Using Support Vector Regression for Web Development Effort Estimation

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

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Least Square Support Vector Machine in Analogy-Based software development effort estimation

2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE) ◽

10.1109/icraie.2016.7939524 ◽

2016 ◽

Cited By ~ 2

Author(s):

Tirimula Rao Benala ◽

Rohitha Bandarupalli

Keyword(s):

Support Vector Machine ◽

Software Development ◽

Least Square ◽

Support Vector ◽

Development Effort ◽

Effort Estimation ◽

Software Development Effort ◽

Software Development Effort Estimation

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Web Development Effort Estimation

Handbook of Research on Web Information Systems Quality ◽

10.4018/978-1-59904-847-5.ch002 ◽

2011 ◽

pp. 26-56 ◽

Cited By ~ 1

Author(s):

Emilia Mendes ◽

Silvia Abrahão

Keyword(s):

Multivariate Regression ◽

Project Managers ◽

Case Based Reasoning ◽

Development Effort ◽

Web Development ◽

Effort Estimation ◽

Estimation Techniques ◽

Classification And Regression ◽

Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

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