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Software Cost Estimation using Fuzzy Logic Technique


  • Department of Computer Engineering, Punjabi University, Patiala – 147002, Punjab,, India


Estimation of software development cost has been a challenging research area. Soft computing based techniques such as fuzzy logic outperform traditionally used methods in terms of accuracy of estimation. The current research presents a novel method that shows promising results. The results are compared with COCOMO technique and the accuracy level is improved considerably. The proposed method is simple yet effective as it implements the technique using MATLAB’s fuzzy logic toolbox.


COCOMO, Efforts, Fuzzy Logic, Software

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  • Boehm BW. Software Engineering Economics, PrenticeHall, Englewood Cliffs, NJ, 1994.
  • Zadeh LA. Fuzzy Sets, Information and Control. 1965; 8(3):338−53.
  • Mago, VK, Bhatia N, Bhatia A, Mago A. Clinical Decision Support System for Dental Treatment, Journal of Computational Science. 2012; 3(5):254−61.
  • Singh H, Singh G, Bhatia N. Election Results Prediction System based on Fuzzy Logic, International Journal of Computer Applications. 2012; 53(9).
  • Singh G, Bhatia N, Singh S. Fuzzy Logic Based Cricket Player Performance Evaluator, IJCA Special Issue on Artificial Intelligence Techniques-Novel Approaches and Practical Applications. 2011; 1(3):11−16.
  • Kumar S, Bhatia N, Kapoor N. Fuzzy logic based decision support system for loan risk assessment. In Proceedings of the International Conference on Advances in Computing and Artificial Intelligence, ACM. 2011, July; 179−82.
  • Du WL, Capretz LF, Nassif AB, Ho D. A Hybrid Intelligent Model for Software Cost Estimation, arXiv preprint arXiv:1512.00306.
  • Dizaji ZA, Gharehchopogh FS. A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation, Indian Journal of Science and Technology. 2015; 8(2):128.
  • Shivakumar N, Balaji N, Ananthakumar K. A Neuro Fuzzy Algorithm to Compute Software Effort Estimation, Global Journal of Computer Science and Technology. 2016; 16(1).
  • Sharma V, Verma HK. Optimized Fuzzy Logic Based Framework for Effort Estimation in Software Development, International Journal of Computer Science Issues. 2010; 7(2):30−38.
  • Bhatnagar R, Ghose MK. Comparing Soft Computing Techniques for Early Stage Software Development Effort Estimations, International Journal of Software Engineering and Application. 2012; 3(2):119−27.
  • Sehra SK, Brar YS, Kaur N. Soft Computing Techniques for Software Project Effort Estimation, International Journal of Advanced Computer and Mathematical Science. 2011; 2(3):160−67.
  • Kad S, Vinay C. Fuzzy Logic Based Framework for Software Development Effort Estimation, International Journal of Engineering Science. 2011; 1:3330−342.
  • Potdar SM, Puri M, Potdar MP. Literature Survey on Algorithmic Methods for Software Development Cost Estimation, International Journal of Computer Technology and Application. 2014; 5(1):183−88.
  • Reddy CS, Raju K. A Concise Neural Network Model for Estimating Software Effort, International Journal of Recent Trends in Engineering. 2009; 1(1):183−93.
  • Shradhanand AK, Satbir J. Use of Fuzzy Logic in Software Development, Issues in Information Systems. 2007; 8(2).
  • Kumar S, Chopra V. To Design and Implement Neural Network and Fuzzy Logic for Software Development Effort Prediction, International Journal of Computer Applications. 2013; 84(11).
  • Boetticher G, Menzies T, Ostrand T. PROMISE Repository of Empirical Software Engineering Data, West Virginia University, Department of Computer Science. 2007. http:// repository.
  • Dejaeger K, Verbeke W, Martens D, Baesens B. Data Mining Techniques for Software Effort Estimation: A Comparative Study, IEEE Transactions on Software Engineering. 2012; 38(2):375−97.
  • Oliveira AL, Braga PL, Lima RM, Cornélio ML. GA-Based Method for Feature Selection and Parameters Optimization for Machine Learning Regression Applied to Software Effort Estimation, Information and Software Technology.2010; 52(11):1155−66.
  • Azzeh M, Neagu D, Cowling PI. Fuzzy Grey Relational Analysis for Software Effort Estimation, Empirical Software Engineering. 2010; 15(1):60−90.
  • Bardsiri VK, Jawawi DNA, Hashim SZM, Khatibi E. A PSO-Based Model to Increase the Accuracy of Software Development Effort Estimation, Software Quality Journal.2013; 21(3):501−26.
  • Ochodek M, Nawrocki J, Kwarciak K. Simplifying Effort Estimation Based on Use Case Points, Information and Software Technology. 2011; 53(3):200−13.
  • Azzeh M. A Replicated Assessment and Comparison of Adaptation Techniques for Analogy-based Effort Estimation, Empirical Software Engineering. 2012; 17(12):90−127.
  • Corazza A, Di Martino S, Ferrucci F, Gravino C, Sarro F, and Mendes E. Using Tabu Search to Configure Support Vector Regression for Effort Estimation, Empirical Software Engineering. 2013; 18(3):506−46.
  • Song Q, Shepperd M. Predicting Software Project Effort: A Grey Relational Analysis Based Method, Expert Systems with Applications, 2011; 38(6):7302−16.
  • Idri A, Azzahra Amazal F, Abran A. Analogy-Based Software Development Effort Estimation: A Systematic Mapping and Review, Information and Software Technology. 2015; 58:206−23.


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