Skip to main content
SpringerLink
Log in

A Taxonomy of Approaches and Methods for Software Effort Estimation

  • Conference paper
  • First Online:
Innovations in Computer Science and Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 385))

Abstract

Accurate effort estimation in software products is all-important and indispensable. So far, different taxonomies have been proposed in the literature of software cost estimation. Each taxonomy has specific principles and factors and is utilized in particular applications. In recent years, there has been significant progress in this field of research. Many papers have adopted different strategies to the problem of software cost estimation; each one proposes a relative taxonomy concerning a specific approach. In this paper, we introduce a novel taxonomy based on techniques presented in the field of software cost estimation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
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

References

  1. Attarzadeh I, Ow SH (2010) Proposing a new software cost estimation model based on artificial neural networks In: 2nd International Conference on Computer Engineering and Technology pp V3-487–V3-491

    Google Scholar 

  2. Hari CV, Singh Sethi T (2011) SEEPC: a toolbox for software effort estimation using soft computing techniques. International J Comput Appl 31(4):12–19

    Google Scholar 

  3. Khatibi V, Jawawi DN (2011) Software cost estimation methods: a review 1

    Google Scholar 

  4. Bilgaiyan S, Mishra S, Das M (2016) A review of software cost estimation in agile software development using soft computing techniques In: 2nd International Conference on Computational Intelligence and Networks (CINE) pp 112–117

    Google Scholar 

  5. Mévellec P, Perry N (2006) Whole life-cycle costs: a new approach International Journal of Product Lifecycle Management 1(4):400–414

    Google Scholar 

  6. Minku LL, Yao X (2013) Ensembles and locality: insight on improving software effort estimation Information Software Technology 55(8):1512–1528

    Google Scholar 

  7. Xu Y, Elgh F, Erkoyuncu JA, Bankole O, Goh Y, Cheung WM, Baguley P, Wang Q, Arundachawat P, Shehab E, Newnes L (2012) Cost Engineering for manufacturing: current and future research International Journal of Computer Integrated Manufacturing 25(4–5):300–314

    Google Scholar 

  8. Benala TR, Dehuri S, Mall R (2012) Computational intelligence in software cost estimation: an emerging paradigm. ACM SIGSOFT Softw Eng Notes 37(3):1–7

    Article  Google Scholar 

  9. Singh A, Kumar M (2020) Comparative analysis on prediction of software effort estimation using machine learning techniques. SSRN 3565822

    Google Scholar 

  10. Niazi A, Dai JS, Balabani S, Seneviratne L (2006) Product cost estimation: technique classification and methodology review. J Manuf Sci Eng 128(2):563–575

    Article  Google Scholar 

  11. Jørgensen M (2014) What we do and don’t know about software development effort estimation IEEE software 31(2):37–40

    Google Scholar 

  12. Layer A, Brinke ET, Houten FV, Kals H, Haasis S (2002) Recent and future trends in cost estimation International journal of computer integrated manufacturing 15(6):499–510

    Google Scholar 

  13. Korpi E, Ala‐Risku T (2008) Life cycle costing: a review of published case studies Managerial auditing journal

    Google Scholar 

  14. Newnes L, Mileham A, Cheung WM, Marsh R, Lanham J, Saravi ME, Bradbery RW (2008) Predicting the whole-life cost of a product at the conceptual design stage Journal of Engineering Design 19(2):99–112

    Google Scholar 

  15. Heemstra F, Kusters R (1991) Function point analysis: evaluation of a software cost estimation model. Eur J Inf Syst 1(4):229–237

    Article  Google Scholar 

  16. Heemstra FJ (1992) Software cost estimation. Inf Softw Technol 34(10):627–639

    Article  Google Scholar 

  17. Boehm B, Abts C, Chulani S (2000) Software development cost estimation approaches—a survey. Ann Softw Eng 10(1–4):177–205

    Article  Google Scholar 

  18. Jorgensen M, Shepperd M (2007) A systematic review of software development cost estimation studies. IEEE Trans Softw Eng 33(1)

    Google Scholar 

  19. Molokken K, Jorgensen M (2003) A review of software surveys on software effort estimation International Symposium on Empirical Software Engineering pp 223–230

    Google Scholar 

  20. Keaveney S, Conboy K (2006) Cost estimation in agile development projects pp 183–197

    Google Scholar 

  21. Leung H, Fan Z (2002) Software cost estimation. In: Handbook of software engineering and knowledge engineering: volume II: emerging technologies. World Scientific, pp. 307–324

    Google Scholar 

  22. Patil LV, Waghmode RM, Joshi S, Khanna V (2014) Generic model of software cost estimation: a hybrid approach IEEE International Advance Computing Conference (IACC) pp 1379–1384

    Google Scholar 

  23. Naik P, Nayak S (2017) Insights on research techniques towards cost estimation in software design. Int J Electr Comput Eng 7(5):2883

    Google Scholar 

  24. Bingamawa MT, Kamalrudin M A review of software cost estimation: tools, methods, and techniques

    Google Scholar 

  25. Kumar PS, Behera H (2020) Role of soft computing techniques in software effort estimation: an analytical study. In: Computational Intelligence in Pattern Recognition. Springer, pp 807–831

    Google Scholar 

  26. Osman HH, Musa ME (2016) A survey of Agile software estimation methods. Int J Comput Sci Telecommun 7(3)

    Google Scholar 

  27. Boehm BW (1981) Software engineering economics. Prentice-hall Englewood Cliffs (NJ)

    Google Scholar 

  28. Putnam LH (1978) A general empirical solution to the macro software sizing and estimating problem. IEEE Trans Softw Eng 4:345–361

