Skip to main content

Solving Agile Software Development Problems with Swarm Intelligence Algorithms

  • Conference paper
  • First Online:
New Technologies, Development and Application II (NT 2019)

Abstract

This paper outlines a short overview of swarm intelligence algorithms that are used within the software engineering area. Swarm intelligence algorithms have been used in many software engineering tasks, e.g., grammatical inference or mutation testing. However, their presence in the agile software development field is still awakening. As there are some promising results of solving different problems of agile software development with swarm intelligence, this paper discusses such problems and the proposed solutions within the last decade. Based on the results we propose a systematic classification of swarm intelligence algorithms according to problems within agile software development, i.e., next release problem, risk, software design, software cost estimation, and software effort estimation. Afterwards, we present papers that fall in the scope of the proposed classification, and provide highlights of each paper for researchers, conducting research in this and associated fields. In this manner, we provide some conclusions for each of the classified problem groups, and, in the end, we review the guidelines for the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that only the last nine years were considered in this study.

References

  1. Agrawal, R., Singh, D., Sharma. A.: Prioritizing and optimizing risk factors in agile software development. In: 2016 Ninth International Conference on Contemporary Computing (IC3), pp. 1–7 (2016)

    Google Scholar 

  2. Aloka, S., Singh, P., Rakshit, G., Srivastava, P.R.: Test Effort Estimation-Particle Swarm Optimization Based Approach, pp. 463–474. Springer, Heidelberg (2011)

    Google Scholar 

  3. Azzeh, M.: Adjusted Case-Based Software Effort Estimation Using Bees Optimization Algorithm, pp. 315–324. Springer, Heidelberg (2011)

    Google Scholar 

  4. Beniand, G., Wang, J.: Swarm Intelligence in Cellular Robotic Systems, pp. 703–712. Springer, Heidelberg (1993)

    Google Scholar 

  5. Brezočnik, L., Fister, I., Podgorelec, V.: Scrum task allocation based on particle swarm optimization. In: Korošec, P., Melab, N., Talbi, E.-G. (eds.) Bioinspired Optimization Methods and Their Applications, pp. 38–49. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  6. Brezočnik, L., Podgorelec, V.: Applying weighted particle swarm optimization to imbalanced data in software defect prediction. In: Karabegović, I. (ed.) New Technologies, Development and Application, pp. 289–296. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  7. Brezočnik, L., Fister, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8(9) (2018)

    Article  Google Scholar 

  8. Chaves-González, J.M., Pérez-Toledano, M.A., Navasa, A.: Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm. Knowl.-Based Syst. 83, 105–115 (2015)

    Article  Google Scholar 

  9. de Souza, J.T., Maia, C.L.B., do Nascimento Ferreira, T., de do Carmo, R.A.F., de Brasil, M.M.A.: An AntColony Optimization Approach to the Software Release Planning with Dependent Requirements, pp. 142–157. Springer, Heidelberg (2011)

    Google Scholar 

  10. delSagrado, J., del Águila, I.M., Orellana, F.J.: Multi-objective ant colony optimization for requirements selection. Empirical Softw. Eng. 20(3), 577–610 (2015)

    Article  Google Scholar 

  11. do Nascimento Ferreira, T., Arajo, A.A., Neto, A.D.B., de Souza, J.T.: J.T.: Incorporating user preferences in ant colony optimization for the next release problem. Appl. Soft Comput. 49, 1283–1296 (2016)

    Article  Google Scholar 

  12. Harman, M.: The current state and future of search based software engineering. In: 2007 Future of Software Engineering, pp. 342–357. IEEE Computer Society (2007)

    Google Scholar 

  13. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Softw. Eng. 37(5), 649–678 (2011)

    Article  Google Scholar 

  14. Jiang, H., Zhang, J., Xuan, J., Ren, Z., Hu, Y.: A hybrid ACO algorithm for the next release problem. In: The 2nd International Conference on Software Engineering and Data Mining, pp. 166–171. IEEE (2010)

