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Finding Bad Code Smells with Neural Network Models

Dong Kwan Kim
2017 International Journal of Electrical and Computer Engineering (IJECE)  
The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories.  ...  In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects  ...  Figure 1 shows the overall workflow of the proposed code smell detection system to find code smells in Java programs.The proposed detection system uses the twenty Java projects which are downloaded from  ... 
doi:10.11591/ijece.v7i6.pp3613-3621 fatcat:jjrvkrqwb5a2pfxbdhsfp3ig4m

Table of Contents

2018 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)  
Variants on the Retrieval Effectiveness of a Recovery Tool:Exploring the Use of Rapid Type Analysis for Detecting the Dead Method Smell in Java Code Model-Based Testing of Software-Based System Functions  ...  Analysis Fault-Prone Java Method Analysis Focusing on Pair of Local Variables with Confusing Names Keiichiro Tashima (Ehime University, Japan), Hirohisa Aman (Ehime University, Japan), Sousuke Amasaki  ... 
doi:10.1109/seaa.2018.00004 fatcat:x7vxgfhbdjb6hlnxslz5n5j7xm

Detecting Antipatterns in Android Apps

Geoffrey Hecht, Romain Rouvoy, Naouel Moha, Laurence Duchien
2015 2015 2nd ACM International Conference on Mobile Software Engineering and Systems  
Thus, the automatic detection of antipatterns is an important activity that eases both maintenance and evolution tasks.  ...  We validate the effectiveness of our approach on a set of popular mobile apps downloaded from the Google Play Store.  ...  Concerning Android, Verloop [38] used popular Java refactoring tools, such as PMD [7] or Jdeodorant [35] to detect code smells, like large class or long method in open-source software.  ... 
doi:10.1109/mobilesoft.2015.38 dblp:conf/icse/HechtRMD15 fatcat:3auw45mxwvdcnjgplnsh4xl3we


Daniel Maia, Marco Couto, João Saraiva, Rui Pereira
2020 Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Workshops  
This plugin uses a robust, well defined, and extendable smell catalog based on current green software literature, with each smell defining the potential energy savings.  ...  This metric is a reflection on the implied cost, in terms of energy consumption over time, of choosing an energy flawed software implementation over a more robust and efficient, yet time consuming, approach  ...  ACKNOWLEDGMENTS This work is financed by National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020.  ... 
doi:10.1145/3417113.3422999 fatcat:dgpxr4nyr5g5jmsf4bce272jce

Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

Khalid Alkharabsheh, Sadi Alawadi, Yania Crespo, M. Esperanza Manso, Jose A. Taboada Gonzalez
2021 IEEE Access  
Work co-funded by the Spanish ministry of economics and competitivity (Ministerio de Economía y Competitividad) through project MAGIST-ELA: Large Scale Geo-processing for Exploratory and Learning Analytics  ...  (PID2019-105221RB-C42) of the R+D+i state program oriented to society challenges (Programa Estatal de I+D+i Orientada a los Retos de la Sociedad).  ...  as Duplicate Code or Dead Code.  ... 
doi:10.1109/access.2021.3123123 fatcat:5mxqfqcluvfpbdevnimdweptwe

History-driven Fix for Code Quality Issues

Jiangtao Xue, Xinjun Mao, Yao Lu, Yue Yu, Shangwen Wang
2019 IEEE Access  
To ensure the internal code quality of contributions in open source software (OSS) communities, static analysis tools (e.g.  ...  We collected 5,047,678 CQI isstances, 31,013 fixes of the CQIs that were detected by SonarQube from 206 GitHub projects and mined 68 common Fix Patterns for 56 CQI types.  ...  ACKNOWLEDGMENT The authors would like to thank developers for participating our live study in GitHub.  ... 
doi:10.1109/access.2019.2934975 fatcat:44lpo6onzjhfrl2fh6umdr5x2i

Research on Third-Party Libraries in AndroidApps: A Taxonomy and Systematic LiteratureReview [article]

Xian Zhan, Tianming Liu, Lingling Fan, Li Li, Sen Chen, Xiapu Luo, Yang Liu
2021 arXiv   pre-print
Although there are already many studies for characterizing third-party libraries, including automated detection, security and privacy analysis of TPLs, TPL attributes analysis, etc., what strikes us odd  ...  Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword.  ...  Some methods can be deleted in dead code removal so these tools are not resilient to dead code removal.  ... 
arXiv:2108.03787v1 fatcat:jnj4kvlkuzg3hbgy4pl5wpvle4

Managing Technical Debt in Software Engineering (Dagstuhl Seminar 16162)

