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Journal articles on the topic 'COCOMO MODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Chhabra, Sonia, and Harvir Singh. "Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm." International Journal of Computational Intelligence and Applications 19, no. 01 (2020): 2050005. http://dx.doi.org/10.1142/s1469026820500054.

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Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy

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Manas Prasad Rout. "Advanced Machine Learning Software Cost Prediction Model using AdaBoost and COCOMO Cost Parameters." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 1266–72. https://doi.org/10.52783/jisem.v10i49s.10128.

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Playing a pivotal role in software development, the Constructive Cost Model (COCOMO) offers a systematic and structured approach to cost estimation. It stands as a widely utilized model, aiding project managers in estimating the required effort, time, and cost for software development projects. COCOMO takes into consideration diverse factors, including the project's size, complexity, and the experience of the development team. The utilization of COCOMO empowers software development teams to make informed decisions related to resource allocation, project scheduling, and budgeting. Its applicati

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DANEVA, MAYA. "UNCERTAIN CONTEXT FACTORS IN ERP PROJECT ESTIMATION ARE AN ASSET: INSIGHTS FROM A SEMI-REPLICATION CASE STUDY IN A FINANCIAL SERVICES FIRM." International Journal of Software Engineering and Knowledge Engineering 21, no. 03 (2011): 389–411. http://dx.doi.org/10.1142/s0218194011005335.

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This paper reports on the findings of a case study in a company in the financial services sector in which we replicated the use of a previously published approach to systematically balance the contextual uncertainties in the estimation of Enterprise Resource Planning (ERP) projects. The approach is based on using three techniques, a parametric model, namely COCOMO II, a portfolio management model, and Monte Carlo simulations. We investigated (i) whether the adjustment of uncertain cost drivers in the COCOMO II model increases the chance of project success in a portfolio of ERP projects, (ii) w

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Brito, e. Abreu Fernando. "Modelo COCOMO: das origens à actualidade." Interface Abril 1998, no. 6 (1998): 10. https://doi.org/10.5281/zenodo.6989103.

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<em>No primeiro de dois artigos sobre o modelo de estimação COCOMO, analisam-se as motivações que levaram à sua proposta e apresentam-se os seus pressupostos, arquitectura global, equações básicas e também algumas das suas limitações. Descreve-se resumidamente o conjunto de passos necessários para conceber um modelo de estimação e dá-se conta de vários projectos patrocinados pela Comissão Europeia para suscitar a adopção mais generalizada deste tipo de modelos. S

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Khuat, Thanh Tung, and My Hanh Le. "A Novel Technique of Optimization for the COCOMO II Model Parameters using Teaching-Learning-Based Optimization Algorithm." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2016): 84–89. http://dx.doi.org/10.26636/jtit.2016.1.708.

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Software cost estimation is a critical activity in the development life cycle for controlling risks and planning project schedules. Accurate estimation of the cost before the start-up of a project is essential for both the developers and the customers. Therefore, many models were proposed to address this issue, in which COCOMO II has been being widely employed in actual software projects. Good estimation models, such as COCOMO II, can avoid insufficient resources being allocated to a project. However, parameters for estimation formula in this model have not been optimized yet, and so the estim

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Kholed, Langsari, Sarno Riyanarto, and Sholiq. "Optimizing Time and Effort Parameters of COCOMO II Using Fuzzy Multi-objective Particle Swarm Optimization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2199–207. https://doi.org/10.12928/TELKOMNIKA.v16i5.9698.

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Estimating the efforts, costs, and schedules of software projects is a frequent challenge to software development projects. A bad estimation will result in bad management of a project. Various models of estimation have been defined to complete this estimate. The Constructive Cost Model II (COCOMO II) is one of the most famous models as a model for estimating efforts, costs, and schedules. To estimate the effort, cost, and schedule in project of software, the COCOMO II uses inputs: Effort Multiplier (EM), Scale Factor (SF), and Source Line of Code (SLOC). Evidently, this model is still lack in

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Ramadhan, As'ary. "Estimasi Pada Effort Perangkat Lunak dengan Pendekatan Feed Forward Neural Network Backpropagation (FFNN-BP)." Technologia: Jurnal Ilmiah 12, no. 2 (2021): 89. http://dx.doi.org/10.31602/tji.v12i2.4576.

