Refactoring Java Programs for Customizable Locks Based on Bytecode Transformation

2020

Concurrent Engineering

Self Cite

Refactoring has become an effective approach to convert sequential programs into concurrent programs. Many refactoring algorithms and tools are proposed to assist developers in writing high-performance concurrent programs. Although researchers actively conduct surveys on refactoring, we are not aware of any survey that summarizes, categorizes and discusses concurrency-oriented refactoring. To this end, this paper presents a survey that investigates how refactoring assists with concurrent programming. To the best of our knowledge, this paper is the first survey that summarizes the state-of-the-art, concurrency-oriented refactoring. First, we design six research questions addressing the concurrent structure, programming language, performance improvement and consistency evaluation. Second, we answer these questions by examining the related papers and then present the results to show how refactoring provides support for concurrent programming after a decade of development, such as transforming the concurrent structures, supporting parallel language, and improving performance. Finally, we summarize the related works and present the future trends.

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

A survey of concurrency-oriented refactoring

2020

Concurrent Engineering

Self Cite

“…Zhang et al 29 presented an automated refactoring method among locks at the byte code level. With the promising features of StampedLock, Zhang et al 30 presented an automated refactoring framework to convert a synchronized lock to a StampedLock.…”

Section: Refactoring Consistency-based Methodsmentioning

confidence: 99%

A consistency-guaranteed approach for Internet of Things software refactoring

Sun

International Journal of Distributed Sensor Networks

et al. 2020

Self Cite

The software architecture of Internet of Things defines the component model and interconnection topology of Internet of Things systems. Refactoring is a systematic practice of improving a software structure without altering its external behaviors. When the Internet of Things software is refactored, it is necessary to detect the correctness of Internet of Things software to ensure its security. To this end, this article proposes a novel refactoring correction detection approach to ensure software security. Control flow analysis and data flow analysis are used to detect code changes before and after refactoring, and synchronization dependency analysis is used to detect changes in synchronization dependency. Three detection algorithms are designed to detect refactoring correctness. Four real-world benchmark applications are used to evaluate our approach. The experimental results show that our proposed approach can ensure correctness of Internet of Things software refactoring.

“…The new feature information is obtained by multiplying the weight value and the original feature information. The weighted value S is calculated by Equation (2).…”

Section: Metric-attention Mechanismmentioning

confidence: 99%

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

J Software Evolu Process

Dong

2021

Self Cite

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.