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Journal articles on the topic '"Software Cost Model"'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Ferens, Daniel V., and Sherry Stukes. "Software Cost Model Calibration." Journal of Parametrics 18, no. 1 (1998): 77–98. http://dx.doi.org/10.1080/10157891.1998.10462563.

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2

Gayek, J. E., L. G. Long, K. D. Bell, R. M. Hsu, and R. K. Larson. "Software Cost and Productivity Model." Journal of Parametrics 25, no. 1 (2006): 31–71. http://dx.doi.org/10.1080/10157891.2006.10462271.

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3

Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation,

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4

Saif, Sana, and Sohrab Ansari. "Software cost estimatin through putnam model." Global Sci-Tech 9, no. 4 (2017): 240. http://dx.doi.org/10.5958/2455-7110.2017.00032.5.

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5

Gaffney, John E. "A Cost Model of Software Prototyping." Journal of Parametrics 18, no. 1 (1998): 99–108. http://dx.doi.org/10.1080/10157891.1998.10462564.

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6

Kanawat, Sachin Dev, Apurva Pandey, Abhishek Singh, and Anurag Maloo. "Software Testing Model for Quality." Advanced Materials Research 403-408 (November 2011): 4507–11. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4507.

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Software testing plays an important role during the software developing period. Any saving on software test will significantly reduce the total cost of software. The key point of this paper is to build a software testing process with the cost control management and to make tradeoffs between the software quality and the software cost. Typical software testing model are analysed and compared. A Quality controlled 3-D testing model is introduced. The model combined with software test process, software quality cost and the testing type. The computational formula is defined to calculate software qu

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7

Lee, Sang-Un. "A Gompertz Model for Software Cost Estimation." KIPS Transactions:PartD 15D, no. 2 (2008): 207–12. http://dx.doi.org/10.3745/kipstd.2008.15-d.2.207.

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8

He, Xiaoyang. "Model-Based Methods for Software Cost Estimation." Journal of Computer Research and Development 43, no. 5 (2006): 777. http://dx.doi.org/10.1360/crad20060502.

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9

Ye Zhang, and Kaigui Wu. "Software Cost Model Considering Reliability and Time of Software in Use." Journal of Convergence Information Technology 7, no. 13 (2012): 135–42. http://dx.doi.org/10.4156/jcit.vol7.issue13.16.

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10

Obilor, Njoku A., Agbakwuru A. Onyekachi, Amanze B. Chibuike, and Njoku O. Donatus. "Constructive Cost Model II Metrics for Estimating Cost of Indigenous Software." International Journal of Advanced Engineering Research and Science 8, no. 7 (2021): 224–32. http://dx.doi.org/10.22161/ijaers.87.26.

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11

Du. "A HYBRID INTELLIGENT MODEL FOR SOFTWARE COST ESTIMATION." Journal of Computer Science 9, no. 11 (2013): 1506–13. http://dx.doi.org/10.3844/jcssp.2013.1506.1513.

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12

Russo, Barbara, and Giancarlo Succi. "A Cost Model of Open Source Software Adoption." International Journal of Open Source Software and Processes 1, no. 3 (2009): 60–82. http://dx.doi.org/10.4018/jossp.2009070105.

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13

Guerra, L. G., and H. S. Lobos. "An Empirical Validation of a Software Cost Model." IFAC Proceedings Volumes 23, no. 5 (1990): 57–62. http://dx.doi.org/10.1016/s1474-6670(17)52210-7.

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14

BROWN, A. WINSOR, and BARRY BOEHM. "SOFTWARE COST ESTIMATION IN THE INCREMENTAL COMMITMENT MODEL." Systems Research Forum 04, no. 01 (2010): 45–55. http://dx.doi.org/10.1142/s1793966610000156.

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15

Kim, Hee-Soo, Mi-Young Shin, and Dong-Ho Park. "Optimal Software Release Policy for Random Cost Model." Communications for Statistical Applications and Methods 12, no. 3 (2005): 673–82. http://dx.doi.org/10.5351/ckss.2005.12.3.673.

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16

Lederer, A. L., and J. Prasad. "A causal model for software cost estimating error." IEEE Transactions on Software Engineering 24, no. 2 (1998): 137–48. http://dx.doi.org/10.1109/32.666827.

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17

Marwane, R., and A. Mili. "Building tailor-made software cost model: Intermediate TUCOMO." Information and Software Technology 33, no. 3 (1991): 232–38. http://dx.doi.org/10.1016/0950-5849(91)90138-2.

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18

Mishra, Gaurav, P. K. Kapur, and A. K. Shrivastava. "Multi Release Cost Model — A New Perspective." International Journal of Reliability, Quality and Safety Engineering 24, no. 06 (2017): 1740007. http://dx.doi.org/10.1142/s0218539317400071.

