{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:49:24Z","timestamp":1777697364654,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2021,1,6]]},"abstract":"<jats:p>Estimation of software cost (ESC) is considered a crucial task in the software management life cycle as well as time and quality. Prior to the development of a software project, precise estimations are required in the form of person month and time. In the last few decades, various parametric and non-algorithmic or non-parametric approaches regarding the estimation of software costs have been developed. Among them, the constrictive cost model (COCOMO-II) is a commonly used method for estimating software cost. To further improve the accuracy of this model, researchers and practitioners have applied numerous computational intelligence algorithms to optimize their parameters. However, accuracy is still a big problem in this model to be addressed. In this paper, we proposed a biogeography-based optimization (BBO) method to optimize the current coefficients of COCOMO-II for better estimation of software project cost or effort. The experiments are conducted on two standard data sets: NASA-93 and Turkish Industry software projects. The performance of the proposed algorithm called BBO-COCOMO-II is evaluated by using performance indicators including the manhattan distance (MD) and the mean magnitude of relative error (MMRE). Simulation results reveal that the proposed algorithm obtained high accuracy and significant error minimization compared to original COCOMO-II, particle swarm optimization, genetic algorithm, flower pollination algorithm, and other various baseline cost estimation models.<\/jats:p>","DOI":"10.3233\/idt-200103","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T12:38:23Z","timestamp":1608035903000},"page":"441-448","source":"Crossref","is-referenced-by-count":7,"title":["Optimization of software cost estimation model based on biogeography-based optimization algorithm"],"prefix":"10.1177","volume":"14","author":[{"given":"Aman","family":"Ullah","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfang","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Asim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Hunan, China"},{"name":"School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/IDT-200103_ref1","doi-asserted-by":"crossref","first-page":"250","DOI":"10.22266\/ijies2018.0831.25","article-title":"Software cost estimation by optimizing cocomo model using hybrid batgsa algorithm","volume":"11","author":"Nandal","year":"2018","journal-title":"International Journal of Intelligent Engineering and Systems"},{"key":"10.3233\/IDT-200103_ref2","doi-asserted-by":"crossref","unstructured":"Langsari K, Sarno R. Optimizing COCOMO II parameters using particle swarm method. In: 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE; 2017. pp. 29\u201334.","DOI":"10.1109\/ICSITech.2017.8257081"},{"key":"10.3233\/IDT-200103_ref3","doi-asserted-by":"crossref","unstructured":"Ullah A, Wang B, Sheng J, Long J, Asim M, Riaz F. A Novel Technique of Software Cost Estimation Using Flower Pollination Algorithm. In: 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE; 2019. pp. 654\u2013658.","DOI":"10.1109\/ICICAS48597.2019.00142"},{"key":"10.3233\/IDT-200103_ref5","unstructured":"Salam A, Khan A, Baseer S. A comparative study for software cost estimation using COCOMO-II and Walston-Felix models. In: The 1st International Conference on Innovations in Computer Science & Software Engineering, (ICONICS 2016). 2016. pp. 15\u201316."},{"key":"10.3233\/IDT-200103_ref6","doi-asserted-by":"crossref","unstructured":"Zhang W, Yang Y, Wang Q. A study on software effort prediction using machine learning techniques. In: International Conference on Evaluation of Novel Approaches to Software Engineering. Springer; 2011. pp. 1\u201315.","DOI":"10.1007\/978-3-642-32341-6_1"},{"key":"10.3233\/IDT-200103_ref7","doi-asserted-by":"crossref","unstructured":"Grover M, Bhatia PK, Mittal H. Estimating Software Test Effort Based on Revised UCP Model Using Fuzzy Technique. In: International Conference on Information and Communication Technology for Intelligent Systems. Springer; 2017. pp.\u00a0490\u2013498.","DOI":"10.1007\/978-3-319-63673-3_59"},{"key":"10.3233\/IDT-200103_ref8","doi-asserted-by":"crossref","unstructured":"Kumari S, Pushkar S. Software cost estimation using cuckoo search. In: Advances in Computational Intelligence. Springer; 2017. pp. 167\u2013175.","DOI":"10.1007\/978-981-10-2525-9_17"},{"issue":"12","key":"10.3233\/IDT-200103_ref9","doi-asserted-by":"crossref","first-page":"4767","DOI":"10.1007\/s00542-018-3871-9","article-title":"Cuckoo search based hybrid models for improving the accuracy of software effort estimation","volume":"24","author":"Kumari","year":"2018","journal-title":"Microsystem Technologies"},{"key":"10.3233\/IDT-200103_ref10","doi-asserted-by":"crossref","unstructured":"Gharehchopogh FS, Rezaii R, Arasteh B. A new approach by using Tabu search and