Browsing by Author "Kaur, Sharanpreet"

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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).
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    Improving the Quality of Open Source Software
    (wiley, 2023-02-08T00:00:00) Kaur, Sharanpreet; Singh, Satwinder
    This study aims at development of generating metrics based code smells prediction to improve the software quality assurance by working at preventive maintenance level. In order to do so, Refactoring is the best solution for identification of smelly areas in the code to reveal the portions which demands patching. It not only increases the life of code but eventually increases the quality of software in long run, where versions of a software are launched one after the other. The empirical model development considered Deep learning based neural network technique for establishing the association between code smells and metrics in the source code of Eclipse which is a Java based application contributing efficiently on the open source platform. A statistical analysis was pre applied on the set of code smells and metrics for finding the connection between the both. Later on, Multi Layer Perceptron model development on four versions of Eclipse has been made. Subsequently Area Under Curve (ROC) has been generated for class & method level code smells. The value of ROC in predicting code smells pointed towards the fact that Neural Network Multi Layer Perceptron model perform fair to good in determining the presence of code smells based on software metrics in Eclipse. Therefore from the results obtained it is concluded that smelly classes are predicted efficiently by software metric based code smells prediction model. The present study will be beneficial to the software development community to locate the refactoring areas and providing resources for testing. The results of empirical study also guide the development community by providing information relative to code smells and its types. The aim of this study is to provide statistical proof of linkage between metrics and code smells. The software metric based prediction � 2023 Scrivener Publishing LLC.
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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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    A systematic literature review: Refactoring for disclosing code smells in object oriented software
    (Ain Shams University, 2016) Singh, Satwinder; Kaur, Sharanpreet
    Context: Reusing a design pattern is not always in the favor of developers. Thus, the code starts smelling. The presence of "Code Smells" leads to more difficulties for the developers. This racket of code smells is sometimes called Anti-Patterns. Objective: The paper aimed at a systematic literature review of refactoring with respect to code smells. However the review of refactoring is done in general and the identification of code smells and anti-patterns is performed in depth. Method: A systematic literature survey has been performed on 238 research items that includes articles from leading Conferences, Workshops and premier journals, theses of researchers and book chapters. Results: Several data sets and tools for performing refactoring have been revealed under the specified research questions. Conclusion: The work done in the paper is an addition to prior systematic literature surveys. With the study of paper the attentiveness of readers about code smells and anti-patterns will be enhanced. ? 2017.