Web Effort Estimation Research Articles

Web Effort Estimation: Function Point Analysis vs. COSMIC

Context: software development effort estimation is a crucial management task that critically depends on the adopted size measure. Several Functional Size Measurement (FSM) methods have been proposed. COSMIC is considered a 2nd generation FSM method, to differentiate it from Function Point Analysis (FPA) and its variants, considered as 1st generation ones. In the context of Web applications, few investigations have been performed to compare the effectiveness of the two generations. Software companies could benefit from this analysis to evaluate if it is worth to migrate from a 1st generation method to a 2nd one.Objective: the main goal of the paper is to empirically investigate if COSMIC is more effective than FPA for Web effort estimation. Since software companies using FPA cannot build an estimation model based on COSMIC as long as they do not have enough COSMIC data, the second goal of the paper is to investigate if conversion equations can be exploited to support the migration from FPA to COSMIC.Method: two empirical studies have been carried out by employing an industrial data set. The first one compared the effort prediction accuracy obtained with Function Points (FPs) and COSMIC, using two estimation techniques (Simple Linear Regression and Case-Based Reasoning). The second study assessed the effectiveness of a two-step strategy that first exploits a conversion equation to transform historical FPs data into COSMIC, and then builds a new prediction model based on those estimated COSMIC sizes.Results: the first study revealed that, on our data set, COSMIC was significantly more accurate than FPs in estimating the development effort. The second study revealed that the effectiveness of the analyzed two-step process critically depends on the employed conversion equation.Conclusion: for Web effort estimation COSMIC can be significantly more effective than FPA. Nevertheless, additional research must be conducted to identify suitable conversion equations so that the two-step strategy can be effectively employed for a smooth migration from FPA to COSMIC.

A Fuzzy Decision Tree to Estimate Development Effort for Web Applications

Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation, it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used, as measures of prediction accuracy, for this study. A series of experiments is reported using Tukutuku software projects dataset. The results are compared with those produced by three crisp versions of decision trees: ID3, C4.5 and CART.

Investigating the use of Support Vector Regression for web effort estimation

Support Vector Regression (SVR) is a new generation of Machine Learning algorithms, suitable for predictive data modeling problems. The objective of this paper is twofold: first, to investigate the effectiveness of SVR for Web effort estimation using a cross-company dataset; second, to compare different SVR configurations looking at the one that presents the best performance. In particular, we took into account three variables' preprocessing strategies (no-preprocessing, normalization, and logarithmic), in combination with two different dependent variables (effort and inverse effort). As a result, SVR was applied using six different data configurations. Moreover, to understand the suitability of kernel functions to handle non-linear problems, SVR was applied without a kernel, and in combination with the Radial Basis Function (RBF) and the Polynomial kernels, thus obtaining 18 different SVR configurations. To identify, for each configuration, which were the best values for each of the parameters we defined a procedure based on a leave-one-out cross-validation approach. The dataset employed was the Tukutuku database, which has been adopted in many previous Web effort estimation studies. Three different training and test set splits were used, including respectively 130 and 65 projects. The SVR-based predictions were also benchmarked against predictions obtained using Manual StepWise Regression and Case-Based Reasoning. Our results showed that the configuration corresponding to the logarithmic features' preprocessing, in combination with the RBF kernel provided the best results for all three data splits. In addition, SVR provided significantly superior prediction accuracy than all the considered benchmarking techniques.

Validating a size measure for effort estimation in model-driven Web development

Web development is moving towards model-driven processes whose goal is the development of Web applications at a higher level of abstraction based on models and model transformations. This brings new opportunities to the Web project manager to make early estimates of the size and the effort required to produce Web applications based on their conceptual models. In the last few years, several studies for size and effort estimation have been performed. However, there are no studies regarding effort estimation in model-driven Web development. In this paper, we present the validation of a model-based size measure (OO-HFP) for Web effort estimation in the context of a model-driven Web development method. The validation is performed by comparing the prediction accuracy that OO-HFP provides with the accuracy provided by the standard function point analysis (FPA) method. The results of the study (using industrial data gathered from 31 Web projects) show that the effort estimates obtained for projects that are sized using OO-HFP are more accurate than the effort estimates obtained using the standard FPA method. This suggests that by following a model-driven development approach, the size measure obtained at the conceptual model of a Web application can be considered a suitable predictor of effort.

Bayesian Network Models for Web Effort Prediction: A Comparative Study

The objective of this paper is to compare, using a cross-company dataset, several Bayesian network (BN) models for Web effort estimation. Eight BNs were built; four automatically using Hugin and PowerSoft tools with two training sets, each with 130 Web projects from the Tukutuku database; four using a causal graph elicited by a domain expert, with parameters automatically fit using the same training sets used in the automated elicitation (hybrid models). Their accuracy was measured using two validation sets, each containing data on 65 projects, and point estimates. As a benchmark, the BN-based estimates were also compared to estimates obtained using manual stepwise regression (MSWR), case-based reasoning (CBR), mean- and median-based effort models. MSWR presented significantly better predictions than any of the BN models built herein, and in addition was the only technique to provide significantly superior predictions to a median-based effort model. This paper investigated data-driven and hybrid BN models using project data from the Tukutuku database. Our results suggest that the use of simpler models, such as the median effort, can outperform more complex models, such as BNs. In addition, MSWR seemed to be the only effective technique for Web effort estimation.