# Internal Quality Evolution of Open-Source Software Systems
## 4 Research Methodology  
Software quality can be easily analyzed through in-depth software evaluation. The current research evaluates the open-source software system for assessing the software quality and to answer following research questions:  
* RQ1: How does open-source software evolution affect the Line of Code (LOC)?
* RQ2: Is there a statistically significant relationship between size and other source code attributes (e.g., complexity, coupling, inheritance, and cohesion)?To answer the first research question, a statistical test named 'Analysis of Variance' (ANOVA) is conducted. ANOVA signifies the set of statistical models and their related estimation procedures that are used for analyzing the differences among means. In particular, it is the statistical method of finding out if the results of the experiment are significant. Moreover, it also assists in identifying if two or more means are equal; hence, generalizing the t-test beyond two means. It indicates that ANOVA is used for determining the equivalence of sample means, for three or more populations. It has been established that the variances, occurred in data, are mainly due to two reasons, i.e., 'just by chance' or'specific'. In this regard, ANOVA assists in analyzing if the cause of variance is 'just by chance' or'specific'. Likewise, the present study has used ANOVA analysis to examine if the difference in LOC between the systems is significant.  
Moreover, post-hoc analysis is also conducted using the Least Significant Difference (LSD) method to carry out multiple comparisons and defining where exactly the difference occurred. In particular, the LSD method--proposed by [PERSON]--is adopted for creating confidence intervals for all pairwise differences between factor level means, drawn from ANOVA. During this process, the individual error rate is controlled to the specified significance level. Afterwards, the LSD method of [PERSON] used the number of comparisons and individual error rate for calculating the simultaneous confidence level. It is important to note that the simultaneous confidence level indicates that all confidence intervals possess the true difference. Moreover, it is also worth noting that the family error rate must be taken into consideration, specifically while conducting multiple comparisons. It is mainly due to the fact that the possibility of occurrence of Type I error is higher during a series of comparisons than the rate of error that is associated with single comparisons.  
Another way of analyzing the means is to statistically model them rather than simply describing them as they appear in the data. Marginal means are means that are extracted from a statistical model and represent the average response variable for each level of the predictor variable.  
In a multiple regression model, multicollinearity can be understood as the phenomenon in which one predictor can be linearly predicted from the others with higher accuracy levels. In other words, multicollinearity is the extent to which any variable effect can be predicted by other variables. It has been established that multicollinearity takes place when the model includes multiple factors, with a strong correlation. Afterward, changes in the other variables are also shown as a function of the version and Java systems and it is usually represented in the form of a graph.  
To answer the second research questions, correlation analysis and regression analysis are conducted. The rationale of conducting correlation analysis is that it helps in studying the variation between two or more independent variables that eventually assists in identifying the extent of correlation between them. On the other hand, regression analysis allows the analysis of the relationship between independent and dependent variables. In particular, it allows the assessment of the strength of the relationship between the variables that is later used for the modelling of the relationship that exists between the variables.