WCET optimization strategy based on source code refactoring

Meng, Fanqi; Su, Xin

doi:10.1007/s10586-017-1369-3

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the priority and component recentness purposes are less effective in many/multi‐objective setups when used together. Meng and Su 27 deliberated the worst‐case execution time (WCET) optimization strategy based on source code refactoring. The framework leads programmers through an appropriate sequence to look for refactoring possibilities.…”

Section: Related Work On Software Code Refactoringmentioning

confidence: 99%

Software code refactoring based on deep neural network‐based fitness function

K¹,

Thirumaaran²

2022

Concurrency and Computation

View full text Add to dashboard Cite

Refactoring is extensively recognized for enhancing the internal structure of object-oriented software while preserving its external behavior. However, determining refactoring opportunities is challenging for designers and researchers alike. In recent years, machine learning algorithms have shown a great possibility of resolving this issue. This study proposes a deep neural network-based fitness function (DNNFF) to resolve the software refactoring issue. This study suggests an effective learning technique that automatically featured extracted from the trained models and predicted code clones to recommend which category to refactor. The software engineers automatically assess the recommended refactoring solutions using Genetic Algorithms (GA) for minimum iterations. A Deep Neural Networks (DNN) utilizes these training instances to assess the refactoring solutions for the residual iterations. The refactoring process primarily depends on software designers' skills and perceptions. The simulation findings demonstrate that the suggested DNNFF model enhances the code change score of 98.7%, automatic refactoring score of 97.3%, defect correlation ratio of 96.9%, refactoring precision ratio of 95.9%, flaw detection ratio of 94.4%, and reduces the execution time of 10.2% compared to other existing methods.

show abstract

Section: Related Work On Software Code Refactoringmentioning

confidence: 99%

Software code refactoring based on deep neural network‐based fitness function

K¹,

Thirumaaran²

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Note that all non-orthogonal nested loops lie in branch statements. The correction is safe because the non-orthogonal nested loops lie in the invariant path [40] of WCEP. Actually, invariant path is a concept in the field of code optimization for reducing WCET, and it includes various cases.…”

Section: Efficiencymentioning

confidence: 99%

Reducing WCET Overestimations by Correcting Errors in Loop Bound Constraints

Meng

2017

Energies

Self Cite

View full text Add to dashboard Cite

Abstract:In order to reduce overestimations of worst-case execution time (WCET), in this article, we firstly report a kind of specific WCET overestimation caused by non-orthogonal nested loops. Then, we propose a novel correction approach which has three basic steps. The first step is to locate the worst-case execution path (WCEP) in the control flow graph and then map it onto source code. The second step is to identify non-orthogonal nested loops from the WCEP by means of an abstract syntax tree. The last step is to recursively calculate the WCET errors caused by the loose loop bound constraints, and then subtract the total errors from the overestimations. The novelty lies in the fact that the WCET correction is only conducted on the non-branching part of WCEP, thus avoiding potential safety risks caused by possible WCEP switches. Experimental results show that our approach reduces the specific WCET overestimation by an average of more than 82%, and 100% of corrected WCET is no less than the actual WCET. Thus, our approach is not only effective but also safe. It will help developers to design energy-efficient and safe real-time systems.

show abstract

The Study of Power Server Operations Information Visualization Key Technologies

Guo

et al. 2018

Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing

View full text Add to dashboard Cite

WCET optimization strategy based on source code refactoring

Cited by 3 publications

References 16 publications

Software code refactoring based on deep neural network‐based fitness function

Software code refactoring based on deep neural network‐based fitness function

Reducing WCET Overestimations by Correcting Errors in Loop Bound Constraints

The Study of Power Server Operations Information Visualization Key Technologies

Contact Info

Product

Resources

About