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Estimasi biaya pengembangan proyek perangkat lunak merupakan salah satu masalah yang kritis dalam rekayasa perangkat lunak. Kegagalan dari proyek perangkat lunak diakibatkan ketidak akuratannya estimasi sumber daya yang dibutuhkan. Beberapa model telah dikembangkan dalam beberapa puluh tahun belakangan ini. Untuk meberikan keakuratan dalam estimasi biaya proyek perangkat lunak masih menjadi tantangan hingga saat ini. Tujuan dilakukannya penelitian ini meningkatkan akurasi estimasi biaya proyek perangkat lunak dengan menerapkan algoritma genetika sebagai proses pelatihan pada Feed Forward Neura

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Kholed, Langsari, Sarno Riyanarto, and Sholiq. "Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2208–16. https://doi.org/10.12928/TELKOMNIKA.v16i5.9703.

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Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To

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Attarzadeh, Iman, and Siew Hock Ow. "Proposing an Effective Artificial Neural Network Architecture to Improve the Precision of Software Cost Estimation Model." International Journal of Software Engineering and Knowledge Engineering 24, no. 06 (2014): 935–53. http://dx.doi.org/10.1142/s0218194014500338.

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Software companies have to manage different software projects based on different time, cost, and manpower requirement, which is a very complex task in software project management. Accurate software estimates at the early phase of software development is one of the crucial objectives and a great challenge in software project management, in the last decades. Since software development attributes are vague and uncertain at the early phase of development, software estimates tend to a certain degree of estimation error. A software development cost estimation model incorporates soft computing techni

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Prabhanjali, B. "Optimizing Software Effort Estimation Models Using Enhancement of Firefly Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2270–77. http://dx.doi.org/10.22214/ijraset.2024.59309.

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Abstract: Estimating the amount of work required in order to create software development is regarded as essential to the software development life cycle and to the control of project costs, schedules, and quality. As a result, precise estimation plays a critical role in project success and risk mitigation. Software effort estimation has drawn a lot of interest from scholars recently and presented a problem to the software business. Three COCOMO-based models' parameters are to be optimized using a metaheuristic method called improvement of Firefly Algorithm, which is proposed in this research.

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Yang, Hai. "Improved Software Cost Estimation Method Based on COCOMO Model and Linear Regression." Advanced Materials Research 989-994 (July 2014): 1497–500. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1497.

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Software cost estimation is the key step to software development management. In order to make COCOMO model applicable to Chinese enterprises, an improved software cost estimation method based on COCOMO model and linear regression was proposed in this paper. Then the replication experiment was taken by using the historical software project data of given enterprises, and then compared experience estimation with the new improved method proposed in this paper about the forecasting accuracy. The results verified that the improved cost estimation method has more practical value to software developme

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Putri, Rahmi Rizkiana. "Peningkatan Akurasi Perkiraan Biaya dan Waktu Proyek Perangkat Lunak Berdasarkan Model Fuzzy Gaussian dan Perubahan Nilai Parameter." Jurnal IPTEK 22, no. 2 (2019): 67–76. http://dx.doi.org/10.31284/j.iptek.2018.v22i2.447.

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Perkiraan biaya dan waktu akan mempengaruhi manajemen proyek perangkat lunak. Penambahan cost driver yang diperkenalkan Barry Boehm pada tahun 2000 digunakan dalam penulisan ini guna memberikan hasil akurasi yang lebih baik karena telah mencakup keseluruhan bagian yang di estimasi. Namun jika hanya menggunakan metode COCOMO II hasil estimasi masih jauh dari Actual Effort. Oleh sebab itu peningkatan akurasi hasil COCOMO II dapat menggunakan metode Fuzzy Gaussian yang memberikan hasil estimasi lebih baik dilihat dari hasil MMRE. Tidak hanya menggunakan metode tersebut, tetapi juga mengubah nilai

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Li, Zhen You. "Predicting Project Effort Intelligently in early Stages by Applying Genetic Algorithms with Neural Networks." Applied Mechanics and Materials 513-517 (February 2014): 2035–40. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2035.

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In the early stages of a software development project, estimating the amount of effort is one of the most important project management concerns. This study has successfully produced global optimal reduced models intelligently predicting software cost estimation by employing neural networks with back-propagation learning algorithms combined with genetic algorithms (GA-NN) to determine the most significant explanatory variables among the 16 COCOMO cost drivers. The performance of the full model of GA-NN is much superior to that of the COCOMO, whilst the predicting performance of its global optim

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Marrwa, Abd-AlKareem Alabajee, Akram AlSaati Najla, and Riyadh Alreffaee Taghreed. "Parameter tuning of software effort estimation models using antlion optimization." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 817–28. https://doi.org/10.12928/telkomnika.v19i3.16907.

