23.
    Zar, J.H.: Significance testing of the Spearman rank correlation coefficient. J. Am. Stat. Assoc. 67(339), 578–580 (1972)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhenlian Peng
    • 1
    • 2
  • Jian Wang
    • 1
    Email author
  • Keqing He
    • 1
  • Hongtao Li
    • 1
  1. 1.State Key Laboratory of Software Engineering, Computer SchoolWuhan UniversityWuhanChina
  2. 2.Computer SchoolHunan University of Science and TechnologyXiangtanChina

Personalised recommendations