Advertisement

Software effort estimation using FAHP and weighted kernel LSSVM machine

  • Sumeet Kaur Sehra
  • Yadwinder Singh Brar
  • Navdeep Kaur
  • Sukhjit Singh Sehra
Methodologies and Application
  • 10 Downloads

Abstract

In the life cycle of software product development, the software effort estimation (SEE) has always been a critical activity. The researchers have proposed numerous estimation methods since the inception of software engineering as a research area. The diversity of estimation approaches is very high and increasing, but it has been interpreted that no single technique performs consistently for each project and environment. Multi-criteria decision-making (MCDM) approach generates more credible estimates, which is subjected to expert’s experience. In this paper, a hybrid model has been developed to combine MCDM (for handling uncertainty) and machine learning algorithm (for handling imprecision) approach to predict the effort more accurately. Fuzzy analytic hierarchy process (FAHP) has been used effectively for feature ranking. Ranks generated from FAHP have been integrated into weighted kernel least square support vector machine for effort estimation. The model developed has been empirically validated on data repositories available for SEE. The combination of weights generated by FAHP and the radial basis function (RBF) kernel has resulted in more accurate effort estimates in comparison with bee colony optimisation and basic RBF kernel-based model.

Keywords

Software effort estimation Fuzzy analytic hierarchy process Least square support vector machine 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Attarzadeh I, Ow SH (2009) Software development effort estimation based on a new fuzzy logic model. Int J Comput Theory Eng 1(4):473–476CrossRefGoogle Scholar
  2. Azzeh M, Nassif AB (2016) A hybrid model for estimating software project effort from use case points. Appl Soft Comput 49:981–989CrossRefGoogle Scholar
  3. Belton V, Stewart T (2002) The multiple criteria problem. Multiple criteria decision analysis: an integrated approach. Springer, Cham, pp 13–33CrossRefGoogle Scholar
  4. Benala TR, Bandarupalli R (2016) Least square support vector machine in analogy-based software development effort estimation. In: International conference on recent advances and innovations in engineeringGoogle Scholar
  5. Braga PL, Oliveira ALI, Ribeiro GHT, Meira SRL (2007) Bagging predictors for estimation of software project effort. In: International joint conference on neural networks. IEEE, Florida, USA, pp 1595–1600Google Scholar
  6. Buckley J (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247MathSciNetCrossRefGoogle Scholar
  7. Chalotra S, Sehra S, Brar Y, Kaur N (2015a) Tuning of cocomo model parameters by using bee colony optimization. Indian J Sci Technol 8(14):1–5CrossRefGoogle Scholar
  8. Chalotra S, Sehra SK, Sehra SS (2015b) An analytical review of nature inspired optimization algorithms. Int J Sci Technol Eng 2(2):123–126Google Scholar
  9. Chang D-Y (1992) Extent analysis and synthetic decision, optimization techniques and applications. World Sci 1(1):352–355Google Scholar
  10. Chang D-Y (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655CrossRefGoogle Scholar
  11. Corazza A, Martino SD, Ferrucci F, Gravino C, Mendes E (2011) Investigating the use of support vector regression for web effort estimation. Empir Softw Eng 16(2):211–243CrossRefGoogle Scholar
  12. Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the lambda-max method. Fuzzy Sets Syst 120(2):181–195MathSciNetCrossRefGoogle Scholar
  13. Dasheng X, Shenglan H (2012) Estimation of project costs based on fuzzy neural network. In: World congress on information and communication technologies. IEEE, Trivandrum, India, pp 1177–1181Google Scholar
  14. Dave VS, Dutta K (2011) Comparison of regression model, feed-forward neural network and radial basis neural network for software development effort estimation. ACM SIGSOFT Softw Eng Notes 36(5):1–5CrossRefGoogle Scholar
  15. Ferrucci F, Gravino C, Sarro F (2011) How multi-objective genetic programming is effective for software development effort estimation? Search based software engineering. Springer, New York, pp 274–275Google Scholar
  16. Furulund KM, Molokken-Ostvold K (2007) Increasing software effort estimation accuracy using experience data, estimation models and checklists. In: Seventh international conference on quality software. IEEE, Portland, USA, pp 342–347Google Scholar
  17. Gestel Suykens JA, Baesens B, Viaene S, Vanthienen J, Dedene G, de Moor B, Vandewalle J (2004) Benchmarking least squares support vector machine classifiers. Mach Learn 54:5–32CrossRefGoogle Scholar
