An Approach to Suggest Code Smell Order for Refactoring

2019 ◽  
pp. 250-260 ◽  
Author(s):  
Thirupathi Guggulothu ◽  
Salman Abdul Moiz
Keyword(s):  
Code Smell

A Study on Different Tools for Code Smell Detection

2018 ◽  
Vol 6 (7) ◽  
pp. 762-764
Author(s):  
S.James Benedict Felix ◽  
Viji Vinod
Keyword(s):  
Code Smell

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.

Developer-Driven Code Smell Prioritization

2020 ◽  
Author(s):  
Fabiano Pecorelli ◽  
Fabio Palomba ◽  
Foutse Khomh ◽  
Andrea De Lucia
Keyword(s):  
Code Smell

scholarly journals AUTOMATSKA DETEKCIJA INDIKATORA LOŠE DIZAJNIRANOG KODA BAZIRANA NA ISTORIJI PROMENA KODA

2020 ◽  
Vol 36 (01) ◽  
pp. 43-46
Author(s):  
Simona Prokić
Keyword(s):  
Code Smells ◽  
Code Smell

Kod niskog kvaliteta sadrži strukture (code smells) koje otežavaju održavanje i dalji razvoj softvera. U ovom radu predstavljen je model zasnovan na mašinskom učenju za automatsku detekciju indikatora loše dizajniranog koda (code smell-ova) baziranu na istoriji promena koda. Ulaz modela su vrednosti metrika softverskog koda, izračunate u n revizija za posmatrani isečak koda. Izlaz iz modela je labela koja označava da li posmatrani isečak koda sadrži indikator loše dizajniranog koda ili ne. Studija slučaja izvršena je na detekciji klasa sa mnogo odgovornosti (God Class). Predloženi su koraci za poboljšanje i dalji razvoj arhitekture.

scholarly journals Automatic detection of Long Method and God Class code smells through neural source code embeddings

2021 ◽  
Author(s):  
Aleksandar Kovačević ◽  
Jelena Slivka ◽  
Dragan Vidaković ◽  
Katarina-Glorija Grujić ◽  
Nikola Luburić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Negative Impact ◽  
Source Code ◽  
Systematic Evaluation ◽  
Small Scale ◽  
Code Smells ◽  
Code Metrics ◽  
Code Smell ◽  
F Measure

<p>Code smells are structures in code that often have a negative impact on its quality. Manually detecting code smells is challenging and researchers proposed many automatic code smell detectors. Most of the studies propose detectors based on code metrics and heuristics. However, these studies have several limitations, including evaluating the detectors using small-scale case studies and an inconsistent experimental setting. Furthermore, heuristic-based detectors suffer from limitations that hinder their adoption in practice. Thus, researchers have recently started experimenting with machine learning (ML) based code smell detection. </p><p>This paper compares the performance of multiple ML-based code smell detection models against multiple traditionally employed metric-based heuristics for detection of God Class and Long Method code smells. We evaluate the effectiveness of different source code representations for machine learning: traditionally used code metrics and code embeddings (code2vec, code2seq, and CuBERT).<br></p><p>We perform our experiments on the large-scale, manually labeled MLCQ dataset. We consider the binary classification problem – we classify the code samples as smelly or non-smelly and use the F1-measure of the minority (smell) class as a measure of performance. In our experiments, the ML classifier trained using CuBERT source code embeddings achieved the best performance for both God Class (F-measure of 0.53) and Long Method detection (F-measure of 0.75). With the help of a domain expert, we perform the error analysis to discuss the advantages of the CuBERT approach.<br></p><p>This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. A secondary contribution of our study is the systematic evaluation of the effectiveness of multiple heuristic-based approaches on the same large-scale, manually labeled MLCQ dataset.<br></p>

scholarly journals Revealing Developers’ Arguments on Validating the Incidence of Code Smells: A Focus Group Experience

2021 ◽  
Author(s):  
Luis Felipi Junionello ◽  
Rafael de Mello ◽  
Roberto Oliveira ◽  
Leonardo Sousa ◽  
Alexander López ◽  
...  
Keyword(s):  
Focus Group ◽  
Tacit Knowledge ◽  
Automated Detection ◽  
Group Session ◽  
Focus Group Session ◽  
Code Smells ◽  
Identifying Code ◽  
Code Smell ◽  
Human Validation ◽  
Group Experience

Identifying code smells is considered a subjective task. Unfortunately, current automated detection tools cannot deal with such subjectivity, requiring human validation. Developers tend to follow different, albeit complementary, strategies when validating the identified smells. Intending to find out developers' arguments when validating the incidence of code smells, we conducted a focus group session with developers familiar with identifying code smells. We distributed them among two groups, in which they had to argue about the incidence of a code smell: either accepting or rejecting its presence. Based on their arguments, we compiled a set of general heuristics that developers follow when validating smells. We then used these heuristics for composing validation items. We understand that the set of validation items proposed may support developers in reflecting on the incidence of code smells. However, further studies are needed for reaching a more comprehensive and optimized set. The experience of this study reveals that conducting focus group sessions is helpful to emerge the tacit knowledge of developers when validating code smells.

scholarly journals Design of testing framework for code smell detection (OOPS) using BFO algorithm

2018 ◽  
Vol 7 (2.27) ◽  
pp. 161
Author(s):  
Pratiksha Sharma ◽  
Er. Arshpreet Kaur
Keyword(s):  
Software Metrics ◽  
Software Evolution ◽  
False Positive Rate ◽  
Optimization Technique ◽  
Research Work ◽  
Object Oriented Programming ◽  
Detection Methods ◽  
Major Drawback ◽  
Code Refactoring ◽  
Code Smell

Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.    

scholarly journals Does your configuration code smell?

2016 ◽  
Author(s):  
Tushar Sharma ◽  
Marios Fragkoulis ◽  
Diomidis Spinellis
Keyword(s):  
Code Smell
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