A survey of concurrency-oriented refactoring

Zhang, Yang; Li, Liuxu; Zhang, Dongwen

doi:10.1177/1063293x20958932

Cited by 3 publications

(2 citation statements)

References 61 publications

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“…Huang et al 37 proposed a gated recurrent residual neural network for modulation classification. Zhang et al 38 investigated how refactoring assists with concurrent programming. Wang et al 16 proposed the BPNN approach that achieves better performance compared to machine learning and heuristic approaches on class-level and method-level datasets.…”

Section: Deep Learning For Detecting Code Smellmentioning

confidence: 99%

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang

Dong

2021

J Software Evolu Process

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Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep‐learning‐based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real‐world applications. MARS is evaluated on BrainCode and compared to other machine‐learning‐based and deep‐learning‐based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state‐of‐the‐art.

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Section: Deep Learning For Detecting Code Smellmentioning

confidence: 99%

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang

Dong

2021

J Software Evolu Process

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“…The state of the art shows some notable approaches that can automatically change applications into parallel versions [10]. Some approaches only focus on recursive algorithms, which represent a narrow category of algorithms [11,12].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Transformer from Sequential to Parallel Java Code

Midolo,

Tramontana

2023

Future Internet

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Sequential programs can benefit from parallel execution to improve their performance. When developing a parallel application, several techniques are employed to achieve the desired behavior: identifying parts that can run in parallel, synchronizing access to shared data, tuning performance, etc. Admittedly, manually transforming a sequential application to make it parallel can be tedious due to the large number of lines of code to inspect, the possibility of errors arising from inaccurate data dependence analysis leading to unpredictable behavior, and inefficiencies when the workload between parallel threads is unbalanced. This paper proposes an automatic approach that analyzes Java source code to identify method calls that are suitable for parallel execution and transforms them so that they run in another thread. The approach is based on data dependence and control dependence analyses to determine the execution flow and data accessed. Based on the proposed method, a tool has been developed to enhance applications by incorporating parallelism, i.e., transforming suitable method calls to execute on parallel threads, and synchronizing data access where needed. The developed tool has been extensively tested to verify the accuracy of its analysis in finding parallel execution opportunities, the correctness of the source code alterations, and the resultant performance gain.

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