  18. Gharehchopogh FS, Rezaii R, Arasteh B (2015) A new approach by using Tabu search and genetic algorithms in software cost estimation. In: 9th International conference on application of information and communication technologies. IEEE, Rostov-on-Don, Russia, pp 113–117Google Scholar
  19. Glass RL, Vessey I, Ramesh V (2002) Research in software engineering: an analysis of the literature. Inf Softw Technol 44(8):491–506CrossRefGoogle Scholar
  20. Guo B, Gunn SR, Damper RI, Nelson JDB (2008) Customizing kernel functions for SVM-based hyperspectral image classification. IEEE Trans Image Process 17(4):622–629MathSciNetCrossRefGoogle Scholar
  21. Hidmi O, Sakar BE (2017) Software development effort estimation using ensemble machine learning. Int J Comput Commun Instrum Eng 4(1):143–147Google Scholar
  22. Idri A, Abran A, Mbarki S (2006) An experiment on the design of radial basis function neural networks for software cost estimation. In: International conference on information & communication technologies: from theory to applications, vol 2. IEEE, Damascus, Syria, pp 1612–1617Google Scholar
  23. Idri A, Khoshgoftaar TM, Abran A (2002) Can neural networks be easily interpreted in software cost estimation? In: IEEE international conference on fuzzy systems FUZZ-IEEE’02, vol 2. IEEE, Honolulu, Hawaii, pp 1162–1167Google Scholar
  24. Idri A, Zahi A, Mendes E, Zakrani A (2007) Software cost estimation models using radial basis function neural networks. In: Cuadrado-Gallego JJ, Braungarten RB, Dumke RR, Arban A (eds) Software process and product measurements, vol 4895. Lecture notes in computer science. Springer, Berlin, pp 21–31CrossRefGoogle Scholar
  25. Jiang Z, Naudé P (2007) An examination of the factors influencing software development effort. Int J Comput Inf Syst Sci Eng 1(4):182–191Google Scholar
  26. Jørgensen M, Shepperd M (2007) A systematic review of software development cost estimation studies. IEEE Trans Softw Eng 33(1):33–53CrossRefGoogle Scholar
  27. Jørgensen M, Boehm B, Rifkin S (2009) Software development effort estimation: Formal models or expert judgment? IEEE Softw 26(2):14–19CrossRefGoogle Scholar
  28. Kahraman C, Cebeci U, Ulukan Z (2003) Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag 16(6):382–394CrossRefGoogle Scholar
  29. Kocaguneli E, Menzies T, Mendes E (2015) Transfer learning in effort estimation. Emp Softw Eng 20(3):813–843CrossRefGoogle Scholar
  30. Kuswandari R (2004) Assessment of different methods for measuring the sustainability of forest management. Master’s thesis and Earth Observation, International Institute for Geo-information Science, Enschede, The NetherlandsGoogle Scholar
  31. Lee W-S, Tu W-S (2011) Combined MCDM techniques for exploring company value based on Modigliani–Miller theorem. Expert Syst Appl 38(7):8037–8044CrossRefGoogle Scholar
  32. Liao C-N (2011) Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy. Comput Ind Eng 61(3):831–841CrossRefGoogle Scholar
  33. Liu Q, Shi S, Zhu H, Xiao J (2014) A mutual information-based hybrid feature selection method for software cost estimation using feature clustering. In: 38th annual IEEE computer software and applications conference. IEEE, Vasteras, Sweden, pp 27–32Google Scholar
  34. Liu W, Liu L, Tong F (2017) Least squares support vector machine for ranking solutions of multi-objective water resources allocation optimization models. Water 9:1–15Google Scholar
  35. Liyi M, Shiyu Z, Jian G (2010) A project risk forecast model based on support vector machine. In: IEEE international conference on software engineering and service sciences, Beijing, China, pp 463–466Google Scholar
  36. Madheswaran M, Sivakumar D (2014) Enhancement of prediction accuracy in COCOMO model for software project using neural network. In: International conference on computing, communication and networking technologies. IEEE, Hefei, China, pp 1–5Google Scholar
  37. Marković I, Stojanović M, Božić M, Stanković J (2015) Stock market trend prediction based on the LS-SVM model update algorithm. In: Bogdanova A (ed) ICT innovations 2014. Advances in intelligent systems and computing, vol 311. Springer, Cham, pp 105–114Google Scholar
  38. Marković I, Stojanović M, Stanković J, Stanković M (2017) Stock market trend prediction using ahp and weighted kernel LS-SVM. Soft Comput 21(18):5387–5398CrossRefGoogle Scholar
  39. Mendes E, Watson I, Triggs C, Mosley N, Counsell S (2002) A comparison of development effort estimation techniques for Web hypermedia applications. In: Eighth IEEE symposium on software metrics. IEEE, Ottawa, Canada, pp 131–140Google Scholar
  40. Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32(11):883–895CrossRefGoogle Scholar
  41. Menzies T, Caglayan B, He Z, Kocaguneli E, Krall J, Peters F, Turhan B (2012) The promise repository of empirical software engineering dataGoogle Scholar
