Structural Complexity Framework And Metrics For Analyzing The Maintainability Of Sassy Cascading Style Sheets

ABSTRACT

One of the most popular languages in the web domain is Cascading Style Sheet (CSS).

The language has evolved over time with the latest development being the introduction

of CSS preprocessors which has made it possible to write CSS codes in a faster and

efficient way. Therefore, the migration from CSS to CSS preprocessors by the frontend

developers has been tremendous. There are several CSS preprocessors available

in the industry with the Syntactically Awesome Style Sheets (SASS) becoming one of

the most preferred preprocessors. This elevation of SASS is as a result of influence by

its new syntax SCSS (Sassy Cascading Style Sheets) which is closer to CSS syntax.

Although SCSS is very promising, it has inherent complexity which keeps increasing

with time as a result of maintenance practices. The Entity-Attribute-Metric (EAM)

model was used to describe the process followed to identify SCSS metrics while the

Boehm model was used to identify the maintainability sub-characteristics. In addition,

the Muketha’s structural attributes classification framework was extended so as to

develop the SCSS structural attributes classification framework. The measurement of

software complexity via software metrics for different software’s and software

paradigms has continued to gain grounds over the years. There exists several structural

CSS metrics but they cannot be directly applied to SCSS because SCSS has richer

features than CSS. In addition, there is no existing framework that can be used to guide

the definition of SCSS structural complexity metrics. To close the gaps identified, the

researcher developed an SCSS complexity attributes classification framework which

was validated through an expert opinion survey. This study proposed a suite of SCSS

structural complexity metrics which were theoretically validated via Weyuker’s

properties and Kaner framework. In addition, a tool was developed to automate the

collection and computation of metric values. The data collected was analyzed through

descriptive statistics (frequencies, mean and standard deviation) and inferential

statistics (Spearman’s rho, ANOVA tests, and principle component analysis).

Empirical studies by way of experimentation were conducted and all the proposed

metrics strongly correlated with the three aspects of maintainability, namely,

understandability, modifiability, and testability. Additionally, the metrics were found

to be important for the measurement of SCSS complexity. The findings of this study

show that all the proposed metrics can serve as maintainability predictors for SCSS.