    Article  Google Scholar 

  29. Albrecht AJ, Gaffney JE (1983) Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans Softw Eng 6:639–648

    Article  Google Scholar 

  30. Jensen R (1983) An improved macrolevel software development resource estimation model. pp 88–92

    Google Scholar 

  31. Grenning J (2002) Planning poker or how to avoid analysis paralysis while release planning. Hawthorn Woods: Renaissance Softw Consult 3:22–23

    Google Scholar 

  32. Moløkken-Østvold K, Haugen NC, Benestad HC (2008) Using planning poker for combining expert estimates in software projects. J Syst Softw 81(12):2106–2117

    Article  Google Scholar 

  33. Helmer O, Brown B, Gordon TJ (1966) Social Technology.[By] Olaf Helmer.[With] Contributions by Bernice Brown, Theodore Gordon: Basic Books

    Google Scholar 

  34. Keung J (2009) Software development cost estimation using analogy: a review AustralianSoftware Engineering Conference pp 327–336

    Google Scholar 

  35. Li Y-F, Xie M, Goh T (2009) A study of the non-linear adjustment for analogy based software cost estimation. Empir Softw Eng 14(6):603–643

    Article  Google Scholar 

  36. Jørgensen M (2004) Top-down and bottom-up expert estimation of software development effort. Inf Softw Technol 46(1):3–16

    Article  Google Scholar 

  37. Dote Y, Ovaska SJ (2001) Industrial applications of soft computing: a review. Proc IEEE 89(9):1243–1265

    Article  Google Scholar 

  38. Attarzadeh I, Mehranzadeh A, Barati A (2012) Proposing an enhanced artificial neural network prediction model to improve the accuracy in software effort estimation Fourth International Conference on Computational Intelligence, Communication Systems and Networks pp 167–172

    Google Scholar 

  39. Nassif AB, Azzeh M, Capretz LF, Ho D (2016) Neural network models for software development effort estimation: a comparative study. Neural Comput Appl 27(8):2369–2381

    Article  Google Scholar 

  40. Kumar PS, Behera HS, Kumari A, Nayak J, Naik B Advancement from neural networks to deep learning in software effort estimation: perspective of two decades. Comput Sci Rev 38:100288

    Google Scholar 

  41. N. Tadayon N (2005) Neural network approach for software cost estimation International Conference on Information Technology: Coding and Computing Vol 2, pp 815–818

    Google Scholar 

  42. Kumar KV, Ravi V, Carr M, Kiran NR (2008) Software development cost estimation using wavelet neural networks. J Syst Softw 81(11):1853–1867

    Article  Google Scholar 

  43. Idri A, Abran A, Kjiri L (2000) COCOMO cost model using fuzzy logic In: 7th international conference on fuzzy theory & techniques Vol 27

    Google Scholar 

  44. Mittal A, Parkash K, Mittal H (2010) Software cost estimation using fuzzy logic. ACM SIGSOFT Softw Eng Notes 35(1):1–7

    Article  Google Scholar 

  45. Amazal FA, Idri A, Abran A (2014) Improving fuzzy analogy based software development effort estimation In: 21st Asia-Pacific Software Engineering Conference Vol 1, pp 247–254 pp 247–254

    Google Scholar 

  46. Du WL, Ho D, Capretz LF (2015) A Neuro-fuzzy model with SEER-SEM for software effort estimation. arXiv preprint arXiv:1508.00032

  47. Murillo-Morera J, Quesada-López C, Castro-Herrera C, Jenkins M (2017) A genetic algorithm based framework for software effort prediction. J Softw Eng Res Develop 5(1):1–33

    Article  Google Scholar 

  48. Benala TR, Mall R (2018) DABE: differential evolution in analogy-based software development effort estimation Swarm Evolutionary Computation Vol 38, pp 158–172

    Google Scholar 

  49. Wu D, Li J, Bao C (2018) Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation. Soft Comput 22(16):5299–5310

    Article  Google Scholar 

  50. Kumari S, Pushkar S (2018) Cuckoo search based hybrid models for improving the accuracy of software effort estimation. Microsyst Technol 24(12):4767–4774

    Article  Google Scholar 

  51. Puspaningrum A, Sarno R (2017) A hybrid cuckoo optimization and harmony search algorithm for software cost estimation. Procedia Comput Sci 124:461–469

    Article  Google Scholar 

  52. Nassif AB, Azzeh M, Idri A, Abran A (2019) Software development effort estimation using regression fuzzy models. Comput Intell Neurosci 2019

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Data Science Research Center, Shahrekord University, Shahrekord, Iran

    Maedeh Dashti

  2. Department of Computer Science, Shahrekord University, Shahrekord, Iran

    Taghi Javdani Gandomani

Authors
  1. Maedeh Dashti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taghi Javdani Gandomani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maedeh Dashti .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    Rishi Sayal

  3. Jawaharlal Nehru Technological University, Hyderabad, India

    A. Govardhan

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dashti, M., Gandomani, T.J. (2022). A Taxonomy of Approaches and Methods for Software Effort Estimation. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 385. Springer, Singapore. https://doi.org/10.1007/978-981-16-8987-1_11

Download citation

Publish with us

Policies and ethics

Search

Navigation