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WEB EFFORT ESTIMATION TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW

Manpreet kaur Dhaliwal, Sumesh Sood

Abstract


Web Effort Estimation is an important estimation measure for predicting the effort required to develop a web application. The completion of web projects within stipulated time and budget is not possible without accurate effort estimation. The numerous effort estimation models are present these days and they have achieved a pinnacle of success, but the uncertainty features are daunting its progress due to deviations in the data set collected, types of projects, and data set characteristics. The literature studied for this research task elaborated that this field still lacks in a significant direction for consolidated documentation, which guides the researchers to choose a specific technique in order to predict the effort required for web application development. The wide and versatile nature of this domain daunting the researchers to mine the literature in a more appropriate way and deploy ensemble techniques of effort prediction models in order to achieve better results for web application viz., schedule delays, budget overruns. The systematic literature review (SLR) in this research task has been done to inspect the various aspects affecting the prediction accuracy of web applications and these identified characteristics lead to a better effort estimation model. The literature review is conducted on a collection of 143 papers retrieved from online journals and conference proceedings. Only 53 relevant papers are selected for broad investigation.  The study reveals that the expert judgment and algorithm-based models are very popular and used frequently for effort prediction, instead the machine learning (ML) based models are rare in use but cater comparatively better prediction accuracy. The authors suggest taking cognizance of this research domain for developing ensembles of early effort prediction models to overcome delays in schedule and budget.


Keywords


Effort Estimation; Machine Learning; Web applications; Algorithmic; Expert Opinion.

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References


Martino, S. D., Ferrucci, F., Gravino, C., & Mendes, E. (2007). Comparing Size Measures for Predicting Web Application Development Effort: A Case Study. First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007) (pp. 324-333). Madrid, Spain: IEEE.

Moayed, M. J., Ghani, A. A., & Seyedzadegan, M. (2007). Comparing Between Web Application Effort Estimation Methods. Fifth International Conference on Computational Science and its Applications (ICCSA 2007) (pp. 153-160). Kuala Lumpur, Malaysia: IEEE.

Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2003). A Comparative Study of Cost Estimation models for Web Hypermedia Applications. (L. Briand, Ed.) Empirical Software Engineering, 163-196.

Mendes, E., N., M., & Counsell, S. (2001). Using an Engineering Approach to Understanding and Predicting Web authoring and Design. In Proc. 34th Hawaii Int'l Conference on System Sciences. IEEE.

Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning-based software development effort estimation models. Information and Software Technology, 54, 41-59.

Mendes, E., & Mosley, N. (2008). Bayesian Network Models for Web Effort Prediction: A Comparative Study. IEEE Transactions on Software Engineering, 34(6), 723-737.

Corazza, A., Martino, D. S., Ferrucci, F., Gravino, C., & Mendes, E. (2009). Applying Support Vector Regression for Web Effort Estimation using a Cross-Company Dataset. Third International Symposium on Empirical Software and measurement (pp. 191-202). IEEE.

Satapathy, S. M., & Rath, S. K. (2016). Effort estimation of web-based applications using machine learning techniques. Int'l Conference on Advances in Computing, Communications, and Informatics(ICACCI) (pp. 973-980). Jaipur, India: IEEE.

Kitchenham, B., Brereton, O. P., Budgon, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in Software Engineering- A systematic literature review. Information and Software Technology, 7-15.

Kitchenham, B. (2004). Procedures for performing systematic reviews. NICTA Technical Report.

Mendes, E., Mosley, N., & Watson, I. (2002). A Comparison of Case-Based reasoning Approaches to Web Hypermedia Project Cost Estimation. Proceedings of the 11th international conference on World Wide Web (pp. 272-280). Honolulu, Hawaii, USA: ACM.

Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2002). A Comparison of Development Effort estimation Techniques for Web Hypermedia Applications. Proceedings Eighth IEEE Symposium on Software Metrics. Ottawa, Ontario, Canada, IEEE.

Hooi, T. C., & Yusoff, Y. H. (2008). Comparative Study on Applicability of WEBMO in Web Application Cost Estimation within Klang Valley in Malaysia. 8th International Conference on Computer and Information Technology Workshops (pp. 116-121). Sydney, QLD, Australia: IEEE.

Araujo, Ricardo de A., Sergio Soares and L.I. Adriano Oliveira. "Hybrid morphological methodology for software development cost estimation." Expert Systems with Applications 39 (2012): 6129-6139.

Bardsiri, Vahid Khatibi, et al. "A PSO-based model to increase the accuracy of software development effort estimation." Software Quality (2013): 501-526.

Dave, V. S. and K. Dutta. "Neural network based models for software effort estimation: a review." Artificial Intelligence (2014): 295-307.

Urbanek, T., et al. "Prediction accuracy measurements as a fitness function for software effort estimation." SpringerPlus (2015).

Sachan, R. K., et al. "Optimizing Basic COCOMO Model using simplified Genetic Algorithm." Twelfth International multi-conference on Information processing. Elsevier ScienceDirect, 2016. 492-498.

Reza, S. M., Rahman, M. M., Parvez, M. H., Kaiser, M. S., & Mamun, S. A. (2015). Innovative approach in Web Application Effort & Cost Estimation using Functional Measurement Type. 2nd Int'l Conference on Electrical Engineering and Information & Communication Technology(ICEEICT). Dhaka, Bangladesh: IEEE.