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In this work, the antlion optimization (ALO) is employed due to its efficiency and wide applicability to estimate the parameters of four modified models of the basic constructive cost model (COCOMO) model. Three tests are carried out to show the effectiveness of ALO: first, it is used with Bailey and Basili dataset for the basic COCOMO Model and Sheta’s Model 1 and 2, and is compared with the firefly algorithm (FA), genetic algorithms (GA), and particle swarm optimization (PSO). Second, parameters of Sheta’s Model 1 and 2, Uysal’s Model 1 and 2 are optimized using Bailey and

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Siti Fatonah, Onah, and Yasmi Afrizal. "MODEL ESTIMASI BIAYA PERANGKAT LUNAK MENGGUNAKAN COCOMO II (STUDI KASUS PT. X)." Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi 2, no. 2 (2016): 54–61. https://doi.org/10.34010/jtk3ti.v2i1.302.

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Estimasi pembangunan perangkat lunak adalah proses memperkirakan upaya untuk pengembangan perangkat lunak. Upaya tersebut meliputi usaha (orang/bulan), jadwal (bulan), dan biaya pembangunan perangkat lunak. Metode estimasi biaya yang digunakan dalam penelitian ini yaitu COCOMO II yang merupakan sebuah metode perkiraan biaya yang obyektif untuk perencanaan dan pelaksanaan proyek- proyek perangkat lunak. Penelitian ini menggunakan 10 data set dari proyek TI di PT. X dan bertujuan untuk membuat suatu model perkiraan biaya perangkat lunak menggunakan COCOMO II serta mengetahui tingkat keberhasilan

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Naveen, Malik, Kumar Goyal Sandip, and Malik Vinisha. "Software Quality Assesment using COCOMO-II Metrics with ABC and NN." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2020): 2982–88. https://doi.org/10.35940/ijeat.C5633.029320.

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Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and

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Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.

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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for so

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Majid, Ahadi1 Ahmad Jafarian2. "A NEW HYBRID FOR SOFTWARE COST ESTIMATION USING PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION ALGORITHMS." Informatics Engineering, an International Journal (IEIJ) 04, mar (2016): 01–11. https://doi.org/10.5121/ieij.2016.4106.

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Software Cost Estimation (SCE) is considered one of the most important sections in software engineering that results in capabilities and well-deserved influence on the processes of cost and effort. Two factors of cost and effort in software projects determine the success and failure of projects. The project that will be completed in a certain time and manpower is a successful one and will have good profit to project managers. In most of the SCE techniques, algorithmic models such as COCOMO algorithm models have been used. COCOMO model is not capable of estimating the close approximations to th

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Sachan, Rohit Kumar, Ayush Nigam, Avinash Singh, et al. "Optimizing Basic COCOMO Model Using Simplified Genetic Algorithm." Procedia Computer Science 89 (2016): 492–98. http://dx.doi.org/10.1016/j.procs.2016.06.107.

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Khin, Htay, Mie Aung Mie, Yin Cho Yin, and Moe Thein Moe. "Software Engineering Cost Estimation using COCOMO II Model." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2326–29. https://doi.org/10.5281/zenodo.3591420.

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Estimating the software cost and price to customer is vital role for software engineering. Accurate Software development cost estimation is important for effective project management such as budgeting project planning and control. Before software development processes begin, software cost and duration for any project should be agreement between developers and customers.In this paper, the effort required to develop the system, schedule needed to complete and the required average staff are estimated by using COCOMO constructive cost model model that is one of the algorithmic models. A mathematic

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ul Hassan, Ch Anwar, Muhammad Sufyan Khan, Rizwana Irfan, et al. "Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach." Computational Intelligence and Neuroscience 2022 (October 4, 2022): 1–20. http://dx.doi.org/10.1155/2022/3145956.

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Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine

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Mukunga, Catherine Wambui, John Gichuki Ndia, and Geoffrey Mariga Wambugu. "A METRICS -BASED MODEL FOR ESTIMATING THE MAINTENANCE EFFORT OF PYTHON SOFTWARE." International Journal of Software Engineering & Applications 14, no. 3 (2023): 15–29. http://dx.doi.org/10.5121/ijsea.2023.14302.

