scholarly journals O PAPEL DOS DESENVOLVEDORES NOS CODE SMELLS EM PROJETOS OPEN SOURCE

2021 ◽  
Author(s):  
Bernardo Oliveira Rosa
Keyword(s):  
Open Source ◽  
Code Smells ◽  
Software Code ◽  
Para Determinar

Este trabalho visa contribuir com evidências experimentais para a discussão sobre CodeSmell (FOWLER, 1999) e a utilidade real de sua identificação e estudo para a indústriade software. Code smells, ou, numa tradução literal, “mal cheiros de código”, é umconceito utilizado na Programação Orientada a Objetos para determinar se um projetopode vir a ter problemas no sua evolução.

scholarly journals Implementation and Analysis of a Refactoring Tool for Detecting Code Smells

2013 ◽  
Vol 6 (1) ◽  
pp. 242-247 ◽  
Author(s):  
Amandeep Kaur ◽  
Himanshi Raperia
Keyword(s):  
Software Development ◽  
Internal Structure ◽  
Clustering Algorithm ◽  
Code Smells ◽  
Efficient Code ◽  
Clustering Approach ◽  
Code Smell ◽  
Software Code

Software development is a field which is in action for decades. Preparing code for Software is not a difficult task, but preparing an efficient code is complicated one. To change the code is to make internal structure of the code easier to understand and economic to modify, without changing the behavior and desired response. More changes will make software patchy. No Software is free from smells especially the patchy one. Lots of work has been done for detecting and removing a few of the smells (Refactoring) from code. In this paper our main focus will be on tool SCSD (Software Code Smell Detector) developed, uses a bit classification, clustering approach with K-mean Clustering Algorithm to detect the code smells, which can implement completely different architecture if it discovers smell. 

scholarly journals Toma de decisiones en el sector turismo mediante el uso de Sistemas de Información Geográfica e inteligencia de negocios

2020 ◽  
Vol 38 (2) ◽  
pp. 160-173
Author(s):  
Carlos Fernando Barrera-Narváez ◽  
Juan Sebastián González-Sanabria ◽  
Gustavo Cáceres-Castellanos
Keyword(s):  
Open Source ◽  
Revisión Sistemática ◽  
Open Source Gis ◽  
Toma De Decisiones ◽  
Para Determinar ◽  
Sistemas De Informacion

Actualmente, la inteligencia de negocios está presente en cualquier proceso de análisis de datos, principalmente, en casos en los que se evalúa la viabilidad de mercados, la inclusión de nuevos productos o la actividad que permite conocer los hábitos de consumo de las personas, lo cual muestra ventajas evidentes.  El sector turístico tiene como fin proyectar y potencializar a las regiones, según las necesidades o intereses de las personas, por lo que los modelos de inteligencia de negocios son de fundamental utilidad para cumplir con dicho objetivo. Adicionalmente, al integrar a la inteligencia de negocios, aspectos relacionados con el espacio para determinar la cercanía o el desplazamiento de las personas entre regiones, mediante el uso de Sistemas de Información Geográfica (SIG), se puede obtener una representación visual del estado del turismo en una región de interés e incluso evaluar la integración de regiones cercanas y ofrecer planes en conjunto para tener beneficios y progreso conjunto. Este artículo presenta una revisión sistemática de literatura sobre el uso de sistemas de información geográfica y de inteligencia de negocios para la toma de decisiones apoyadas en el sector turismo; para lo cual se analizaron artículos publicados durante 2015-2019 en las bases de datos Scopus e IEEE. Se obtuvo como resultado que durante dicho periodo la tendencia en investigaciones relacionadas con la temática de uso de los GIS en turismo ha sido creciente. Se concluye que gracias a los avances tecnológicos con el uso de las WebGIS y Open Source GIS, los gerentes de empresas turísticas han podido construir sistemas de Información de Recursos Turísticos.

scholarly journals Restructuring with Moora and Measuring Code Smells

2019 ◽  
Vol 8 (12) ◽  
pp. 1817-1820
Keyword(s):  
Structural Modeling ◽  
High Accuracy ◽  
Code Smells ◽  
Ripple Effects ◽  
Priority Method ◽  
Code Maintenance ◽  
Software Code ◽  
Pairwise Analysis ◽  
Optimized Code ◽  
The Way