genetic algorithms in Software Cost estimation. In: 2015 9th International Conference on Application of Information and Communication Technologies (AICT). IEEE; 2015. pp. 113\u2013117.","DOI":"10.1109\/ICAICT.2015.7338528"},{"key":"10.3233\/IDT-200103_ref11","doi-asserted-by":"crossref","unstructured":"Urbanek T, Prokopova Z, Silhavy R, Kuncar A. Using analytical programming for software effort estimation. In: Computer Science Online Conference. Springer; 2016. pp. 261\u2013272.","DOI":"10.1007\/978-3-319-33622-0_24"},{"key":"10.3233\/IDT-200103_ref12","doi-asserted-by":"crossref","unstructured":"Dalal S, Dahiya N, Jaglan V. Efficient Tuning of COCOMO Model Cost Drivers Through Generalized Reduced Gradient (GRG) Nonlinear Optimization with Best-Fit Analysis. In: Progress in Advanced Computing and Intelligent Engineering. Springer; 2018. pp. 347\u2013354.","DOI":"10.1007\/978-981-10-6872-0_32"},{"issue":"7","key":"10.3233\/IDT-200103_ref13","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1109\/TLA.2018.8447378","article-title":"Safety critical software effort estimation using COCOMO II: a case study in aeronautical industry","volume":"16","author":"Santos","year":"2018","journal-title":"IEEE Latin America Transactions"},{"key":"10.3233\/IDT-200103_ref15","doi-asserted-by":"crossref","unstructured":"Attarzadeh I, Mehranzadeh A, Barati A. Proposing an enhanced artificial neural network prediction model to improve the accuracy in software effort estimation. In: 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks. IEEE; 2012. pp. 167\u2013172.","DOI":"10.1109\/CICSyN.2012.39"},{"key":"10.3233\/IDT-200103_ref16","first-page":"64","article-title":"Estimating software project effort using analogies","volume":"16","author":"Shepperd","year":"2005","journal-title":"Series on Software Engineering and Knowledge Engineering"},{"key":"10.3233\/IDT-200103_ref17","unstructured":"Effendi YA, Sarno R, Prasetyo J. Implementation of Bat Algorithm for COCOMO II Optimization. In: 2018 International Seminar on Application for Technology of Information and Communication. IEEE; 2018. pp. 441\u2013446."},{"issue":"4","key":"10.3233\/IDT-200103_ref18","first-page":"94","article-title":"Whale-crow optimization (WCO)-based optimal regression model for software cost estimation","volume":"44","author":"Ahmad","year":"2019","journal-title":"S\u00e4dhan\u00e4"},{"key":"10.3233\/IDT-200103_ref19","doi-asserted-by":"crossref","unstructured":"Sheta A, Rine D, Ayesh A. Development of software effort and schedule estimation models using soft computing techniques. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE; 2008. pp. 1283\u20131289.","DOI":"10.1109\/CEC.2008.4630961"},{"issue":"1","key":"10.3233\/IDT-200103_ref21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/BF02249046","article-title":"Cost models for future software life cycle processes: COCOMO 2.0","volume":"1","author":"Boehm","year":"1995","journal-title":"Annals of Software Engineering"},{"key":"10.3233\/IDT-200103_ref22","unstructured":"Srivastava DK, Chauhan D, Singh R. VRS model: a model for estimation of efforts and time duration in development of IVR software system. Int J of Software Engineering, IJSE. 2012; 5(1)."},{"key":"10.3233\/IDT-200103_ref23","unstructured":"Chulani S. Bayesian analysis of software cost and quality models. In: Proceedings IEEE International Conference on Software Maintenance. ICSM 2001. IEEE; 2001. pp. 565\u2013568."},{"issue":"10","key":"10.3233\/IDT-200103_ref24","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1109\/32.6191","article-title":"Understanding and controlling software costs","volume":"14","author":"Boehm","year":"1988","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10.3233\/IDT-200103_ref25","doi-asserted-by":"crossref","unstructured":"Rarick R, Simon D, Villaseca FE, Vyakaranam B. Biogeography-based optimization and the solution of the power fl w problem. In: 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE; 2009. pp. 1003\u20131008.","DOI":"10.1109\/ICSMC.2009.5346046"},{"key":"10.3233\/IDT-200103_ref26","doi-asserted-by":"crossref","unstructured":"Yahya MA, Ahmad R, Lee SP. Effects of software process maturity on COCOMO IIs effort estimation from CMMI perspective. In: 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies. IEEE; 2008. pp. 255\u2013262.","DOI":"10.1109\/RIVF.2008.4586364"},{"issue":"1","key":"10.3233\/IDT-200103_ref29","first-page":"134","article-title":"Hybrid biogeography based optimization algorithm for optimization problems","volume":"33","author":"Alam","year":"2017","journal-title":"Gomal University Journal of Research"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-200103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:22:49Z","timestamp":1777454569000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-200103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":24,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/idt-200103","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,6]]}}}