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Journal articles on the topic 'COCOMO MODEL'

Author: Grafiati

Published: 11 September 2023

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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 (March 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 logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm.

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2

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 (May 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) which cost drivers of the COCOMO II model can be adjusted in a way that maximized the chance of portfolio success under time constraints, and (iii) which cost drivers of the COCOMO II model can be adjusted in a way that maximized the chance of portfolio success under effort constraints. We found that 11 COCOMO II cost drivers can be changed so that the change impacts the project outcomes under both time and effort constraints. This result is different from the result in the first case study in which 13 such factors were found.

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3

Ramadhan, As'ary. "Estimasi Pada Effort Perangkat Lunak dengan Pendekatan Feed Forward Neural Network Backpropagation (FFNN-BP)." Technologia: Jurnal Ilmiah 12, no. 2 (April 10, 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 Neural Network Backpropagation (FFNN-BP) yang mengakomodasi formula dari Post Architecture Model (COCOMO II). Magnitude of Relative Error (MRE) dan Mean Magnitude of Relative-Error (MMRE) digunakan sebagai pengkuran indikasi kinerja. Hasil percobaan menunjukkan bahwa model yang diusulkan memberikan hasil estimasi biaya proyek perangkat lunak menjadi lebih akurat dari COCOMO II dan FFNN-BP. Dalam kasus ini MMRE untuk COCOMO II adalah 74.68%, FFNN-BP adalah 39.90% . Kata kunci: COCOMO II, Machine Learning, Proyek Manajemen IT, Backpropagation

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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 (August 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 techniques provides a solution to fit the vagueness and uncertainty of software attributes. In this paper, an adaptive artificial neural network (ANN) architecture for Constructive Cost Model (COCOMO) is proposed in order to produce accurate software estimates. The ANN is utilized to determine the importance of calibration of the software attributes using past project data in order to produce accurate software estimates. Software project data from the COCOMO I and NASA'93 data sets were used in the evaluation of the proposed model. The result shows an improvement in estimation accuracy of 8.36% of the ANN-COCOMO II when compared with the original COCOMO II.

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5

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 development.

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6

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 (February 11, 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 parameter COCOMO II secara menurun dengan nilai gradual 0,0001 untuk mencapai nilai optimal. Berdasarkan hasil implementasi metode yang diusulkan disini kesalahan akurasi perkiraan biaya dapat turun 30% dan kesalahan akurasi perkiraan waktu proyek perangkat lunak dapat turun 1,19% bila dibandingkan penelitian sebelumnya. Jadi keakuratan biaya dan waktu dalam penelitian ini dapat meningkat.

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7

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 optimal reduced model is also comparable to that of the COCOMO in terms of MMRE and PRED (25). The optimal reduced models and their found significant factors can offer powerful supports for the project managers to make right decisions in the early stages of the projects.

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8

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 (January 31, 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 software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

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9

Sachan, Rohit Kumar, Ayush Nigam, Avinash Singh, Sharad Singh, Manjeet Choudhary, Avinash Tiwari, and Dharmender Singh Kushwaha. "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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10

ul Hassan, Ch Anwar, Muhammad Sufyan Khan, Rizwana Irfan, Jawaid Iqbal, Saddam Hussain, Syed Sajid Ullah, Roobaea Alroobaea, and Fazlullah Umar. "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-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

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11

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 (May 26, 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 within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

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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 (January 2007): 29–40. http://dx.doi.org/10.1016/j.asoc.2005.06.007.

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13

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 (January 18, 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 (November 30, 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 proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

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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 (March 8, 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 (May 31, 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 (April 24, 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, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

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18

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 model.

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19

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 (March 15, 2013): 351–64. http://dx.doi.org/10.33899/csmj.2013.163464.

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20

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 (August 31, 2018): 250–63. http://dx.doi.org/10.22266/ijies2018.0831.25.

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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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23

Chhabra, Sonia, and Harvir Singh. "Optimizing design parameters of fuzzy model based COCOMO using genetic algorithms." International Journal of Information Technology 12, no. 4 (July 13, 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 estimate results.