  42. Mikhailov L, Tsvetinov P (2004) Evaluation of services using a fuzzy analytic hierarchy process. Appl Soft Comput 5(1):23–33CrossRefGoogle Scholar
  43. Milios D, Stamelos I, Chatzibagias C (2013) A genetic algorithm approach to global optimization of software cost estimation by analogy. Intell Decis Technol 7(1):45–58CrossRefGoogle Scholar
  44. Minku LL, Yao X (2013) Software effort estimation as a multiobjective learning problem. ACM Trans Softw Eng Methodol 22(4):35:1–35:32Google Scholar
  45. Morgenshtern O, Raz T, Dvir D (2007) Factors affecting duration and effort estimation errors in software development projects. Inf Softw Technol 49(8):827–837CrossRefGoogle Scholar
  46. Naghadehi MZ, Mikaeil R, Ataei M (2009) The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran. Expert Syst Appl 36(4):8218–8226CrossRefGoogle Scholar
  47. Nisar M, Wang Y-J, Elahi M (2008) Software development effort estimation using fuzzy logic—a survey. In: Fifth international conference on fuzzy systems and knowledge discovery, vol 1. IEEE, Shandong, China, pp 421–427Google Scholar
  48. Oliveira ALI, Braga PL, Lima RMF, Cornélio ML (2010) GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Inf Softw Technol 52(11):1155–1166CrossRefGoogle Scholar
  49. Rastogi H, Dhankhar S, Kakkar M (2014) A survey on software effort estimation techniques. In: Confluence the next generation information technology summit (confluence), 2014 5th international conference. IEEE, Noida, India, pp 826–830Google Scholar
  50. Reddy P, Sudha K, Sree PR, Ramesh S (2010) Software effort estimation using radial basis and generalized regression neural networks. J Comput 2(5):87–92Google Scholar
  51. Ryder J (1998) Fuzzy modeling of software effort prediction. In: Information technology conference. IEEE, Syracuse, USA, pp 53–56Google Scholar
  52. Saaty T (2004) Decision making—the analytic hierarchy and network processes (AHP/ANP). J Syst Sci Syst Eng 13(1):1–35CrossRefGoogle Scholar
  53. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98MathSciNetGoogle Scholar
  54. Shaw M (2002) What makes good research in software engineering? Int J Softw Tools Technol Trans 4(1):1–7CrossRefGoogle Scholar
  55. Shepperd M, Cartwright M (2001) Predicting with sparse data. IEEE Trans Softw Eng 27(11):987–998CrossRefGoogle Scholar
  56. Sheta AF, Rine D, Kassaymeh S (2015) Software effort and function points estimation models based radial basis function and feedforward artificial neural networks. Int J Next Gen Comput 6(3):192–205Google Scholar
  57. Srivastava DK, Chauhan DS, Singh R (2012) VRS model: a model for estimation of efforts and time duration in development of IVR software system. Int J Softw Eng 5(1):27–46Google Scholar
  58. Suykens J, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  59. Tang Y-C, Beynon MJ et al (2005) Application and development of a fuzzy analytic hierarchy process within a capital investment study. J Econ Manag 1(2):207–230Google Scholar
  60. Trendowicz A, Münch J, Jeffery R (2008) State of the practice in software effort estimation: a survey and literature review. In: Lin Z, Hu Y, Madachy R, Ravi KV, Boehm BW (eds) Software engineering techniques, vol 4980. Lecture notes in computer science. Springer, Berlin, pp 232–245CrossRefGoogle Scholar
  61. Van Laarhoven P, Pedrycz W (1983) A fuzzy extension of Saaty’ s priority theory. Fuzzy Sets Syst 11(1–3):229–241MathSciNetCrossRefGoogle Scholar
  62. Vapnik V (2013) Nature of statistical learning theory. Information science and statistics, 2nd edn. Springer, New YorkGoogle Scholar
  63. Wang Y-M, Luo Y (2009) On rank reversal in decision analysis. Math Comput Model 49(5–6):1221–1229MathSciNetCrossRefGoogle Scholar
  64. Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54(1):41–59CrossRefGoogle Scholar
  65. Wong J, Ho D, Capretz LF (2009) An investigation of using neuro-fuzzy with software size estimation. In: ICSE workshop on software quality. IEEE, Vancouver, Canada, pp 51–58Google Scholar
  66. Xing H-J, Ha MH, Hu BG, Tian DZ (2009) Linear feature-weighted support vector machine. Fuzzy Inf Eng 1(3):289–305CrossRefGoogle Scholar
  67. Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93CrossRefGoogle Scholar
  68. Zelkowitz MV, Yeh RT, Hamlet RG, Gannon JD, Basili VR (1984) Software engineering practices in the US and Japan. Computer 17(6):57–70CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Sumeet Kaur Sehra
    • 1
    • 2
  • Yadwinder Singh Brar
    • 1
  • Navdeep Kaur
    • 3
  • Sukhjit Singh Sehra
    • 4
  1. 1.I.K.G. Punjab Technical UniversityJalandharIndia
  2. 2.Guru Nanak Dev Engineering CollegeLudhianaIndia
  3. 3.Sri Guru Granth Sahib World UniversityFatehgarh SahibIndia
  4. 4.Elocity Technology Inc.TorontoCanada

Personalised recommendations