Advertisement

Sādhanā

, 44:94 | Cite as

Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

  • Sumera W AhmadEmail author
  • G R Bamnote
Article
  • 22 Downloads

Abstract

Software Cost Estimation (SCE) is the emerging concern of the software companies during the development phase of the software, as it requires effort and cost factors for modelling the software. These factors are modelled using the Artificial Intelligence models, which seem to be less accurate and non-reliable by increasing the risk factor of the software projects. Thus, for estimating the software cost, meta-heuristics are employed. This paper proposes an algorithm, termed as whale–crow optimization (WCO) algorithm, which is the integration of the whale optimization algorithm (WOA) and the crow search algorithm (CSA). The main function of the WCO algorithm is to determine the Optimal Regression coefficients for the regression models, such as the Linear Regression model and the Kernel Logistic Regression model, to develop an Optimal Regression model to estimate the software cost. The experimentation is carried out using four datasets taken from the Promise software engineering repository to perform effective performance analysis. Analysis is carried out regarding the mean magnitude of relative error (MMRE) that proves that the proposed method of SCE is effective, attaining the average MMRE at a rate of 0.2442 for the proposed Linear Regression model and 0.2692 for the proposed Kernel Regression model.

Keywords

Kernel Logistics Regression model Linear Regression model WOA CSA Software Cost Estimation 

