Measurement of Software Size: Advances Made by the COSMIC Community

Symons, Charles; Abran, Alain; Ebert, Christof; Vogelezang, Frank

doi:10.1109/iwsm-mensura.2016.021

Cited by 8 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The correlation between this study and the Journal lies in the alignment of their subject matter, methodology, and objectives. (1) This subject matter aligns with the interests of the international journal of intelligent engineering and systems, which actively seeks research related to intelligent techniques and methodologies applied to various engineering domains. By exploring the integration of CFP into COCOMO II and investigating the conversion to lines of code using regression and SVR models, this study addresses a topic relevant to the Journal's scope.…”

Section: Introductionmentioning

confidence: 91%

“…For this reason, the test of these classic assumptions includes [26]: (1) The normality test aims to know that the data are typically distributed and independent. (2) The multicollinearity test is a condition with a perfect linear relationship or near perfect between the independent variables in the regression model.…”

Section: Modelling Data Using Linear Regressionmentioning

confidence: 99%

“…The software project manager can control the project from design to implementation and evaluation. Four primary performances must be measured and controlled by managers in order to control the project, namely [1]: (i) project delivery for time and budget, (ii) project productivity, (iii) project speed, and (iv) product size and quality. Software cost is measured in the software development process's initial phases for project planning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

2023

IJIES

View full text Add to dashboard Cite

In COCOMO II, the primary input for estimating development effort in person month (PM), duration, and cost is the size of the software. Until now, there are two ways to get the size, namely (1) size is estimated using a line of code of software, and (2) size is estimated using unadjusted function points (UFP), which is one of the functional size measurements (FSM). In this study, we added a new way to obtain the size as the primary input in COCOMO II, namely with COSMIC function points (CFP). CFP has several advantages compared to other FSMs, including UFP. Therefore, like UFP, CFP is converted first to LOC, so the conversion equation must be obtained first. We applied four models to get the conversion functions: Ordinary least squares regression (OLSR), support vector regression (SVR) with linear, polynomial, and Gaussian kernel functions. The four models were applied using a dataset from small-scale business application software in Java. The results showed that PM estimation using the CFP model as the primary input produced better accuracy based on MMRE and Pred (0.25), namely 17%-19% and 67%-80%, than the UFP model on the COCOMO II of 135% and 10%.

show abstract

Section: Introductionmentioning

confidence: 91%

Section: Modelling Data Using Linear Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…Therefore, it's crucial to foresee and plan requirements engineers' CFP training need in a quick and correct manner. A recent paper points out that CFP training need should be represented more in higher education [6]. We think training is also critical in the workplace setting and REs should be continually developed in CFP competence when need arises.…”

Section: Introductionmentioning

confidence: 99%

Using Data Mining to Identify COSMIC Function Point Measurement Competence

Bağrıyanık

Karahoca

2018

IJECE

View full text Add to dashboard Cite

Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.

show abstract

Can Software Metrics Be Unified?

Mshelia

Apeh

2019

Computational Science and Its Applications – ICCSA 2019

View full text Add to dashboard Cite

Measurement of Software Size: Advances Made by the COSMIC Community

Cited by 8 publications

References 10 publications

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

Using Data Mining to Identify COSMIC Function Point Measurement Competence

Can Software Metrics Be Unified?

Contact Info

Product

Resources

About