scholarly journals Methods and tools for teaching parallel and distributed computing in universities: a systematic review of the literature

2020 ◽  
Vol 75 ◽  
pp. 04017
Author(s):  
Yuriy Sitsylitsyn
Keyword(s):  
Distributed Computing ◽  
Main Tool ◽  
Computer Hardware ◽  
Review Of The Literature ◽  
Parallel And Distributed Computing ◽  
Sequential Designs ◽  
C Programming Language ◽  
Research On Teaching ◽  
C Programming ◽  
Software Engineers

As computer hardware becomes more and more parallel, there is a need for software engineers who are experienced in developing parallel programs, not only by “parallelizing” sequential designs. Teach students a parallelism in elementary courses in computer science this is a very important step towards building the competencies of future software engineers. We have conducted research on “teaching parallel and distributed computing” and “parallel programming” publications in the Scopus database, published in English between 2008 and 2019. After quality assessment, 26 articles were included in the analysis. As a result, the main tool for teaching parallel and distributed computing is a lab course with a C++ programming language and MPI library.

scholarly journals Towards a systematic approach to manual annotation of code smells

2021 ◽  
Author(s):  
Nikola Luburić ◽  
Simona Prokić ◽  
Katarina-Glorija Grujić ◽  
Jelena Slivka ◽  
Aleksandar Kovačević ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Manual Annotation ◽  
Code Smells ◽  
C Programming Language ◽  
C Programming ◽  
Software Engineers ◽  
Code Smell ◽  
Supporting Tool ◽  
Almost All ◽  
Primary Contribution

<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

scholarly journals Towards a systematic approach to manual annotation of code smells

2021 ◽  
Author(s):  
Nikola Luburić ◽  
Simona Prokić ◽  
Katarina-Glorija Grujić ◽  
Jelena Slivka ◽  
Aleksandar Kovačević ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Manual Annotation ◽  
Code Smells ◽  
C Programming Language ◽  
C Programming ◽  
Software Engineers ◽  
Code Smell ◽  
Supporting Tool ◽  
Almost All ◽  
Primary Contribution

<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

scholarly journals Towards a systematic approach to manual annotation of code smells

2021 ◽  
Author(s):  
Nikola Luburić ◽  
Simona Prokić ◽  
Katarina-Glorija Grujić ◽  
Jelena Slivka ◽  
Aleksandar Kovačević ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Manual Annotation ◽  
Code Smells ◽  
C Programming Language ◽  
C Programming ◽  
Software Engineers ◽  
Code Smell ◽  
Supporting Tool ◽  
Almost All ◽  
Primary Contribution

<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

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