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Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A wi

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Catherine, Wambui Mukunga, Gichuki Ndia John, and Mariga Wambugu Geoffrey. "A Metrics-Based Model for Estimating the Maintenance Effort of Python Software." International Journal of Software Engineering & Applications (IJSEA) 14, no. 3 (2023): 15–29. https://doi.org/10.5281/zenodo.8189216.

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Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A wi

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Huang, Xishi, Danny Ho, Jing Ren, and Luiz F. Capretz. "Improving the COCOMO model using a neuro-fuzzy approach." Applied Soft Computing 7, no. 1 (2007): 29–40. http://dx.doi.org/10.1016/j.asoc.2005.06.007.

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Kaur, Ishleen, Gagandeep Singh Narula, Ritika Wason, Vishal Jain, and Anupam Baliyan. "Neuro fuzzy—COCOMO II model for software cost estimation." International Journal of Information Technology 10, no. 2 (2018): 181–87. http://dx.doi.org/10.1007/s41870-018-0083-6.

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Puspaningrum, Alifia, Fachrul Pralienka Bani Muhammad, and Esti Mulyani. "Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy." JUITA: Jurnal Informatika 9, no. 2 (2021): 139. http://dx.doi.org/10.30595/juita.v9i2.10511.

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Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value. In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is propo

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Puspaningrum, Alifia, Muhamad Mustamiin, Fauziah Herdiyanti, and Kamaludin Noviyanto. "Software Effort Coefficient Optimization Using Hybrid Bat Algorithm and Whale Optimization Algorithm." JURNAL INFOTEL 17, no. 1 (2025): 122–35. https://doi.org/10.20895/infotel.v17i1.1250.

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Software effort estimation is a crucial aspect in software engineering, especially in project management. It defines an effort required by a person to develop an application in certain of time. One of models which widely used for this purpose is Constructive Cost Model (COCOMO) II. In COCOMO II, two coefficients have a significant role in determining the accuracy of the effort estimation. Various methods have been conducted to estimate these coefficients to closely match the actual effort with the predicted values, such as particle swarm optimization, cuckoo search algorithm, etc. However, sev

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Wakurdekar, Prof Sachin Baburao, Prof Dr S. B. Vanjale, Dr Pallavi Deshpande, et al. "Software Cost Estimation: A Comparative Analysis Of Traditional And Machine Learning Approaches." International Journal of Environmental Sciences 11, no. 7s (2025): 710–20. https://doi.org/10.64252/pn35t491.

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Estimating software costs is essential to project management because it helps businesses allocate resources efficiently. Despite their widespread use, traditional models like COCOMO frequently have drawbacks because they rely on assumptions and predefined parameters that are not well suited to contemporary software development techniques. Machine learning-based models, on the other hand, provide a data-driven strategy by utilizing past project data to increase estimation accuracy. This study compares and contrasts contemporary machine learning methods with conventional cost estimation models.

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Goyal, Somya, and ANUBHA Parashar. "Machine Learning Application to Improve COCOMO Model using Neural Networks." International Journal of Information Technology and Computer Science 10, no. 3 (2018): 35–51. http://dx.doi.org/10.5815/ijitcs.2018.03.05.

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Khatoon, Arfiha, and Rupinder Kaur. "Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm." International Journal of Computer Sciences and Engineering 6, no. 5 (2018): 221–26. http://dx.doi.org/10.26438/ijcse/v6i5.221226.

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Zakaria, Noor Azura, Amelia Ritahani Ismail, Nadzurah Zainal Abidin, Nur Hidayah Mohd Khalid, and Afrujaan Yakath Ali. "Optimized COCOMO parameters using hybrid particle swarm optimization." International Journal of Advances in Intelligent Informatics 7, no. 2 (2021): 177. http://dx.doi.org/10.26555/ijain.v7i2.583.

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Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation

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Gonsalves, Tad, and Kiyoshi Itoh. "IMPROVING THE PREDICTION ACCURACY OF SOFTWARE DEVELOPMENT COST MODELS." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 13, no. 3 (2009): 39–47. http://dx.doi.org/10.3233/jid-2009-13303.

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Software development projects are notorious for being completed behind schedule and over budget and for often failing to meet user requirements. A variety of cost estimation models have been proposed to predict development costs early in the lifecycle with the hope of managing the project well within time and budget. However, studies have reported rather high error rates of prediction even in the case of the well-established and widely acknowledged models. This study focuses on the improvement and fine-tuning of the COCOMO 81 model through the application of the recently developed Swarm Intell

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Sun, Yu Qiang, Mei Weng, Cheng Xian Shi, Jing You, and Qi Wei He. "A Validity Analysis of Estimate Improvement Model of Software Cost." Advanced Materials Research 317-319 (August 2011): 1725–28. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1725.

