Refactoring‐based learning for fine‐grained lock in concurrent programming course

Zhang, Yang

doi:10.1002/cae.22469

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…This method first analyzes the feature attributes of the program through program static analysis technology, and then uses forest algorithm to recommend the lock mechanism. The recommendation accuracy of this model is as high as 95.1% [22]. Duan et al proposed a model based on probability latent semantic index and integrated probability MF to address the issue of how to achieve a better recommendation experience through service rating.…”

Section: And Yang Proposed An Intelligent Recommendation Algorithmmentioning

confidence: 99%

Design and Implementation of an English Learning System Based on Intelligent Recommendation

Yuan

2024

ITC

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The Internet has led to the rapid development of online education, but it has also caused redundancy in educational information. How to choose appropriate courses from a large number of online education resources has become a major problem for current learners. Therefore, the study proposes an English learning system based on efficient and deep Matrix decomposition. The results of the experiments showed that, in practical teaching applications, about 57.5% of students with good grades have improved their grades due to the use of the English learning system proposed by the research institute, with only about 17.5% of their grades decreasing. 67.5% of students with average grades have improved their grades after using the system, with only 10% decreasing. Among the students with poor grades, about 50% of them improved their academic performance through the system, while about 27.5% of them experienced a decrease. Meanwhile, the experiment also tested the efficient deep Matrix decomposition model in the learning system: the minimum absolute average errors of the model on different data sets are about 0.61, 0.69, 0.77 and 0.82, respectively. The minimum Root-mean-square deviation is about 0.91, 0.98, 1.06 and 1.1, which is far lower than other recommended models. The above results show that the system constructed in this paper can recommend courses accordingto students ‘actual learning level, and can effectively improve students’ academic performance in the actual teaching process.

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Section: And Yang Proposed An Intelligent Recommendation Algorithmmentioning

confidence: 99%

Design and Implementation of an English Learning System Based on Intelligent Recommendation

Yuan

2024

ITC

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“…However, the existing technique is a preliminary exploration of pattern matching to optimize programming languages and their lack of prototype implementation. Zhang et al 5–10 presented several concurrency‐related refactoring tools to improve lock patterns and detect code smell patterns. In the software industry, refactoring plugins for pattern matching are integrated into modern IDEs like Visual Studio and IntelliJ IDEA.…”

Section: Introductionmentioning

confidence: 99%

“…prototype implementation. Zhang et al [5][6][7][8][9][10] presented several concurrency-related refactoring tools to improve lock patterns and detect code smell patterns. In the software industry, refactoring plugins for pattern matching are integrated into modern IDEs like Visual Studio and IntelliJ IDEA.…”

mentioning

confidence: 99%

ReInstancer: An automatic refactoring approach for Instanceof pattern matching

Zhang,

Hong

2024

J Software Evolu Process

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The instanceof pattern matching can improve the code quality and readability by removing the redundant typecasting and simplifying the design in different scenarios. However, existing works do not provide sufficient support for refactoring instanceof pattern matching. This paper first identifies several cases that cannot be well handled by existing IDEs. Based on these observations, we propose a novel approach called ReInstancer to refactor instanceof pattern matching automatically. ReInstancer conducts program analysis for multi‐branch statements to obtain pattern variables. After analyzing these patterns, the multi‐branch statements are optimized and finally refactored into switch statements or expressions. ReInstancer is evaluated by 20 real‐world projects with more than 7,700 instanceof pattern matching. The experimental results demonstrate that a total of 3,558 instanceof expressions and 228 multi‐branch statements are refactored within 10.8 s on average for each project. ReInstancer improves the code quality by reducing redundant typecasting, demonstrating its effectiveness.

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“…However, existing technique is a preliminary exploration of pattern matching to optimize programming languages, and there lacks of prototype implementation. Zhang et al [5] [6] [7][8] [9] [10] presented several concurrency-related refactoring tools to improve lock patterns and detect code smell patterns. In the software industry, refactoring plugins for pattern matching are integrated into the modern integrated development environment (IDE) like Visual Studio and IntelliJ IDEA.…”

mentioning

confidence: 99%

ReInstancer: An Automatic Refactoring Approach for Instancerof Pattern Matching

Zhang

CLE

2023

Preprint

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The extit{instanceof} pattern matching is periodically previewed in the latest published JDK by removing the redundant casting and optimizing its usage in different scenarios, which improves the code quality and readability. However, the existing integrated development environment (IDE) does not provide sufficient support for refactoring of extit{instanceof} pattern matching in all situations. This paper first identifies several cases that cannot be handled by existing IDEs, and then proposes a novel approach called extit{ReInstancer} to refactor extit{instanceof} pattern matching automatically. extit{ReInstancer} conducts visitor pattern analysis for extit{if} statement blocks with extit{instanceof}. For the extit{instanceof} expression, a pattern variable is obtained after removing the redundant type casting by pattern analysis. For multi-branch statements, extit{ReInstancer} employs class hierarchy analysis and control flow analysis to infer the relationship of instance type in each branch and then optimize these branches. Finally, the multi-branch statements are converted into switch statements or switch expressions. extit{ReInstancer} is evaluated with 20 real-world applications. The experimental results demonstrate a total of 3558 extit{instanceof} expressions and 228 multi-branch statements applying the refactoring, and each application takes an average of 10.8s. extit{ReInstancer} not only reduces the redundant type casting but also improves the code quality.

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Refactoring‐based learning for fine‐grained lock in concurrent programming course

Cited by 7 publications

References 30 publications

Design and Implementation of an English Learning System Based on Intelligent Recommendation

Design and Implementation of an English Learning System Based on Intelligent Recommendation

ReInstancer: An automatic refactoring approach for Instanceof pattern matching

ReInstancer: An Automatic Refactoring Approach for Instancerof Pattern Matching

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