Do code smells hamper novice programming? A controlled experiment on Scratch programs

2016 ◽  
Author(s):  
Felienne Hermans ◽  
Efthimia Aivaloglou
Keyword(s):  
Controlled Experiment ◽  
Code Smells ◽  
Novice Programming

Identifying Code Smells with Collaborative Practices: A Controlled Experiment

2016 ◽  
Author(s):  
Roberto Oliveira ◽  
Bernardo Estacio ◽  
Alessandro Garcia ◽  
Sabrina Marczak ◽  
Rafael Prikladnicki ◽  
...  
Keyword(s):  
Controlled Experiment ◽  
Code Smells ◽  
Collaborative Practices ◽  
Identifying Code

JAVASCRIPT ASYNCHRONOUS PROGRAMMING

2019 ◽  
Vol 128 (2B) ◽  
pp. 5-16
Author(s):  
Tran Thanh Luong ◽  
Le My Canh
Keyword(s):  
Design Patterns ◽  
Source Code ◽  
Object Oriented ◽  
Exception Handling ◽  
Code Smells ◽  
Asynchronous Programming ◽  
Event Driven ◽  
Programming Patterns ◽  
The Impact

JavaScript has become more and more popular in recent years because its wealthy features as being dynamic, interpreted and object-oriented with first-class functions. Furthermore, JavaScript is designed with event-driven and I/O non-blocking model that boosts the performance of overall application especially in the case of Node.js. To take advantage of these characteristics, many design patterns that implement asynchronous programming for JavaScript were proposed. However, choosing a right pattern and implementing a good asynchronous source code is a challenge and thus easily lead into less robust application and low quality source code. Extended from our previous works on exception handling code smells in JavaScript and exception handling code smells in JavaScript asynchronous programming with promise, this research aims at studying the impact of three JavaScript asynchronous programming patterns on quality of source code and application.

A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

2020 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur ◽  
Sushma Jain ◽  
Shivani Goel ◽  
Gaurav Dhiman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Software Quality ◽  
Empirical Studies ◽  
Statistical Testing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Code Smells ◽  
Detection Techniques ◽  
Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

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