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Software testing plays a crucial role in software development process to achieve high quality software. Increased demand for new features, market competition, time and resource limitation has shortened the software lifecycle. To be in the market, software companies have to release new versions of software with new functionalities. Many ideologies and cost models have been formulated in the past decades with the idea of when to release the software for single and multi-versions. In the existing literature, the total debugging cost for the first version and next versions includes the cost of deb

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19

Ahamad, Mohd. "Microgrid Model on Homer Software." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 757–60. http://dx.doi.org/10.22214/ijraset.2021.36375.

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A new concept in power generation is a microgrid. The Microgrid concept assumes a cluster of loads and microsources operating as a single controllable system that provides power to its local area. This concept provides a new paradigm for defining the operation of distributed generation. The microsources of special interest for MGs are small (<100-kW) units with power electronic interfaces. These sources are placed at customers sites. They are low cost, low voltage and have a high reliability with few emissions. Power electronics provide the control and flexibility required by the MG concept

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20

Singh, Ompal, P. K. Kapur, A. K. Shrivastava, and Gaurav Mishra. "A Multi Release Cost Model in Distributed Environment." International Journal of Reliability, Quality and Safety Engineering 24, no. 01 (2017): 1750001. http://dx.doi.org/10.1142/s0218539317500012.

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Software industry has reached far beyond since its origin. Technical advancements are taking place at a speed faster than ever. This has further enhanced the pressure on software developers. They are trying hard to keep a pace with these rapid developments by coming out with strategies to increase the pace of their work without significantly affecting the software quality and reliability. One such strategy is distributed development of software in which the software is composed of two different types of components viz. newly developed and re-used component. Software developed in Distributed De

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21

Goyal, Sheena, and Sheilly Padda. "Back Propagation Algorithm Based Model for Software Cost Estimation." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (2017): 509–13. http://dx.doi.org/10.23956/ijarcsse/v7i6/0278.

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22

Kashyap, Divya. "Software Development Effort and Cost Estimation: Neuro-Fuzzy Model." IOSR Journal of Computer Engineering 2, no. 4 (2012): 12–14. http://dx.doi.org/10.9790/0661-0241214.

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23

Malik, Shobha Rani. "A Cost Estimation Model For Reuse Based Software Program." IOSR Journal of Computer Engineering 4, no. 6 (2012): 06–12. http://dx.doi.org/10.9790/0661-0460612.

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24

Ellis, Tim. "1.2.2 COTS INTEGRATION IN SOFTWARE SOLUTIONS A COST MODEL." INCOSE International Symposium 5, no. 1 (1995): 174–82. http://dx.doi.org/10.1002/j.2334-5837.1995.tb01859.x.

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25

Ferens, Daniel V. "A Common Sense Approach to Software Cost Model Selection." Journal of Parametrics 6, no. 2 (1986): 30–40. http://dx.doi.org/10.1080/10157891.1986.10462687.

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26

Pillai, K., and V. S. Sukumaran Nair. "A model for software development effort and cost estimation." IEEE Transactions on Software Engineering 23, no. 8 (1997): 485–97. http://dx.doi.org/10.1109/32.624305.

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27

Hoang Pham and Xuemei Zhang. "A software cost model with warranty and risk costs." IEEE Transactions on Computers 48, no. 1 (1999): 71–75. http://dx.doi.org/10.1109/12.743412.

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28

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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29

Cuelenaere, AME, MJIM van Genuchten, and FJ Heemstra. "Calibrating a software cost estimation model: why and how." Information and Software Technology 29, no. 10 (1987): 558–67. http://dx.doi.org/10.1016/0950-5849(87)90089-9.

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30

PHAM, HOANG. "A software cost model with imperfect debugging, random life cycle and penalty cost." International Journal of Systems Science 27, no. 5 (1996): 455–63. http://dx.doi.org/10.1080/00207729608929237.

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31

ZHANG, XUEMEI, and HOANG PHAM. "A software cost model with warranty cost, error removal times and risk costs." IIE Transactions 30, no. 12 (1998): 1135–42. http://dx.doi.org/10.1080/07408179808966570.

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32

Moradbeiky, Amin, Vahid Khatibi Bardsiri, and Mehdi Jafari. "Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation." Computing and Informatics 39, no. 6 (2020): 1148–71. http://dx.doi.org/10.31577/cai_2020_6_1148.

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33

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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34

Ahmed Khalid, Tarig, and Eng-Thiam Yeoh. "Enhancing software development cost control by forecasting the cost of rework: preliminary study." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 524. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp524-537.

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<p><span>Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting t

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35

Rashid, Junaid, Muhammad Wasif Nisar, Toqeer Mahmood, Amjad Rehman, and Yasser Arafat Syed. "A Study of Software Development Cost Estimation Techniques and Models." April 2020 39, no. 2 (2020): 413–31. http://dx.doi.org/10.22581/muet1982.2002.18.

