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An Empirical Study of Correlations between Function Points and Software Defects
Masood Uzzafer 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.7
Software defects prediction research is converging on the use of function point elements for software defect predictions. Studies on the nature and behavior of the function point elements are expanding. Previous studies have analyzed the correlation between the function point elements. This paper presents correlation analysis between function point elements and the software defects. It is observed that external input count and external inquiry count function point elements show some correlation with software defects. Different data subsets were analyzed, where 4GL projects shows strong correlation with defects over 3GL and ApG/other projects, while enhancement software projects show more correlation with defects over new software projects.
Bootstrap Correlation Analysis of Function Point Elements
Masood Uzzafer 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.3
This research work investigates the correlation of software function point elements using bootstrap simulation. The correlation of software function point elements plays an important role in understanding the software size; the correlation among function point elements suggests that they measure the same attribute of a software project. Bootstrapping is an effective method to study the statistical properties of correlation coefficients; bootstrap produces a histogram of the possible values of correlation coefficients, which helps to understand the range and spread of the correlation among different function point elements, rater then generating a single point estimate of the correlation.
Measuring the Risk of Software Projects
Masood Uzzafer 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.11
The risk of software projects is measured in terms of cost that is needed to abate the risk. Traditional practice to measure the risk of software projects uses risk exposure; however, risk measure cannot quantify the risk beyond the expected value of cost. Software project managers are keen to quantify the risk based on a certain probability which is beyond the expected value of the cost. This research work presents a model to measure the risk based on certain probability beyond the expectation. A case-study validates that proposed model shows an improvement in the measurement of risk of real software projects compared to the actual risk of software projects.