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In virtue of fuzzy matrix and fuzzy linear transformation, not only the fuzzy comprehensive ability’s quantitative value is got, but also single fuzzy ability’s quantitative value is got. The type of software personnel can be distinguished by comparing the fuzzy integrated ability assessing value of software personnel with pre-set threshold, pointed out statistic improvement for project in the process of confirming factors, in order to enhancing the accuracy of the estimate results. And an analysis of specific application instance of improved COCOMO model verified the validity of the improved

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Suharso, Wildan. "Penerapan Scrum dan Algoritma COCOMO Pada Aplikasi Manajemen Proyek Perangkat Lunak." SATIN - Sains dan Teknologi Informasi 4, no. 1 (2018): 97. http://dx.doi.org/10.33372/stn.v4i1.300.

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Karakteristik manajemen proyek perangkat lunak adalah biaya yang mahal, waktu terbatas dan minimnya transparansi pada semua pemangku kepentingan, Perencanaan yang baik perlu diimbangi dengan pelaksanaan yang sesuai karena banyak dari proyek yang gagal karena kurangnya perencanaan. Metode Scrum dapat membantu semua pemangku kepentingan dalam memahami proyek sehingga hasil dari proyek sesuai dengan perencanaan. Pada penelitian ini dilakukan penerapan metode scrum pada aplikasi manajemen proyek perangkat lunak untuk membantu tim dalam memahami proyek. Pada penelitian ini scrum tidak hanya sebagai

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Al-Din Sayed Majeed, Jamal, and Isra Majeed Qabaa. "Estimate Programmatic Effort using the Traditional COCOMO Model and Neural Networks." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 1 (2013): 351–64. http://dx.doi.org/10.33899/csmj.2013.163464.

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Nandal, Deepak, and Om Sangwan. "Software Cost Estimation by Optimizing COCOMO Model Using Hybrid BATGSA Algorithm." International Journal of Intelligent Engineering and Systems 11, no. 4 (2018): 250–63. http://dx.doi.org/10.22266/ijies2018.0831.25.

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Rashid, Chaudhry Hamza, Imran Shafi, Bilal Hassan Ahmed Khattak, Mejdl Safran, Sultan Alfarhood, and Imran Ashraf. "ANN-based software cost estimation with input from COCOMO: CANN model." Alexandria Engineering Journal 113 (February 2025): 681–94. https://doi.org/10.1016/j.aej.2024.11.042.

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Meinke, Matthias, Matthias S. Müller, Michael Schlottke Lakemper, Sandra Wienke, and Julian Miller. "Applicability of the software cost model COCOMO II to HPC projects." International Journal of Computational Science and Engineering 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijcse.2017.10011355.

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Miller, Julian, Sandra Wienke, Michael Schlottke Lakemper, Matthias Meinke, and Matthias S. Müller. "Applicability of the software cost model COCOMO II to HPC projects." International Journal of Computational Science and Engineering 17, no. 3 (2018): 283. http://dx.doi.org/10.1504/ijcse.2018.095849.

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Chhabra, Sonia, and Harvir Singh. "Optimizing design parameters of fuzzy model based COCOMO using genetic algorithms." International Journal of Information Technology 12, no. 4 (2019): 1259–69. http://dx.doi.org/10.1007/s41870-019-00325-7.

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Sun, Yu Qiang, Xue Li Tao, Cong Pin Zhang, and Xiao Lin Zhang. "The Estimate Model Improvement of Software Cost Based on Fuzzy Matrix Technology." Advanced Materials Research 186 (January 2011): 327–31. http://dx.doi.org/10.4028/www.scientific.net/amr.186.327.

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Combining the current situation of China's domestic software enterprises, the cost driving factors of COCOMO model is improved to enhance the the accuracy of the estimate results by the fuzzy matrix method in this paper. In virtue of fuzzy matrix and fuzzy linear transformation, not only the fuzzy comprehensive ablity’s quantitative value is got, but also sigle fuzzy ability’s quantitative value is got. The type of software personnel can be distinguished by comparing the fuzzy integrated ability assessing valur of software personnel with pre-set threshold, enhancing the the accuracy of the est

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Yang, Hai. "Research on Improved Staged Software Cost Estimation Method Based on COCOMO Model." Advanced Materials Research 989-994 (July 2014): 1501–4. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1501.