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SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the literature will assist future researchers to get maximum knowledge about SDCE techniques and models from one paper and to save their time. In this paper, we review numerous software development effort and cost estimation models and techniques, which are divided into different categories. These categories are parametric models, expertise-based techniques

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36

Rashid, Junaid, Muhammad Wasif Nisar, Toqeer Mahmood, Amjad Rehman, and Yasser Arafat Syed. "A Study of Software Development Cost Estimation Techniques and Models." April 2020 39, no. 2 (2020): 413–31. http://dx.doi.org/10.22581/10.22581/muet1982.2002.18.

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Abstract:

SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the literature will assist future researchers to get maximum knowledge about SDCE techniques and models from one paper and to save their time. In this paper, we review numerous software development effort and cost estimation models and techniques, which are divided into different categories. These categories are parametric models, expertise-based techniques

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37

Sohn, So Young, and Ji Soo Lee. "Cost of ownership model for a CRM system." Science of Computer Programming 60, no. 1 (2006): 68–81. http://dx.doi.org/10.1016/j.scico.2005.06.004.

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38

Chatterjee, Subhashis, and Ankur Shukla. "An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point." Asia-Pacific Journal of Operational Research 34, no. 03 (2017): 1740017. http://dx.doi.org/10.1142/s0217595917400176.

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This paper presents a general software reliability growth model (SRGM) based on non-homogeneous Poisson process (NHPP) and optimal software release policy with cost and reliability criteria. The main motive of this study is to develop a software release time decision model considering maintenance cost and warranty cost under fuzzy environment. In previous studies, maintenance cost has been defined either in terms of warranty cost or fault debugging cost. In reality, maintenance cost includes the cost of free patches, updates, technical support and future enhancement. Also, it is possible that

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39

Yang, Tae-Jin. "The Analysis and Prediction of Software Development Cost Based on NHPP Software Reliability Model." Journal of Engineering and Applied Sciences 14, no. 23 (2019): 8779–85. http://dx.doi.org/10.36478/jeasci.2019.8779.8785.

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40

Baker, M. E., D. Hesselink, G. P. Borkowski, and M. T. Modic. "Cost accounting in radiology: building a cost model using hospital-based, commercially available software." American Journal of Roentgenology 171, no. 1 (1998): 7–12. http://dx.doi.org/10.2214/ajr.171.1.9648754.

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41

Jo, Ju-Yeon, and Sung-Yul Rhew. "Cost Measurement Indicators and Maintenance Cost Estimation Model for Maintenance of Component-based Software." KIPS Transactions:PartD 16D, no. 3 (2009): 353–60. http://dx.doi.org/10.3745/kipstd.2009.16-d.3.353.

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42

Wani, Zahid Hussain, Kaiser Javeed Giri, and Rumaan Bashir. "Improved Software Cost Estimation Model Using Cost Driver Reduction Based on Water Cycle Algorithm." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 64–72. http://dx.doi.org/10.26438/ijcse/v7i2.6472.

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43

V.Patil, Lalit, Sagar K Badjate, and S. D. Joshi. "Develop Efficient Technique of Cost Estimation Model for Software Applications." International Journal of Computer Applications 87, no. 16 (2014): 18–22. http://dx.doi.org/10.5120/15292-3951.

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44

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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45

Heemstra, F. J., and R. J. Kusters. "Function point analysis: evaluation of a software cost estimation model." European Journal of Information Systems 1, no. 4 (1991): 229–37. http://dx.doi.org/10.1057/ejis.1991.45.

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46

Mertes, Karen R., Daniel V. Ferens, and David S. Christensen. "An Empirical Validation of the Checkpoint Software Cost Estimation Model." Journal of Cost Analysis & Management 1, no. 1 (1999): 35–44. http://dx.doi.org/10.1080/15411656.1999.10462398.

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47

Patra, H. Parthasarathi, and Kumar Rajnish. "A new high performance empirical model for software cost estimation." International Journal of Computer Aided Engineering and Technology 11, no. 4/5 (2019): 601. http://dx.doi.org/10.1504/ijcaet.2019.10020296.

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48

Patra, H. Parthasarathi, and Kumar Rajnish. "A new high performance empirical model for software cost estimation." International Journal of Computer Aided Engineering and Technology 11, no. 4/5 (2019): 601. http://dx.doi.org/10.1504/ijcaet.2019.100454.

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49

Kim, Kyung-Soo, and Hee-Cheul Kim. "The Comparative Software Reliability Cost Model of Considering Shape Parameter." Journal of Digital Convergence 12, no. 3 (2014): 219–26. http://dx.doi.org/10.14400/jdc.2014.12.3.219.

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50

Galinac Grbac, Tihana, Željka Car, and Darko Huljenić. "A quality cost reduction model for large-scale software development." Software Quality Journal 23, no. 2 (2014): 363–90. http://dx.doi.org/10.1007/s11219-014-9240-8.

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