Zare, F., H. K. Zare and M. S. Fallahnezhad. "Software effort estimation based on the optimal Bayesian belief network." Applied Soft Computing (2016): 968-980

Minku, L. L. and X. Yao. "Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models." Auton Software Engineering (2017): 499-542.

Resmi, V., Vijayalakshmi, S., Chandrabose, R. S. “An effective software project effort estimation system using optimal firefly algorithm”. Cluster Computing Springer (2017)

Moosavi, S. H. and V. K. Bardsiri. "Satin Bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation." Engineering applications of Artificial Intelligence 60 (2017): 1-15.

Satapathy, S. M. and S. K. Rath. "Empirical assessment of machine learning moedls for agile software development effort estimation using story points." Innovations System Software (2017): 191-200.

Pospieszny, P., B. C. Chrobot and A. Kobylinski. "An effective approach for software project effort and duration estimation with machine learning algorithms." The Journal of Systems and Software 137 (2018): 184-196.

Usman, M., Petersen, K.., Borstler, J., Neto, P.S. "Developing and using checklists to improve software effort estimation: A multi-case study." The Journal of Systems and Software (2018): 286-309.

Singh, T., R. Singh and K K Mishra. "Software cost estimation using Environmental Adaptation method." 8th International conference on advances in computing and communication. Elsevier ScienceDirect, 2018. 325-332.

Usman, M.,Britto, R., Damm, L., Borstler, J. "Effort estimation in large-scale software development: An industrial case study." Information and Software technology (2018): 21-40.

Abrahao, S., Marco, L. D., Ferrucci, F., Gomez, J., Gravino, C. "Definition and evaluation of a COSMIC measurement procedure for sizing web applications in model-driven development environment." Information and Software technology (2018) 144-161.

Floriano, a. G., et al. "Support Vector regression for predicting software enhancement effort." Information and Software Technology (2018): 99-109.

Abdelali, Z., H. Mustapha and N. Abdelwahed. "Investigating the use of random forest in software effort estimation." Second International Conference on Intelligent Computing in data Sciences. Els ScienceDirectevier, 2019. 343-352.

Lorko, M., M. Servatka and Le Zhang. "Anchoring in project duration estimation." Journal of Economic behavior and Organization (2019): 49-65.

Minku, L. Leandro. "A novel online supervised hyperparameter tuningn procedure applied to cross-company software effort estimation." Empirical Software Engineering (2019): 3153-3204.

Reifer, D. (2002). Estimating Web Development Costs: There are differences. Crosstalk, pp. 13-17.

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (version 2.3). Software Engineering Group, School of Computer Science and Mathematics, Keele University and Department of Computer Science, University of Durham.

Morisio, M., Stamelos, I., Spahose, V., & Romano, D. (1999). Measuring functionality and productivity in Web-based applications : A Case Study. In: Proceeding of International Software metrics Symposium (pp. 111-118). Los Alanitos: IEEE press.

Martino, D. S., Ferrucci, F., Gravino, C., & Sarro, F. (2011). Using Web objects for development effort estimation of web applications. Springer-Verlag Berlin Heidelberg, 186-201.

Ruhe, M., Wieczorek, I., & Jeffery, R. (2003). Cost estimation for Web applications. Proceedings - International Conference on Software Engineering. Research Gate.

Rosmina, & Suharjito. (2012). FHSWebEE: An Effort Estimation Model for Web Application. International Conference on Advances Science and Contemporary Engineering.50, pp. 613-622. Elsevier, ScienceDirect.

Reifer, D. (2000). Web Development: Estimating Quick time to Market Software. IEEE Software. 17(6), 57-64.

Marco, L. D., Ferrucci, F., Gravino, C., Sarro, F., Abrahao, S., & Gomez, J. (2012). Functional versus Design Measures for Model-Driven Web Applications:A Case Study in the Context of Web Effort Estimation. WETSoM (pp. 21-27). Zurich, Switzerland: IEEE.

Oliveira, A., Braga, P. L., Lima, R. M., & Cornelio, M. L. (2010). GA-based method for feature selection and parameter optimization for machine learning regression applied to software effort estimation. Information and Software Technology, 52, 1155-1166.

Lin, J. C., Chang, C. T., & Huang, S. Y. (2011). Research on Software Effort Estimation Combined with Genetic Algorithm and Support Vector Regression. International Symposium on Computer Science and Society (pp. 349-352). IEEE.

Corazza, A., Martino, S. D., Ferrucci, F., Gravino, C., Sarro, F., & Mendes, E. (2011). Using Tabu Search to configure support vector regression for effort estimation. In T. Menzies, & G. Koru (Ed.), Empirical Software Engineering. Springer.

T. K. Lee, K. T. Wei and A. A. A. Ghani, "Systematic literature review on effort estimation for Open Sources (OSS) web application development," 2016 Future Technologies Conference (FTC), San Francisco, CA, 2016, pp. 1158-1167.

doi: 10.1109/FTC.2016.7821748.




DOI: http://dx.doi.org/10.6084/ijact.v8i11.1017

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