An Automated Refactoring Approach to Improve IoT Software Quality

Zhang, Yang; Shao, Shuai; Ji, Minghan; Qiu, Jing; Tian, Zhihong; Du, Xiaojiang; Guizani, Mohsen

doi:10.3390/app10010413

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Prior research on refactoring had developed many useful tools [8,11,31]. However, these tools were mainly proposed for scientific research, not being introduced for teaching and learning.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

2021

Comp Applic In Engineering

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Fine‐grained lock is frequently used to mitigate lock contention in the multithreaded program running on a shared‐memory multicore processor. However, a concurrent program based on the fine‐grained lock is hard to write, especially for beginners in the concurrent programming course. How to help participants learn fine‐grained lock has become increasingly important and urgent. To this end, this paper presents a novel refactoring‐based approach to enhance the learning effectiveness of fine‐grained locks. Two refactoring tools are introduced to provide illustrating examples for participants by converting original coarse‐grained locks into fine‐grained ones automatically. Learning effectiveness and limitations are discussed when refactoring tools are applied. We evaluate students' outcomes with two benchmarks and compare their performance in Fall 2018 with those in Fall 2019. We also conduct experiments on students' outcomes by dividing them into two groups (A and B) in a controlled classroom where participants in group A learn the fine‐grained locks with the help of refactoring tools while those in group B do not access these tools. Evaluation of the results when they have been taught with the refactoring‐based approach reveals a significant improvement in the students' learning.

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“…Prior research on refactoring had developed many useful tools [8,11,31]. However, these tools were mainly proposed for scientific research, not being introduced for teaching and learning.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

2021

Comp Applic In Engineering

Self Cite

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Ensemble Deep Network for Secured Refactoring Framework by Predicting Code‐Bad Smells in Software Projects

Pandiyavathi,

Sivakumar

2024

J Software Evolu Process

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In modern times, refactoring is one of the significantly utilized approaches for enhancing the software's quality like understandability, testability, and maintainability. Moreover, the refactoring effect on its security has been underrated. In addition to that, there are only a few studies that offer the classification over refactoring approaches depending on the effect over the quality attributes that help the designer to attain certain objectives by choosing the most significant approach and it is applied in the right places based on the specified software quality attributes. The contradictory outcomes are attained by considering the quality of the software creates limitations for the developers while performing the software refactoring process. In this paper, a secured deep learning‐based software refactoring approach is designed. At first, software projects collected from online sources are offered as input for this software refactoring process to detect the security metrics in the projects. After detecting the security metrics, refactoring is applied in the software projects to change the internal design. Then, the security metrics of the refactored projects are detected again. Further, the security metrics computed before and after refactoring are compared with the software projects. The projects are labeled based on security, needs, and refactoring level. Then, the Ensemble Attention‐based Deep Network (EA‐DNet) is developed, which is designed with the Recurrent Neural Network (RNN), Deep Temporal Convolution Network (DTCN), and Bi‐directional Long Short Term Memory (Bi‐LSTM). This network is trained to get better results in the prediction of code‐bad smells in software projects. The prior software refactoring approaches are compared with the proposed code‐bad smells‐based software refactoring process.

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Consistency Validation Method for Java Fine-Grained Lock Refactoring

Zhang

Bai

2021

IEEE Access

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An Automated Refactoring Approach to Improve IoT Software Quality

Cited by 6 publications

References 28 publications

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

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

Ensemble Deep Network for Secured Refactoring Framework by Predicting Code‐Bad Smells in Software Projects

Consistency Validation Method for Java Fine-Grained Lock Refactoring

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