A model for predicting the probability of code beauty

Session

Computer Science and Communication Engineering

Description

Software maintenance is one of the most expensive phases of the software development life cycle. This cost increases more if maintenance is performed on poorly written code (less aesthetic). There exist a set of code writing patterns that developers need to follow to write good looking code. However, coding conforms ‘rules’ is not always possible. During software evolution, code goes through different changes, which are the main reasons for breaking rules of beautiful code. In this paper, we propose an AI (artificial intelligence) based model which will measure the beauty of a written code. The model is built on a set of code- based features that are used to assign the probability of being a beautiful code.

Keywords:

Machine learning, Code beauty, Software maintenance

Session Chair

Bertan Karahoda

Session Co-Chair

Besnik Qehaja

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-437-96-7

Location

Lipjan, Kosovo

Start Date

31-10-2020 10:45 AM

End Date

31-10-2020 12:30 PM

DOI

10.33107/ubt-ic.2020.512

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Oct 31st, 10:45 AM Oct 31st, 12:30 PM

A model for predicting the probability of code beauty

Lipjan, Kosovo

Software maintenance is one of the most expensive phases of the software development life cycle. This cost increases more if maintenance is performed on poorly written code (less aesthetic). There exist a set of code writing patterns that developers need to follow to write good looking code. However, coding conforms ‘rules’ is not always possible. During software evolution, code goes through different changes, which are the main reasons for breaking rules of beautiful code. In this paper, we propose an AI (artificial intelligence) based model which will measure the beauty of a written code. The model is built on a set of code- based features that are used to assign the probability of being a beautiful code.