    Google Scholar 

  15. Jiang, J.-J., Yang, X., Yin, M.: Cooperative control model of geographically distributed multi-team agile development based on MO-CSO. In: Proceedings of the 2nd International Conference on E-Education, E-Business and E-Technology, ICEBT 2018, pp. 121–125, New York, NY, USA. ACM (2018)

    Google Scholar 

  16. Kaushik, A., Verma, S., Singh, H.J., Chhabra, G.: Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm. Int. J. Syst. Assur. Eng. Manag. 8(2), 1461–1471 (2017)

    Article  Google Scholar 

  17. KhatibiBardsiri, V., Jawawi, D.N.A., Hashim, S.Z.M., Khatibi, E.: A PSO-based modelto increase the accuracy of software development effort estimation. Softw. Qual. J. 21(3), 501–526 (2013)

    Article  Google Scholar 

  18. Khuat, T., Le. M.: A Novel Hybrid ABC-PSO algorithm for effort estimation of software projects using agile methodologies. J. Intell. Syst. 1–18 (2017)

    Google Scholar 

  19. Khuat, T., My Hanh, L.: Applying teaching-learning to artificial bee colony for parameter optimization of software effort estimation model. J. Eng Sci. Technol 12(5), 1178–1190 (2017)

    Google Scholar 

  20. Manga, I., Blamah, N.: A particle swarm optimization-based framework for agile software effort estimation. Int. J. Eng. Sci. (IJES) 3, 30–36 (2014)

    Google Scholar 

  21. Mernik, M., Hrnčič, D., Bryant, B.R., Sprague, A.P., Gray, J., Liu, Q., Javed, F.: Grammar inference algorithms and applications in software engineering. In: 2009 XXII International Symposium on Information, Communication and Automation Technologies. ICAT 2009, pp. 1–7. IEEE (2009)

    Google Scholar 

  22. Prasad Reddy, P.V.G.D., Hari, C.V.M.K.: Fuzzy Based PSO for Software Effort Estimation, pp. 227–232. Springer, Heidelberg (2011)

    Google Scholar 

  23. Ranjith, N., Marimuthu, A.: A multi objective teacher-learning-artificial bee colony(MOTLABC) optimization for software requirements selection. Indian J. Sci.Technol. 6 (2016)

    Google Scholar 

  24. Rao, G.S., Krishna, C.V.P., Rao, K.R.: Multi Objective Particle Swarm Optimization for Software Cost Estimation, pp. 125–132. Springer International Publishing (2014)

    Google Scholar 

  25. Simons, C.L., Smith, J., White, P.: Interactive ant colony optimization (iACO) for early lifecycle software design. Swarm Intell. 8(2), 139–157 (2014)

    Article  Google Scholar 

  26. Sörensen, K.: Metaheuristics–the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2013). https://doi.org/10.1111/itor.12001

    Article  MathSciNet  Google Scholar 

  27. Srivastava, P.R., Varshney, A., Nama, P., Yang, X.-S.: Software test effort estimation: a model based on cuckoo search. Int. J. Bio-Inspired Comput. 4(5), 278–285 (2012)

    Article  Google Scholar 

  28. Venkataiah, V., Mohanty, R., Pahariya, J.S., Nagaratna, M.: Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation, pp. 315–325. Springer, Singapore (2017)

    Google Scholar 

  29. VersionOne. VersionOne 12th Annual State of Agile Report (2018)

    Google Scholar 

  30. Wu, D., Li, J., Liang, Y.: Linear combination of multiple case-based reasoning with optimized weight for software effort estimation. J. Supercomput. 64(3), 898–918 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lucija Brezočnik .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brezočnik, L., Fister, I., Podgorelec, V. (2020). Solving Agile Software Development Problems with Swarm Intelligence Algorithms. In: Karabegović, I. (eds) New Technologies, Development and Application II. NT 2019. Lecture Notes in Networks and Systems, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-18072-0_35

Download citation

Publish with us

Policies and ethics