Modeling and Analyzing Method for CPS Software Architecture Energy Consumption

Zhang, Guangquan; Zhang, Kan; Xiao, Zhou; Chen, Mingcai; Xu, Chengkai; Yu-zhen, Shao

doi:10.4304/jsw.8.11.2974-2981

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…e cost of classifying defective instances as nondefective instances is too high, thus affecting the usefulness of the prediction model; the high redundancy in the software metric and the high similarity between nondefective instances allow the data quality to be improved by methods such as feature selection [3]. e three methods, as researchers imaginatively call them, analyze software testing in terms of its length, volume, and structure, respectively [12]. Dynamic software defect prediction refers to the technique of predicting the distribution of system defects over time based on the time of defect generation or failure.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

Liu

Wang

2021

Scientific Programming

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This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.

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Section: Current Status Of Researchmentioning

confidence: 99%

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

Liu

Wang

2021

Scientific Programming

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“…A direct relation is described between the granularity of the measurements and the ability to determine the cause of changes in EC. Another approach is to characterize software using Petri nets . Assuming that a complex software product can be fitted into a Petri net, analysis could show the path of lowest EC to perform a specific task.…”

Section: Related Workmentioning

confidence: 99%

“…Another approach is to characterize software using Petri nets. 43 Assuming that a complex software product can be fitted into a Petri net, analysis could show the path of lowest EC to perform a specific task. If the changes in a new release can be included in the Petri net, the difference(s) between releases can be quantified.…”

Section: Energy Consumption Comparison Between Releasesmentioning

confidence: 99%

Energy efficiency on the product roadmap: An empirical study across releases of a software product

Jagroep

Procaccianti

Werf

et al. 2017

J Software Evolu Process

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SUMMARYIn the quest for energy efficient ICT, research has mostly focused on the role of hardware. However, the impact of software on energy consumption has been acknowledged as significant by researchers in software engineering. In spite of that, due to cost and time constraints, many software producing organizations are unable to effectively measure software energy consumption preventing them to include energy efficiency in the product roadmap. In this paper, we apply a software energy profiling method to reliably compare the energy consumed by a commercial software product across two consecutive releases. We demonstrate how the method can be applied and provide an in-depth analysis of energy consumption of software components. Additionally, we investigate the added value of these measurement for multiple stakeholders in a software producing organization, by means of semi-structured interviews. Our results show how the introduction of an encryption module caused a noticeable increase in the energy consumption of the product. Such results were deemed valuable by the stakeholders and provided insights on how specific software changes might affect energy consumption. In addition, our interviews show that such a quantification of software energy consumption helps to create awareness and eventually consider energy efficiency aspects when planning software releases.

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“…In [37] a method is presented to determine the minimum EC path through Petri Nets and reachable state graphs. Others propose to create modular software and collect utilization data of functional elements which is used by a resource utilization model [35] functioning as 'energy broker'.…”

Section: Relating Green Software To Software Architecturementioning

confidence: 99%

Extending software architecture views with an energy consumption perspective

et al. 2016

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The rising energy consumption of the ICT industry has triggered a quest for more sustainable, i.e. energy efficient, ICT solutions. Software plays an essential role in finding these solutions, as software is identified as the true consumer of power. However, in this context, software is often treated as a single, complex entity instead of the interrelated elements that it actually consists of. Although useful results can be gained, this approach fails to provide detailed insight in the elements that invoke specific energy consumption behavior. As a result, software vendors are not able to address energy consumption on software level. In this paper, we propose an energy consumption perspective on software architecture as a means to provide this insight and enable analysis on the architectural elements that are the actual drivers behind the energy consumption. In support of this perspective, we also position sustainability as a potential quality attribute thereby provide a means to quantify energy consumption aspects related to software. In a case study using a commercial software product the perspective and quality attribute are applied, demonstrating the potential by achieving an energy consumption saving of 67.1 %.

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Modeling and Analyzing Method for CPS Software Architecture Energy Consumption

Cited by 5 publications

References 13 publications

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

Energy efficiency on the product roadmap: An empirical study across releases of a software product

Extending software architecture views with an energy consumption perspective

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