Paris Avgeriou, Philippe Kruchten, Ipek Ozkaya, Carolyn Seaman, Marc Herbstritt
2016 Dagstuhl Reports  
This report documents the program and outcomes of Dagstuhl Seminar 16162, "Managing Technical Debt in Software Engineering."  ...  We summarize the goals and format of the seminar, results from the breakout groups, a definition for technical debt, a draft conceptual model, and a research road map that culminated from the discussions  ...  This work has applied detection of "code smells" (low internal code quality), coupling and cohesion, and dependency analysis to identify technical debt.  ... 
doi:10.4230/dagrep.6.4.110 dblp:journals/dagstuhl-reports/AvgeriouKOS16 fatcat:h4kxyter2fcbthuqozlwakcfsa

Refactoring: Improving the Design of Existing Code [chapter]

Martin Fowler
2002 Lecture Notes in Computer Science  
You may not find the exact smell you can detect, but hopefully it should point you in the right direction. Duplicated Code Number one in the stink parade is duplicated code.  ...  Use Extract Method and Move Method to move the code to the host object. For Java 2, you are done with that. For Java 1.1, however, clients may prefer to use an enumeration.  ...  Extract Subclass A class has features that are used only in some instances. Create a subclass for that subset of features. Duplicate code is one of the principal bad things in systems.  ... 
doi:10.1007/3-540-45672-4_31 fatcat:4ija3tn2nnbctk5atyreyjlzxu

Maintaining Smart Contracts on Ethereum: Issues, Techniques, and Future Challenges [article]

Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiaohu Yang
2021 arXiv   pre-print
(ii) What are the current maintenance-related methods used for smart contracts?  ...  In this study, we focus on the maintenance-related concerns of the post-deployment of smart contracts. Smart contracts are self-executed programs that run on a blockchain.  ...  Refactoring the code to remove code smells in software to increase its robustness is a typical preventive maintenance method.  ... 
arXiv:2007.00286v2 fatcat:g5wr7k2edjbw3dpqcfloyvnqrq

Evolution of Web Systems [chapter]

Holger M. Kienle, Damiano Distante
2013 Evolving Software Systems  
For each of these dimensions we introduce the state-of-the-art in the techniques and tools that are currently available.  ...  In order to place current evolution techniques into context, we also provide a survey of the different kinds of web systems as they have emerged, tracing the most important achievements of web systems  ...  Acknowledgements Many thanks to all the reviewers of this chapter, who provided us with excellent suggestions that greatly aided us in preparing the final version of this manuscript.  ... 
doi:10.1007/978-3-642-45398-4_7 fatcat:bdhm65sxyfgbfmp6pemve7o4mm

A Unified Approach to Architecture Conformance Checking

Andrea Caracciolo, Mircea Filip Lungu, Oscar Nierstrasz
2015 2015 12th Working IEEE/IFIP Conference on Software Architecture  
in a method in A is initialized with an object of type B (Initialized Local Variable). a parameter of a method in A is of type B (Parameter). the return type of a method in A is of type B (Return Type  ...  We analyzed version 6.0 beta 1, consisting of 485 Java files and a total of 28,000 non-comment lines of code. During our analysis we detected 44 package cycles.  ... 
doi:10.1109/wicsa.2015.11 dblp:conf/wicsa/CaraccioloLN15 fatcat:p7645eykmfgc3aopopwwmwplu4

User Interfaces for Mobile Augmented Reality Systems [article]

Steve Feiner
2003 International Conference on Vision, Video and Graphics  
This talk provides an overview of work that explores user interface design issues for mobile augmented reality systems, which use tracked see-through and hear-through displays to overlay virtual graphics  ...  What should user interfaces look like when they become an integral part of how we experience the world around us?  ...  The mapping between symbolic and specific colors is hardcoded in Java code for now.  ... 
doi:10.2312/vvg.20031017 dblp:conf/vvg/Feiner03 fatcat:5mztekgvszg33ag6lyoyvxkit4


2020 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)  
This article explores the transmit energy of a linear amplifier used in medical Doppler driver systems.  ...  The latest research from Rockefeller University in the United States in 2014 found that humans can smell one trillion fragrances, and smell is one of the best ways to wake up memory.  ...  In this work, conducted by three partners (UFAM/CETELI, ICTS and ENVISION/TPV), we propose three methods for automated detection of dead pixels.  ... 
doi:10.1109/icce-taiwan49838.2020.9258230 fatcat:g25vw7mzvradxna2grlzp6kgiq

Single-state state machines in model-driven software engineering: an exploratory study

Nan Yang, Pieter Cuijpers, Ramon Schiffelers, Johan Lukkien, Alexander Serebrenik
2021 Empirical Software Engineering  
Context Models, as the main artifact in model-driven engineering, have been extensively used in the area of embedded systems for code generation and verification.  ...  Objective We aim for understanding the phenomenon of using SSSMs in practice as understanding why developers violate the modeling guidelines is the first step towards improvement of modeling tools and  ...  In Section 5 we extend this study to explore the evolutionary aspects of SSSM. We present our method in Section 4.1 The results for RQ1-4 are presented in Sections 4.2, 4.3 and 4.4.  ... 
doi:10.1007/s10664-021-10015-3 fatcat:zan6vbxe6rhxrj2dtumu6cjcom
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