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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 (June 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 model untuk pengembangan perangkat lunak tetapi diimplementasikan pada aplikasi sehingga manajer proyek, master scrum dan tim pengembang dapat secara mudah mengontrol task. Algoritma CoCoMo digunakan untuk mengestimasi biaya proyek, Pengujian menggunakan pengujian fungsional dan skenario yang menjelaskan permasalahan. Hasil yang diperoleh adalah aplikasi dapat membantu manajer proyek, master scrum, dan tim pengembang dalam hal manajemen proyek perangkat lunak. Hasil pengujian estimasi biaya yang dilakukan menunjukkan nilai estimasi tidak melebihi 7% dari nilai real dan estimasi waktu real lebih efisien 40% dari waktu CoCoMo.

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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 effectively avoids the bias problem due to using by single cost estimation method so that the accuracy of cost estimation could be improved.

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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. Significance of 15 cost drivers can be shown by their impact on MMRE of efforts on original 63 NASA datasets. Proposed method is producing tuned values of the cost drivers, which are effective enough to improve the productivity of the projects. Prediction at different levels of MRE for each project reflects the percentage of projects with desired accuracy. Furthermore, this model is validated on two different datasets which represents better estimation accuracy as compared to the COCOMO 81 based NASA 63 and NASA 93 datasets.

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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 scientificity of this model.

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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 (February 1, 2006): 118–23. http://dx.doi.org/10.3844/jcssp.2006.118.123.

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31

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 (August 20, 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 (November 1997): 525–26. http://dx.doi.org/10.1145/267896.267932.

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., Ritu, Kamna Solanki, Amita Dhankhar, and Sandeep Dalal. "An Analysis of Software Reliability Estimation Using Fuzzy Logic Function With Cocomo Ii Model." International Journal of Computer Sciences and Engineering 7, no. 6 (June 30, 2019): 623–26. http://dx.doi.org/10.26438/ijcse/v7i6.623626.

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Razmi, Jafar, Reza Ghodsi, and Marzieh Jokar. "Cost estimation of software development: improving the COCOMO model using a genetic algorithm approach." International Journal of Management Practice 3, no. 4 (2009): 346. http://dx.doi.org/10.1504/ijmp.2009.026961.

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Dillibabu, R., and K. Krishnaiah. "Cost estimation of a software product using COCOMO II.2000 model – a case study." International Journal of Project Management 23, no. 4 (May 2005): 297–307. http://dx.doi.org/10.1016/j.ijproman.2004.11.003.

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Gu, Xungang, Gang Li, Shengli Cao, Yumeng Zhang, and Ran Wang. "Analysis of the cost factors on E-government software cost using fuzzy decision making system." Journal of Intelligent & Fuzzy Systems 40, no. 4 (April 12, 2021): 8151–61. http://dx.doi.org/10.3233/jifs-189638.

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The reasonable cost budget of the e-government scheme can effectively promote the construction of the digital government. To analyze the cost impact components of the e-government system and find out the impact factor model works in China, this paper reviews relevant literature on software cost impact factors and proposes the impact factors model based on COCOMO II. Besides, combined with the actual construction of digital government and specific cases, this paper analyzes the mechanism of each impact factor in detail. The model can be used to guide the cost estimation of e-government software in China, especially with artificial intelligence estimation method. An enhanced decision theory of theory based on fuzzy set has been adopted for analysis of cost factor on E-government software cost.

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Yadav, Chandra Shekhar, and Raghuraj Singh. "Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm." Advances in Software Engineering 2014 (June 3, 2014): 1–6. http://dx.doi.org/10.1155/2014/284531.

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The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. Performance of the model is more accurate than the well-established constructive cost model (COCOMO).

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Parlika, Rizky, Devan Cakra Mudra Wijaya, Heri Khariono, and Rifky Akhmad Fernanda. "Studi literatur perbandingan antara metode LOC, COCOMO, FPA dalam ranah software metric." Jurnal Pendidikan Informatika dan Sains 9, no. 1 (June 30, 2020): 66. http://dx.doi.org/10.31571/saintek.v9i1.1697.

