Email this article 
Open Access
Subscription or Fee Access
Toward a Proposed Model for Effort Estimation of Developing Mobile Applications
Abstract
Effort estimation techniques play a key role in the planning process for the development of mobile phone applications. The development of mobile applications is different from the traditional applications of information systems because of their dissimilar characteristics and the rapid advancement of technology used in the development of the former. For this, existing traditional effort estimation techniques may not be suitable for use in predicting the development effort of mobile applications. The process of estimating and predicting the effort depends mainly on the characteristics of the applications. The aim of this study is to propose a methodology for the use of intelligent techniques to predict the effort to develop mobile applications, which are considered unconventional and to cope with the rapid development of mobile application development environment.
Keywords
References
M. Łabkedzki, P. Promiński, A. Rybicki, and M. Wolski, “Agile effort estimation in software development projects--case study,” Cent. Eur. Rev. Econ. Manag., vol. 1, no. 3, pp. 135–152, 2017.
A. T. ; Raslan, R. D. ; Nagy, and H. A. Hefny, “Towards a Fuzzy based Framework for Effort Estimation in Agile Software Development,” Int. J. Comput. Sci. Inf. Secur., vol. 13, no. 1, pp. 37–45, 2015.
P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski, “An effective approach for software project effort and duration estimation with machine learning algorithms,” J. Syst. Softw., vol. 137, no. November, pp. 184–196, 2018.
R. Tripathi and P. K. Rai, “Machine Learning Methods of Effort Estimation and I t ’ s Performance Evaluation Criteria,” vol. 6, no. 1, pp. 61–67, 2017.
A. Nitze, A. Schmietendorf, and R. Dumke, “An analogy-based effort estimation approach for mobile application development projects,” Proc. - 2014 Jt. Conf. Int. Work. Softw. Meas. IWSM 2014 Int. Conf. Softw. Process Prod. Meas. Mensura 2014, pp. 99–103, 2014.
L. S. De Souza and G. S. De Aquino, “Mobile application development: How to estimate the effort?,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8583 LNCS, no. PART 5, pp. 63–72, 2014.
B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” Engineering, vol. 2, p. 1051, 2007.
G. Jošt, J. Huber, and M. H. E. R. Ičko, “Using Object Oriented Software Metrics for Mobile Application Development,” Second Work. Softw. Qual. Anal. Monit. Improv. Appl., pp. 17–27, 2013.
T. Arnuphaptrairong and W. Suksawasd, “An Empirical Validation of Mobile Application Effort Estimation Models,” vol. II, 2017.
A. Nitze, “Measuring Mobile Application Size Using COSMIC FP,” MetriKon, no. March, pp. 101–114, 2013.
V. Tunalı, “Software Size Estimation Using Function Point Analysis – A Case Study for a Mobile Application,” 7. Mühendislik ve Teknol. Sempozyumu, no. May, pp. 73–76, 2014.
S. A. Shahwaiz, A. A. Malik, and N. Sabahat, “A parametric effort estimation model for mobile apps,” 19th Int. Multi-Topic Conf. INMIC 2016, 2017.
L. S. De Souza and G. S. De Aquino Jr, “Estimating the Effort of Mobile Application Development,” Comput. Sci. Inf. Technol. ( CS IT ), pp. 45–63, 2014.
N. A. S. Abdullah, N. I. A. Rusli, and M. F. Ibrahim, “A case study in COSMIC functional size measurement: Angry bird mobile application,” in 2013 IEEE Conference on Open Systems, ICOS 2013, 2013, pp. 139–144.
F. Ferrucci, C. Gravino, P. Salza, and F. Sarro, “Investigating functional and code size measures for mobile applications: A replicated study,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9459, 2015.
H. K. Flora, X. Wang, and S. V.Chande, “An Investigation into Mobile Application Development Processes: Challenges and Best Practices,” Int. J. Mod. Educ. Comput. Sci., vol. 6, no. 6, pp. 1–9, 2014.
N. Singh and D. Soni, “Proposing New Model for Effort Estimation of Mobile Application Development,” Int. J. Comput. Appl., vol. 170, no. 3, pp. 975–8887, 2017.
R. Francese, C. Gravino, M. Risi, G. Scanniello, and G. Tortora, “On the Use of Requirements Measures to Predict Software Project and Product Measures in the Context of Android Mobile Apps: A Preliminary Study,” Proc. - 41st Euromicro Conf. Softw. Eng. Adv. Appl. SEAA 2015, no. ii, pp. 357–364, 2015.
N. Sabahat, A. L. I. A. Malik, and F. Azam, “A Size Estimation Model for Board-Based Desktop Games,” vol. 5, 2017.
F. Qi, X. Jing, X. Zhu, X. Xie, B. Xu, and S. Ying, “Software effort estimation based on open source projects : Case study of Github,” vol. 92, pp. 145–157, 2017.
S. K. Sehra, Y. S. Brar, N. Kaur, and S. S. Sehra, “Research patterns and trends in software effort estimation,” Inf. Softw. Technol., vol. 91, pp. 1–21, 2017.
J. Huang, Y. Li, and M. Xie, “An empirical analysis of data preprocessing for machine learning-based software cost estimation,” Inf. Softw. Technol., vol. 67, pp. 108–127, 2015.
P. Pospieszny, B. Czarnacka-chrobot, and A. Kobylinski, “The Journal of Systems and Software An effective approach for software project effort and duration estimation with machine learning algorithms,” J. Syst. Softw., vol. 137, pp. 184–196, 2018.
P. Rijwani and S. Jain, “Enhanced Software Effort Estimation Using Multi Layered Feed Forward Artificial Neural Network Technique,” Procedia Comput. Sci., vol. 89, pp. 307–312, 2016.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.