Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

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Author: search.filters.author.Kaur, SharanpreetSubject: search.filters.subject.Refactoring

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    Object Oriented Metrics Based Empirical Model for Predicting �Code Smells� in Open Source Software
    (Springer, 2023-01-03T00:00:00) Kaur, Sharanpreet; Singh, Satwinder
    Refactoring is a technique which involves the change in internal layout of software system while keeping external features same as before. It aims at identification of smelly areas in code in order to apply patch. The presence of these smelly areas in code is also called �Code Smells� generally known as �Bad Smells.� Under the present study, the statistical relationship between software metrics, code smells and faults is investigated in open source systems. Eight versions of two open source projects � Eclipse and Tomcat are investigated in order to identify the effect on classes subject to code smells and faults than other classes. It was observed that in almost all releases of Tomcat, a good perceptiveness was revealed by area under ROC curve. While an excellent perceptiveness was collected by class level code smells for Eclipse, whereas a fair to good discrimination is generated by method level code smells by area under ROC curve. The results obtained from the study showed that software metrics model for smelly classes is helpful in predicting classes. � 2023, The Institution of Engineers (India).
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    Bug Classification Depend Upon Refactoring Area of Code
    (Springer, 2023-01-05T00:00:00) Singh, Satwinder; Jalal, Maddassar; Kaur, Sharanpreet
    Due to rapid development in the software industry, various software is being developed, which compromises the software quality. As long as time passes, the software which compromises with the quality starts showing bugs which adversely affect the working of the software system. Sometimes software undergo repeated addition of functionality, so various classes and functions in software systems become bulky and cause smells in code. Code smells also degrade the software quality, increasing the software system's maintenance cost. So these bad smells should be appropriately detected and eliminated from the software system well on time. With the identification of smells in code, one could easily map the refactoring area of code to improve the code. Improving the code will help in preventive maintenance to identify the bugs quickly. To solve this problem, the current study will focus on the five types of code smells: Data Class, God Class, Feature Envy, Refuse Parent Request and Brain method for identification of bugs in the code. The data set used for this study includes the smell extraction and identification from two versions of Eclipse, i.e. Eclipse 3.6 and Eclipse 3.7, which are renowned open soured industry size software systems. Later on, supervised machine learning classifiers j48, Random Forest, and Naive Bayes are used to identify the bugs in code at the class level. The results show that j48 performed well and provides high accuracy for the identification of bugs with the help of bad smells. � 2023, The Institution of Engineers (India).