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<p>Pengukuran perangkat lunak (<em>software metric</em>) adalah tindakan mengevaluasi dan menilai pelaksanaan kerangka kerja atau seperangkat instrumen untuk memantau dan menentukan tingkat produktivitas perangkat lunak berbasis pada pendekatan kuantitatif. Pengukuran perangkat lunak membantu dalam kemajuan program untuk mengesahkan kualitas barang, survei kualitas individu termasuk dalam pembuatan produk, mengevaluasi manfaat dari penggunaan strategi modern dan instrumen, dan sebagai dasar untuk membuat alat pengukur yang memiliki kegunaan yang berbeda. Estimasi proyek perangkat lunak yang akurat ditentukan oleh tingkat manajerial perangkat lunak yang mana telah memperkirakan ukuran perangkat lunak dengan benar. Perkiraan yang tepat dari ukuran merupakan hal penting dalam menghitung perkiraan biaya proyek, usaha, waktu dan durasi karena menyediakan informasi yang diperlukan untuk pengembangan perangkat lunak. Makalah ini memberikan pendalaman materi dari tiga strategi pengukuran perangkat lunak yang paling umum digunakan yaitu <em>Lines Of Code</em> (LOC), <em>Constructive Cost Model</em> (COCOMO), <em>Function Point Analysis </em>(FPA) dari beberapa referensi yang penulis gunakan.</p>

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Darmaningrat, Eko Wahyu Tyas, Apol Pribadi Subriadi, Sholiq Sholiq, and Abdul Azis. "Implementing COCOMO II as personnel direct cost in an owner estimate cost model for software project." International Journal of Business Information Systems 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbis.2020.10038356.

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Sholiq, N. A., Apol Pribadi Subriadi, Eko Wahyu Tyas Darmaningrat, and Abdul Azis. "Implementing COCOMO II as personnel direct cost in an owner estimate cost model for software project." International Journal of Business Information Systems 41, no. 4 (2022): 472. http://dx.doi.org/10.1504/ijbis.2022.127559.

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Lin, Jin Cherng, and Chu Ting Chang. "Genetic Algorithm and Support Vector Regression for Software Effort Estimation." Advanced Materials Research 282-283 (July 2011): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.748.

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For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).

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Denard, Samuel, Atila Ertas, Susan Mengel, and Stephen Ekwaro-Osire. "Development Cycle Modeling: Resource Estimation." Applied Sciences 10, no. 14 (July 21, 2020): 5013. http://dx.doi.org/10.3390/app10145013.

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This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.

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Najm, Assia, Abdelali Zakrani, and Abdelaziz Marzak. "Cluster-based fuzzy regression trees for software cost prediction." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 1138. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1138-1150.

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The current paper <span lang="EN-US">proposes a novel type of decision tree, which is never used for software development cost prediction (SDCP) purposes, the cluster-based fuzzy regression tree (CFRT). This model uses the fuzzy k-means (FKM), which deals with data uncertainty and imprecision. The tree expansion is based on the variability measure by choosing the node with the highest value of granulation diversity. This paper outlined an experimental study comparing CFRT with four SDCP methods, notably linear regression, multi-layer perceptron, K-nearest-neighbors, and classification and regression trees (CART), employing eight datasets and the leave-one-out cross-validation (LOOCV). The results show that CFRT is among the best, ranked first in 3 datasets according to four accuracy measures. Also, according to the Pred(25%) values, the proposed CFRT model outperformed all the twelve compared techniques in four datasets: Albrecht, constructive cost model (COCOMO), Desharnais, and The International Software Benchmarking Standards Group (ISBSG) using LOOCV and 30-fold cross-validation technique.</span>

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ShekharYadav, Chandra, and Raghuraj Singh. "Tuning of COCOMO II Model Parameters for Estimating Software Development Effort using GA for PROMISE Project Data Set." International Journal of Computer Applications 90, no. 1 (March 1, 2014): 37–43. http://dx.doi.org/10.5120/15542-4367.

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Baumeister, Alexander, and Markus Ilg. "Activity Driven Budgeting of Software Projects." International Journal of Human Capital and Information Technology Professionals 1, no. 4 (October 2010): 14–30. http://dx.doi.org/10.4018/jhcitp.2010100102.

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There are numerous forecast models of software development costs, however, various problems become apparent in context to practical application. Standardized methods, such as COCOMO II have to be calibrated at an individual operational level on the basis of the underlying database. This paper presents a new activity based approach that is based on business specific cost data that can be easily integrated into existing management accounting systems. This approach can be applied to software development projects based on the unified process in which activity driven budgeting promises several advantages compared to common tools in use. It supports enterprise specific cost forecasting and control and can be easily linked with risk analysis. In addition to the presentation of a conceptual design model, the authors present a framework for activity driven budgeting and cost management of software development projects combined with concrete implementation examples.

