Constructing C++ software size estimation model from class diagram

Kiewkanya, Matinee; Surak, Suttipong

doi:10.1109/jcsse.2016.7748880

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…To build a nonlinear regression model for estimating the size of Web apps created using the CodeIgniter framework, we collected data from 50 apps hosted on GitHub (https://github.com). We took 50 apps because it is generally accepted that the lower limit of a large sample is 30, and many models (for example, [11,15]) were built for the data number from 30 to 50. We obtained the data set by the PhpMetrics tool (https://phpmetrics.org/) around the following variables: actual software size (in KLOC) Y, the total number of classes 1 X , the average number of methods per class 2 X , and the DIT average per class 3 X .…”

Section: Problem Solutionmentioning

confidence: 99%

“…This is primarily due to the low accuracy of estimating the size of the software in the early stages of its development. One way to solve this problem is to develop appropriate models for estimating the size of the software, which is developed in a specific programming language such as C++ [11], Java [12], PHP [13], Visual Basic [14] and for a specific type of app, including information systems [15], embedded software components [16], Web apps [17].…”

Section: Introductionmentioning

confidence: 99%

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Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Prykhodko

Shutko²,

Prykhodko³

2022

reks

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Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, the assessment of efforts requires considering such factors (environmental factors). Considering the above factors can be ensured by appropriate models for estimating the LOC-based effort. Nowadays, many Web apps are created using PHP frameworks making the app development faster. CodeIgniter is one such powerful framework. However, there are no regression models for estimating the software size of Web apps created using the CodeIgniter framework. This requires the construction of the appropriate models. The task of this paper is to develop a nonlinear regression model for estimating the software size (in KLOC, kilo lines of code) of Web apps created using the CodeIgniter framework. Method: We apply the technique for constructing nonlinear regression models based on the multivariate normalizing transformations and prediction intervals. The result is three nonlinear regression models with three predictors: the total number of classes, the average number of methods per class, and the DIT (Depth of Inheritance Tree) average per class. To build these models for estimating the size of Web apps created using the CodeIgniter framework, we used three well-known normalizing transformations: two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. Conclusions. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results than other regression models based on the univariate transformations.

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Section: Problem Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Prykhodko

Shutko²,

Prykhodko³

2022

reks

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“…Kiewkanya and Surak [32] developed an automated tool for regression-based software size estimation of C++ projects. They used 8 structural complexity metrics extracted from DCD.…”

Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Early Software Size Estimation Using Analysis-to-Design Adjustment Factors (ADAFs)

Daud

Malik

2021

IEEE Access

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Early software size estimation is a challenging task since limited information is available at the time of project inception. Additional information, however, is gradually added as development progresses. The goal of this research is to quantitatively capture the impact on early software size estimation of this additional information introduced especially when transitioning from the analysis phase to the design phase by comparing the analysis class diagram (ACD) and the design class diagram (DCD). We introduce a new class of metrics called analysis-to-design adjustment factors (ADAFs) to accomplish this goal. ADAFs are calculated for four different class diagram metricsnumber of classes (NOC), number of attributes (NOA), number of methods (NOM), and number of relationships (NOR)used in different class diagram-based software size estimation models. We use practical, theoretical, and empirical validation methods to evaluate the applicability of these ADAFs. To assess the utility of these ADAFs in early software size estimation, we compare the accuracy of existing early software size estimation models before and after the application of ADAFs. Results indicate a marked improvement in the accuracy of these models after the application of ADAFs. Furthermore, regression-based models employing problem domain metrics have also been built to predict these ADAFs. All of these models are statistically significant (pvalues < 0.05) with R 2 values between 0.42 and 0.88.

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Software Size Estimation in Design Phase Based on MLP Neural Network

Panyangam

Kiewkanya

2017

Advances in Intelligent Systems and Computing

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Constructing C++ software size estimation model from class diagram

Cited by 5 publications

References 4 publications

Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Improving the Accuracy of Early Software Size Estimation Using Analysis-to-Design Adjustment Factors (ADAFs)

Software Size Estimation in Design Phase Based on MLP Neural Network

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