References

  1. 1.
    Kaushik A, Soni A K and Soni R 2016 An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation. International Journal of System Assurance Engineering and Management 7: 50–61CrossRefGoogle Scholar
  2. 2.
    Mittas N, Papatheocharous E, Angelis L and Andreou A S 2015 Integrating non-parametric models with linear components for producing software cost estimations. Journal of Systems and Software 99: 120–134CrossRefGoogle Scholar
  3. 3.
    Kapoor D and Gupta R K 2016 Software cost estimation techniques – a review of literature. International Journal of Research and Development in Applied Science and Engineering 9Google Scholar
  4. 4.
    Hodgkinson A C and Garratt P W 1999 A neuro fuzzy cost estimator. In: Proceedings of the Third International Conference on Software Engineering and Applications-SAE, pp. 401–406Google Scholar
  5. 5.
    Borade J G 2013 Software project effort and cost estimation techniques. International Journal of Advanced Research in Computer Science and Software Engineering 3: 730–739Google Scholar
  6. 6.
    Rao K S and Reddy L S S 2013 Software cost estimation in multilayer feed forward network using random holdback method. International Journal of Advanced Research in Computer Science and Software Engineering 3: 1309–1328Google Scholar
  7. 7.
    Mukherjee S, Bhattacharya B and Mandal S 2013 A survey on metrics, models & tools of software cost estimation. International Journal of Advanced Research in Computer Engineering & Technology 2: 2620–2625Google Scholar
  8. 8.
    Ramasubbu N and Balan R K 2012 Overcoming the challenges in cost estimation for distributed software projects. In: Proceedings of the 34th International Conference on Software Engineering (ICSE), pp. 91–101Google Scholar
  9. 9.
    Srinivasan K and Fisher D 1995 Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering 21: 126–137CrossRefGoogle Scholar
  10. 10.
    Shivhare J and Rath S K 2014 Software effort estimation using machine learning techniques. In: Proceedings of the 7th India Software Engineering Conference, pp. 19–21Google Scholar
  11. 11.
    Araújo R A, Soares S and Oliveira A L I 2012 Hybrid morphological methodology for software development cost estimation. Expert Systems with Applications 39: 6129–6139CrossRefGoogle Scholar
  12. 12.
    Araújo R A, Oliveira A L I, Soares S and Meira S 2012 An evolutionary morphological approach for software development cost estimation. Neural Networks 32: 285–291CrossRefGoogle Scholar
  13. 13.
    Maleki I, Ghaffari A and Masdari M 2014 A new approach for software cost estimation with hybrid genetic algorithm and ant colony optimization. International Journal of Innovation and Applied Studies 5: 72–81Google Scholar
  14. 14.
    Sadiq M and Shahid M 2013 A systematic approach for the estimation of software risk and cost using esrcTool. CSI Transactions on ICT 1: 243–252CrossRefGoogle Scholar
  15. 15.
    Mittas N and Angelis L 2013 Ranking and clustering software cost estimation models through a multiple comparisons algorithm. IEEE Transactions on Software Engineering 39: 537–551CrossRefGoogle Scholar
  16. 16.
    Bishnu P S and Bhattacherjee V 2016 Software cost estimation based on modified K-modes clustering algorithm. Natural Computing 15: 415–422MathSciNetCrossRefGoogle Scholar
  17. 17.
    Roli F 2009 Multiple classifier systems. In: Encyclopedia of Biometrics, pp. 981–986Google Scholar
  18. 18.
    Sharma R 2013 Survey: Non algorithmic models for estimating software effort. European International Journal of Science and Technology 2: 164–169Google Scholar
  19. 19.
    Waghmode S and Kolhe K 2014 A novel way of cost estimation in software project development based on clustering techniques. International Journal of Innovative Research in Computer and Communication Engineering 2: 3892–3899Google Scholar
  20. 20.
    Mittas N and Angelis L 2008 Comparing cost prediction models by resampling techniques. Journal of Systems and Software 81: 616–632CrossRefGoogle Scholar
  21. 21.
    Stensrud E and Myrtveit I 1998 Human performance estimating with analogy and regression models: an empirical validation. In: Proceedings of the Fifth IEEE International Symposium on Software Metrics, pp. 205–213Google Scholar
  22. 22.
    Mogale D G, Lahoti G, Jha S B, Shukla M, Kamath N and Tiwari M K 2018 Dual market facility network design under bounded rationality. In: Proceedings of Algorithms 2018, 11: 54MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kitchenham B and Mendes E 2009 Why comparative effort prediction studies may be invalid. In: Proceedings of the Fifth ACM International Conference on Predictor Models in Software Engineering, pp. 1–5Google Scholar
  24. 24.
    Mogale D G, Kumar M, Kumar S K and Tiwari M K 2018 Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transportation Research Part E: Logistics and Transportation Review 111: 40–69CrossRefGoogle Scholar
  25. 25.
    Maiyar L M and Thakkar J J 2018 Modelling and analysis of inter-modal food grain transportation under hub disruption towards sustainability. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.07.021CrossRefGoogle Scholar
  26. 26.
    Mogale D G, Kumar S K and Tiwari M K 2018 An MINLP model to support the movement and storage decisions of the Indian food grain supply chain. Control Engineering Practice 70: 98–113CrossRefGoogle Scholar
  27. 27.
    Elbeltagi E, Hegazy T and Grierson D 2005 Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics 19: 43–53CrossRefGoogle Scholar
  28. 28.
    Dizaji Z A and Gharehchopogh F S 2015 A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation. Indian Journal of Science and Technology 8: 128–133CrossRefGoogle Scholar
  29. 29.
    Gharehchopogh F S, Maleki I and Talebi A 2015 Using hybrid model of artificial bee colony and genetic algorithms in software cost estimation. In: Proceedings of the 9th International Conference on Application of Information and Communication Technologies (AICT), Rostov on Don, RussiaGoogle Scholar
  30. 30.
    Schneider A, Hommel G and Blettner M 2010 Linear regression analysis. Evaluation of Scientific Publications, Part 14Google Scholar
  31. 31.
    Smola A and Sch¨olkopf B 2000 Sparse greedy matrix approximation for machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, USAGoogle Scholar
  32. 32.
    Askarzadeh A 2016 A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers and Structures 169: 1–12CrossRefGoogle Scholar
  33. 33.
    Mirjalili S and Lewis A 2016 The Whale Optimization Algorithm. Advances in Engineering Software 95: 51–67CrossRefGoogle Scholar
  34. 34.
    Promise software engineering repository 2017 “http://promise.site.uottawa.ca/SERepository/datasets-page.htmlc”, accessed in October.
  35. 35.
    Gharehchopogh F S, Ebrahimi L, Maleki I and Gourabi S J 2014 A novel PSO based approach with hybrid of fuzzy C-means and learning automata in software cost estimation. Indian Journal of Science and Technology 7: 795–803Google Scholar
  36. 36.
    Gharehchopogh F S, Rezaii R and Arasteh B 2015 A new approach by using Tabu search and genetic algorithms in software cost estimation. In: Proceedings of the 9th International Conference on Application of Information and Communication Technologies (AICT), Rostov on Don, RussiaGoogle Scholar
  37. 37.
    Maiyar L M and Thakkar J J 2017 A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach. Computers & Industrial Engineering 110: 30–42CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringProf. Ram Meghe Institute of Technology and ResearchBadnera, AmravatiIndia

Personalised recommendations