Abstract
The goal of this work is to practically apply methods of empirical engineering software, algorithms for data collection and data analysis. The results include software measurement, analysis and selection of direct and indirect metrics for research and identification of dependencies between direct and indirect metrics. Based on the received results, there were built dependencies between software metrics and software expertise properties were selected by individual variation.For measurement results analysis there were used primary statistical analysis, expert estimations, correlation and regression analysis. Expert estimation is the dominant strategy when estimating software development effort. Typically, effort estimates are over-optimistic and there is a strong over-confidence in their accuracy. Primary data analysis is the process of comprehending the data collected to answer research questions or to support or reject research hypotheses that the study was originally designed to evaluate. Correlation analysis gives possibility to make some conclusions about which metrics and expert estimations are much coupled, and which are not. Regression analysis involves both graphical construction and analytical research and gives an ability to make a conclusion about which metrics and expert estimations are the most coupled. Analyzing regression lines for metrics of normal and nonnormal distributions give an ability to identify pairs of ‘metric – expert estimation’.There have been calculated and measured metrics relations for defining relation of such quality attributes as Understandability and Functionality Completeness. Understandability expresses the clarity of the system design. If the system is well designed, new developers are able to understand easily the implementation details and quickly begin contributing to the project. Functionality Completeness refers to the absence of omission errors in the program and database. It is evaluated against a specification of software requirements that define the desired degree of generalization and abstraction.Relationship between metric and expertise includes building direct relationships between the metric and expertise, indirect metrics and expertise. Additionally, it has been determined whether they have common trends of the relationship between those direct metrics and expert estimates, indirect metrics and expert estimates. The practical results of this work can be applied for software measurements to analyze what changes in the code (affecting given metric) will cause increasing or decreasing of what quality attribute.Manuscript received 10.06.2020
Highlights
One of the main difficulties during software architecture developing is to evaluate the available design options and choose the best ones
In order to make sure that the picked metrics satisfy the expected software quality attributes we can use a series statistical analysis tools like primary statistical analysis, expert estimations, correlation and regression analysis
Tight Class Cohesion (TCC) and Weight of Class (WOC) metrics are the use with normal distribution
Summary
One of the main difficulties during software architecture developing is to evaluate the available design options and choose the best ones. Even after the right decisions were made during initial phase of the system development, it is crucial to control quality of produced changes into the system afterwards. One of the main reason of problems with depicted needs is that developers are unclear what criteria they should use to make design decisions and why. Some developers rely on their previous engineering experience and personal preferences in methods, technologies, tools, and patterns. The problem is that each member of the development team has its preferences, opinions and assumptions. The debate about subjective opinions and preferences can damage the relationship between collea gues but may not necessarily lead to a software architecture optimized to achieve business goals
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