<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it readable and understandable for professionals. The readability of code has been a great concern for programmers and other technical people in development team because it can have a great influence on software maintenance. A lot of research has been done to measure the influence of program constructs on the code readability but none has placed the highly influential constructs together to predict the readability of a code snippet. In this article, we propose a novel framework using statistical modeling that extracts important features from the code that can help in estimating its readability. Besides that using multiple correlation analysis, our proposed approach can measure dependencies among di erent program constructs. In addition, a multiple regression equation is proposed to predict the code readability. We have automated the proposals in a tool that can do the aforementioned estimations on the input code. Using those tools we have conducted various experiments. The results show that the calculated estimations match with the original values that show the effectiveness of our proposed work. Finally, the results of the experiments are analyzed through statistical analysis in SPSS tool to show their significance.</p>
</abstract>

In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it readable and understandable for professionals. The readability of code has been a great concern for programmers and other technical people in development team because it can have a great influence on software maintenance. A lot of research has been done to measure the influence of program constructs on the code readability but none has placed the highly influential constructs together to predict the readability of a code snippet. In this article, we propose a novel framework using statistical modeling that extracts important features from the code that can help in estimating its readability. Besides that using multiple correlation analysis, our proposed approach can measure dependencies among di erent program constructs. In addition, a multiple regression equation is proposed to predict the code readability. We have automated the proposals in a tool that can do the aforementioned estimations on the input code. Using those tools we have conducted various experiments. The results show that the calculated estimations match with the original values that show the effectiveness of our proposed work. Finally, the results of the experiments are analyzed through statistical analysis in SPSS tool to show their significance.

Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it...

Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling

Pubblicato online: 17 giu 2021

Pagine: 127 - 145

Ricevuto: 21 gen 2020

Accettato: 27 apr 2021

DOI: https://doi.org/10.2478/fcds-2021-0009

Parole chiave
Code Readability, Program Constructs, Code Readability Metrics, Statistical Modeling, Code Readability Prediction

© 2021 Aisha Batool et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling

Pubblicato online: 17 giu 2021

Pagine: 127 - 145

Ricevuto: 21 gen 2020

Accettato: 27 apr 2021

DOI: https://doi.org/10.2478/fcds-2021-0009

Parole chiaveCode Readability, Program Constructs, Code Readability Metrics, Statistical Modeling, Code Readability Prediction

© 2021 Aisha Batool et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Code Readability, Program Constructs, Code Readability Metrics, Statistical Modeling, Code Readability Prediction