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The accuracy of software cost estimation is essential for software development management. By introducing and analyzing the estimation methods of software cost systematically, the paper discussed the necessary of considering the software maintenance stage and estimating the software cost by separating the procedure of software development into several small stages. Then a staged software cost estimation method based on COCOMO model was proposed. The use of the new software cost estimation method proposed by this paper not only contributes to the cost control of software project, but also effec

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Yasmeen, Safia, and G.Manoj Someswar. "Evaluation of Calibration Techniques to Build Software Cost Estimation Models." COMPUSOFT: An International Journal of Advanced Computer Technology 05, no. 08 (2016): 2223–26. https://doi.org/10.5281/zenodo.14800007.

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This research paper describes three calibration techniques, namely ordinary least squares regression, Bayesian analysis, and constrained regression technique, which are applied to calibrating the cost drivers of the model. Ordinary least squares (OLS) regression is the most popular technique used to build software cost estimation models. In COCOMO, the OLS is used for many purposes, such as analyzing the correlation between cost drivers and the effort and generating coefficients and their variances during the Bayesian analysis. 

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Singh, Brajesh Kumar, Shailesh Tiwari, K. K. Mishra, and A. K. Misra. "Tuning of Cost Drivers by Significance Occurrences and Their Calibration with Novel Software Effort Estimation Method." Advances in Software Engineering 2013 (December 31, 2013): 1–10. http://dx.doi.org/10.1155/2013/351913.

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Estimation is an important part of software engineering projects, and the ability to produce accurate effort estimates has an impact on key economic processes, including budgeting and bid proposals and deciding the execution boundaries of the project. Work in this paper explores the interrelationship among different dimensions of software projects, namely, project size, effort, and effort influencing factors. The study aims at providing better effort estimate on the parameters of modified COCOMO along with the detailed use of binary genetic algorithm as a novel optimization algorithm. Signific

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Zhang, Tong, Min Fang Zhang, Hua Zhang, and Yu Qing Hu. "Researches on Software Cost Combined Estimation Based on RBF Neural Network and RVM." Applied Mechanics and Materials 610 (August 2014): 325–31. http://dx.doi.org/10.4028/www.scientific.net/amm.610.325.

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Under the precondition of relatively adequate historical sample data of obtainable software cost, the thesis makes comprehensive analysis of the advantages and disadvantages of complementary neural network and vector machines, and attempts to study the software cost combined estimation based on RBF neural network and RVM and to build combined estimation model, then applies the entropy evaluation method to identify the weight coefficient of this combined estimation model, and finally it adopts the data from COCOMO database to verify this combined estimation as well as the rationality and scient

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Jabar, H. Yousif, and k. Saini Dinesh. "Fuzzy and Mathematical Effort Estimation Models for Web Applications Development." Applied Computing Journal 1, no. 1 (2021): 10–24. https://doi.org/10.5281/zenodo.4477174.

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This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. This paper presented the effort estimation model that involves two levels—the first level estimates by Project Managers, and the second level estimates by Project Leaders or Developers. The model considers the classification of each task as either Low or Medium or High complexity. Efforts are estimated to design, code, and test tasks and take a new requirement as a case study, compared with the practical efforts model using historical data for the existing fu

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Khan, Junaid Ali, Saif Ur Rehman Khan, Tamim Ahmed Khan, and Inayat Ur Rehman Khan. "An Amplified COCOMO-II Based Cost Estimation Model in Global Software Development Context." IEEE Access 9 (2021): 88602–20. http://dx.doi.org/10.1109/access.2021.3089870.

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Sheta, Alaa F. "Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects." Journal of Computer Science 2, no. 2 (2006): 118–23. http://dx.doi.org/10.3844/jcssp.2006.118.123.

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Yadav, Rahul Kumar. "OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC." International Journal of Advanced Research in Computer Science 8, no. 7 (2017): 121–25. http://dx.doi.org/10.26483/ijarcs.v8i7.4113.

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Idri, A., B. Griech, and A. El Iraki. "Towards an adaptation of the COCOMO cost model to the software measurement theory." ACM SIGSOFT Software Engineering Notes 22, no. 6 (1997): 525–26. http://dx.doi.org/10.1145/267896.267932.

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