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Kryvonozhko, Halyna Evgenievna, Olena Valerievna Holikova, and Tatiana Vasilievna Zaikina. "THE IMPORTANCE OF REFACTORING DURING PERFORMANCE OF FORENSIC EXPERTISE (EXPERT RESEARCH) AND CALCULATIONS BASED ON THE COCOMO II MODEL." Expert: Paradigm of Law and Public Administration 9, no. 3 (2020): 84–90. http://dx.doi.org/10.32689/2617-9660-2020-3(9)-84-90.

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Shankhdhar, Gaurav Kant, and Manuj Darbari. "Integrating COCOMO II Model in O-MaSE Methodology for Estimating Effort in Building Heterogeneous and Dynamic Multi-Agent Systems." International Journal of Software Engineering and Its Applications 11, no. 8 (August 31, 2017): 29–40. http://dx.doi.org/10.14257/ijseia.2017.11.8.05.

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Arbain, Siti Hajar, N. H. Mustaffa, N. A. Ali, and D. N. A. Jawawi. "Parameter Design for Group Method Data Handling (GMDH) using Taguchi in Software Effort Estimation." Journal of Physics: Conference Series 2129, no. 1 (December 1, 2021): 012089. http://dx.doi.org/10.1088/1742-6596/2129/1/012089.

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Abstract Recently, the use of data-driven models is becoming increasingly impactful but has proven to offer best prediction with less knowledge of the geological, hydrological, and physical process behaviour and criteria. A Group Data Handling Model (GMDH) is one of the sub-model common neural network data driven. It was first developed for complex systems with a modelling and recognition algorithm. GMDH is known as a self-organizing heuristic modelling approach. For solving modelling issues involving multiple inputs to single output data, it is very successful. While the GMDH model has been implemented in many modelling fields, some modifications in terms of parameter design have been given little attention. In other respects, Dr. Genichi Taguchi suggested that the Taguchi method for improving the process or product design with the help of significant parameter levels that influence the delivery of the product. In this paper, we evaluated the behaviour of GMDH model based on numbers of neuron per layer, hidden layer, alpha, and train ratio parameters using Taguchi method. Cocomo and Kemerer datasets are used to test our hypothesized scenarios. The result shows that number of neurons, layer and train ratio are the important parameters that affects the performance of the GMDH model.

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Goroshko, A., T. Derkach, T. Dmitrenko, and A. Dmitrenko. "ASSESSMENT OF SOCIO-ECONOMIC VALUE AND EFFICIENCY OF AUTHOR'S SOFTWARE AND HARDWARE SYSTEM." Системи управління, навігації та зв’язку. Збірник наукових праць 5, no. 57 (October 30, 2019): 40–44. http://dx.doi.org/10.26906/sunz.2019.5.040.

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The main goal of the article is to study and analyze the functional software and hardware complex application areas, as well as to assess the socio-economic significance and effectiveness of the developed software and hardware complex. Summary. As a study result, the following conclusions were obtained: 1. The implementation effectiveness of a particular invention, project or rationalization proposal is based on taking into account the results and costs incurred to achieve them and the economic feasibility of the proposed solution to the problem. 2. Cost estimation model - COCOMO II and similar models are suitable for automatically calculating the software product cost with a large number of code lines. 3. FSA is the most effective way to determine the economic software and hardware systems feasibility, where you need to take into account the author’s device material component itself. 4. The developed author's software and hardware complex will have an economic effect from the introduction in the first year of its use both for the manufacturer or consumer, and for the state as a whole.

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Kryvonozhko, Halyna Yevgenivna, and Tatiana Vasylivna Zaikina. "FEATURES OF FORENSIC EXPERTISE EXAMINATION OF THE DETERMINATION OF LABOR DEVELOPMENT PROGRAMS ON THE BASIC COCOMO II MODEL UNDER LIMITED INPUT DATA." Expert: Paradigm of Law and Public Administration 7, no. 1 (2020): 101–9. http://dx.doi.org/10.32689/2617-9660-2020-1(7)-101-109.

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