Web service QoS prediction using improved software source code metrics

Rangarajan, Sarathkumar; Liu, Huai; Wang, Hua

doi:10.1371/journal.pone.0226867

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…Figure 9 depicts the comparison result of precision. The proposed method is compared with the existing It portrays the novel deep learning‐based hybrid approach (NDLBHA), 1 intelligent neuro‐fuzzy collaborative filtering (INFCF), 3 and long‐term composed service (LCS) 5 . The figure portrays that the precision for the proposed method is higher than the other existing methods.…”

Section: Experimentation and Results Discussionmentioning

confidence: 99%

“…The internet gives an expanding number of web services that makes it hard to choose important web services physically to fulfill complex client necessities. With the fast advancement of service‐oriented computing and distributed computing, the internet gives an expanding number of web services that makes it hard to choose important web services physically to fulfill complex client necessities 1–3 . Indirectly QoS is playing the role of distinguishing the correctness of web services.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.

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Section: Experimentation and Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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“…In this dynamic approach, data is collected at runtime, and the number of method calls is compared to the static coupling dependency to compute the coupling degree statically and dynamically, in which the dynamic coupling metric complements the static ones. Aggregated coupling and cohesion metrics have been used in [14] to predict the quality of services properties of web services. This approach uses source code metrics and machine-learning techniques to automate the prediction of QoS properties and improve its efficiency.…”

Section: Related Workmentioning

confidence: 99%

Software Coupling and Cohesion Model for Measuring the Quality of Software Components

Alzamil

2023

CMC

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Measuring software quality requires software engineers to understand the system's quality attributes and their measurements. The quality attribute is a qualitative property; however, the quantitative feature is needed for software measurement, which is not considered during the development of most software systems. Many research studies have investigated different approaches for measuring software quality, but with no practical approaches to quantify and measure quality attributes. This paper proposes a software quality measurement model, based on a software interconnection model, to measure the quality of software components and the overall quality of the software system. Unlike most of the existing approaches, the proposed approach can be applied at the early stages of software development, to different architectural design models, and at different levels of system decomposition. This article introduces a software measurement model that uses a heuristic normalization of the software's internal quality attributes, i.e., coupling and cohesion, for software quality measurement. In this model, the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s). The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems. The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit, which degrades their quality and the overall quality of the software system. The introduced model can help in understanding the quality of software design. In addition, it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems, which can be eliminated.

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“…Generally, the clustering scheme suffers from the multicollinearity issue, which reduces the accuracy of the prediction system [26]. The PCA (Principle Component Analysis) is considered the most suitable scheme [27] to reduce multicollinearity. We incorporate a regression model with PCA to overcome multicollinearity in this work.…”

Section: Multicollinearity Reduction Schemementioning

confidence: 99%

EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System

Asha

Rajkumar

2022

EAI Endorsed Scal Inf Syst

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This work introduced a novel approach for the movie recommender system using a machine learning approach. This work introduces a clustering-based approach to introduce a recommender system (RS). The conventional clustering approaches suffer from the clustering error issue, which leads to degraded performance. Hence, to overcome this issue, we developed an expectation- maximization-based clustering approach. However, due to imbalanced data, the performance of RS is degraded due to multicollinearity issues. Hence, we Incorporate PCA (Principal Component Analysis) based dimensionality reduction model to improve the performance. Finally, we aim to reduce the error; thus, a Genetic Algorithm (GA) is included to achieve the optimal clusters and assign the suitable recommendation. The experimental study is carried out on publically available movie datasets performance of the proposed approach is measured in terms of MSE (Mean Squared Error) and Root Mean Squared Error (RMSE). The comparative study shows that the proposed approach achieves better performance when compared with a state-of-art movie recommendation system.

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Web service QoS prediction using improved software source code metrics

Cited by 9 publications

References 34 publications

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Software Coupling and Cohesion Model for Measuring the Quality of Software Components

EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System

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