The paper presents measuring various code smells by finding critical code smells and thereby concentration is increased in those parts through Structural Modeling for arranging those code smells. Arranging the code smells in the way that they will not produce a new smell on their detection and removal is very necessary. Structural modeling helps in clarifying Interrelationship among these code smells. The code smells that contains high driving effects are ordered as optimized code which resulted in the increase in the overall code maintenance of the software code which will be used afterwards for achieving the concept of re-usability. In addition to this we have added a technique for restructuring technology for the purpose to achieve high accuracy. It involves more objectives related to the performance and the code smells are implemented with the concept called as pairwise analysis based on the priority method. Pairwise analysis based on the weights attained by the bad smells provides a better optimized results since more problematic areas are neglected here. This work gives optimized results for the process of overall code maintainability by applying restructuring before the refactoring process with Fuzzy technique and it is followed by finding the code smells which results in high ripple effects and then removing them. Still more research ideologies are needed for removing the bad smells in the code.

scholarly journals Ten simple rules on writing clean and reliable open-source scientific software

2021 ◽  
Vol 17 (11) ◽  
pp. e1009481
Author(s):  
Haley Hunter-Zinck ◽  
Alexandre Fioravante de Siqueira ◽  
Váleri N. Vásquez ◽  
Richard Barnes ◽  
Ciera C. Martinez
Keyword(s):  
Software Development ◽  
Open Source ◽  
Best Practice ◽  
Scientific Research ◽  
Development Project ◽  
Scientific Software ◽  
Software Development Project ◽  
Code Unit ◽  
Software Code ◽  
Unit Tests

Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 “rules” centered on 2 best practice components: clean code and testing. These 2 areas are relatively straightforward and provide substantial utility relative to the learning investment. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load for both the initial developer and subsequent contributors; this allows developers to concentrate on core functionality and reduce errors. Clean coding styles make software code more amenable to testing, including unit tests that work best with modular and consistent software code. Unit tests interrogate specific and isolated coding behavior to reduce coding errors and ensure intended functionality, especially as code increases in complexity; unit tests also implicitly provide example usages of code. Other forms of testing are geared to discover erroneous behavior arising from unexpected inputs or emerging from the interaction of complex codebases. Although conforming to coding styles and designing tests can add time to the software development project in the short term, these foundational tools can help to improve the correctness, quality, usability, and maintainability of open-source scientific software code. They also advance the principal point of scientific research: producing accurate results in a reproducible way. In addition to suggesting several tips for getting started with clean code and testing practices, we recommend numerous tools for the popular open-source scientific software languages Python, R, and Julia.

Investigation of the Software Code Vulnerabilities' Impact on the Popularity of Open Source Software Projects

2021 ◽  
Vol 14 (3) ◽  
pp. 58-69
Author(s):  
Madanjit Singh ◽  
Munish Saini ◽  
Manevpreet Kaur
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Evolution ◽  
Source Code ◽  
Project Managers ◽  
Software Projects ◽  
Software Vulnerabilities ◽  
Software Code ◽  
Change Requests ◽  
Increasing Trends

This paper has statically investigated the source code of open source software (OSS) projects to uncover the presence of vulnerabilities in the code. The conducted research emphasizes that the presence of vulnerabilities has adverse effects on the overall software quality. The authors found the increasing trends in the vulnerabilities as the lines of code (LOC) increases during the software evolution. This signifies the fact that the addition of new features or change requests into the OSS project may cause an increase in vulnerability. Further, the relation between software vulnerabilities and popularity is also examined. This research does not find the existence of any relationship among software vulnerabilities and popularity. This research will provide significant implications to the developers and project managers to better understand the present state of the software.

scholarly journals Engrossing Prosecution of Code Smells Type Identification and Rectification using Machine Learning AdaBoost Classifier

2020 ◽  
Vol 9 (4) ◽  
pp. 624-631
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
False Positive Rate ◽  
Source Code ◽  
Minimum Cost ◽  
Space Complexity ◽  
Identification Accuracy ◽  
Code Smells ◽  
Code Smell ◽  
Software Code

Software code smells are the structural features which reside in a software source code. Code smell detection is an established method to discover the problems in source code and reorganize the inner structure of object-oriented software for improving the quality of such software, particularly in terms of maintainability, reusability and cost minimization. The developer identified where the code smell is identified and rectified within a system is a major challenging issue. The various code smell detection technique has been designed but it failed to classify the code type and minimum rectification cost. In order to perform classification with minimum cost, an efficient technique called Machine Learning Ada-Boost Classifier (MLABC) technique is introduced. The MLABC technique improves the software quality by identifying and rectifying the different types of software code smell in source code. Initially, MLABC technique uses decision tree as base classifier to identify the code smell type. The decision tree is used to classify the code smell type based on the certain rule. After that, the base classifiers are combined to make a strong classifier using adaboost machine learning technique. The output of strong classifier is used to identify the code smell type. Finally, the code smell type rectification is performed by applying the refactoring technique where the code smell is identified with minimum cost and space complexity. Experimental results shows that the proposed MLABC technique improves the software code quality in terms of code smell type identification accuracy, false positive rate, code smell type rectification cost and space complexity with the source code

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