BOHR - Uma Ferramenta para a Identificação de Átomos de Confusão em Códigos Java
Resumo
A atividade de compreensão do código-fonte é fundamental no desenvolvimento e manutenção de software. Nesse contexto, Átomos de Confusão (AC) surgem como a menor porção de código capaz de causar confusão em desenvolvedores nesse processo. Neste artigo, é apresentado o BOHR - The Atoms of Confusion Hunter, uma ferramenta que tem como objetivos: (i) auxiliar a identificação de AC em sistemas Java; (ii) fornecer relatórios sobre a prevalência desses AC; e (iii) fornecer uma API para o desenvolvimento de novos buscadores customizados para a captura de novos AC e, também, melhorias em suas identificações. Nesta primeira versão, o BOHR é capaz de detectar 8 dos 13 tipos de AC apontados por Langhout e Aniche. Uma avaliação preliminar do BOHR foi conduzida em três sistemas reais, de código aberto, largamente adotados pela comunidade de desenvolvimento de softwares Java (Picasso, greenDAO e Socket.IO-client Java). Os resultados iniciais mostraram que o BOHR conseguiu detectar com precisão os AC existentes nesses sistemas analisados, apontando de forma correta os snippets dos AC, assim como o seu tipo, nome da classe que era pertencente e o número da linha de código de sua ocorrência. No total, foram encontrados 105 AC, sendo 37 no Picasso, 25 no greenDAO e 43 no Socket.IO-client Java.
Palavras-chave:
Compreensão de Código, Átomos de Confusão, Análise Estática de Código
Referências
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K.H. Bennett, V.T. Rajlich, and N. Wilde. 2002. Software Evolution and the Staged Model of the Software Lifecycle. Advances in Computers, Vol. 56. Elsevier, 1–54. https://doi.org/10.1016/S0065-2458(02)80003-1
Pierre Carbonnelle. 2021. PYPL PopularitY of Programming Language. https://pypl.github.io/PYPL.html
Fernando Castor. 2018. Identifying confusing code in Swift programs. In Proceedings of the VI CBSoft Workshop on Visualization, Evolution, and Maintenance. ACM.
Benedito de Oliveira, Márcio Ribeiro, José Aldo Silva da Costa, Rohit Gheyi, Guilherme Amaral, Rafael de Mello, Anderson Oliveira, Alessandro Garcia, Rodrigo Bonifácio, and Baldoino Fonseca. 2020. Atoms of Confusion: The Eyes Do Not Lie. In Proceedings of the 34th Brazilian Symposium on Software Engineering (Natal, Brazil) (SBES ’20). Association for Computing Machinery, New York, NY, USA, 243–252. https://doi.org/10.1145/3422392.3422437
Rodrigo Magalhães dos Santos and Marco Aurélio Gerosa. 2018. Impacts of Coding Practices on Readability. In Proceedings of the 26th Conference on Program Comprehension (Gothenburg, Sweden) (ICPC ’18). Association for Computing Machinery, New York, NY, USA, 277–285. https://doi.org/10.1145/3196321.3196342
Felipe Ebert, Fernando Castor, Nicole Novielli, and Alexander Serebrenik. 2017. Confusion Detection in Code Reviews. In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 549–553. https://doi.org/10.1109/ICSME.2017.40
Dan Gopstein, Anne-Laure Fayard, Sven Apel, and Justin Cappos. 2020. Thinking Aloud about Confusing Code: A Qualitative Investigation of Program Comprehension and Atoms of Confusion (ESEC/FSE 2020). Association for Computing Machinery, New York, NY, USA, 605–616. https://doi.org/10.1145/3368089.3409714
Dan Gopstein, Jake Iannacone, Yu Yan, Lois DeLong, Yanyan Zhuang, Martin K.-C. Yeh, and Justin Cappos. 2017. Understanding Misunderstandings in Source Code. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA, 129–139. https://doi.org/10.1145/3106237.3106264
Dan Gopstein, Hongwei Henry Zhou, Phyllis Frankl, and Justin Cappos. 2018. Prevalence of Confusing Code in Software Projects: Atoms of Confusion in the Wild (MSR ’18). Association for Computing Machinery, New York, NY, USA, 281–291. https://doi.org/10.1145/3196398.3196432
Chris Langhout. 2020. Investigating the Perception and Effects of Misunderstandings in Java Code. Master’s thesis. Delft University of Technology.
Chris Langhout and Maurício Aniche. 2021. Atoms of Confusion in Java. arXiv:2103.05424 [cs.SE]
Roberto Minelli, Andrea Mocci, and Michele Lanza. 2015. I Know What You Did Last Summer - An Investigation of How Developers Spend Their Time. In 2015 IEEE 23rd International Conference on Program Comprehension. 25–35. https://doi.org/10.1109/ICPC.2015.12
Stephen O’Grady. 2021. The RedMonk Programming Language Rankings: January 2021. https://redmonk.com/sogrady/2021/03/01/language-rankings-1-21/
Michail Papamichail, Themistoklis Diamantopoulos, and Andreas Symeonidis. 2016. User-Perceived Source Code Quality Estimation Based on Static Analysis Metrics. In 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS). 100–107. https://doi.org/10.1109/QRS.2016.22
Renaud Pawlak, Martin Monperrus, Nicolas Petitprez, Carlos Noguera, and Lionel Seinturier. 2015. Spoon: A Library for Implementing Analyses and Transformations of Java Source Code. Software: Practice and Experience 46 (2015), 1155–1179. https://doi.org/10.1002/spe.2346
Gustavo Pinto, Weslley Torres, Benito Fernandes, Fernando Castor, and Roberto S.M. Barros. 2015. A large-scale study on the usage of Java’s concurrent programming constructs. Journal of Systems and Software 106 (2015), 59–81. https://doi.org/10.1016/j.jss.2015.04.064
Akond Rahman. 2018. Comprehension Effort and Programming Activities: Related? Or Not Related?. In Proceedings of the 15th International Conference on Mining Software Repositories (Gothenburg, Sweden) (MSR ’18). Association for Computing Machinery, New York, NY, USA, 66–69. https://doi.org/10.1145/3196398.3196470
Spencer Rugaber. 1995. Program comprehension. Encyclopedia of Computer Science and Technology 35, 20 (1995), 341–368.
The JUnit Team. 2021. JUnit 5. https://junit.org/junit5/
Xin Xia, Lingfeng Bao, David Lo, Zhenchang Xing, Ahmed E. Hassan, and Shanping Li. 2018. Measuring Program Comprehension: A Large-Scale Field Study with Professionals. IEEE Transactions on Software Engineering 44, 10 (2018), 951–976. https://doi.org/10.1109/TSE.2017.2734091
Publicado
27/09/2021
Como Citar
MENDES, Wendell; VIANA, Windson; ROCHA, Lincoln.
BOHR - Uma Ferramenta para a Identificação de Átomos de Confusão em Códigos Java. In: WORKSHOP DE VISUALIZAÇÃO, EVOLUÇÃO E MANUTENÇÃO DE SOFTWARE (VEM), 9. , 2021, Joinville.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 41-45.
DOI: https://doi.org/10.5753/vem.2021.17216.
