Learning latent features of Web services will greatly boost the ability of search engine to discover relevant services. Extracted information from Web Service Description Language (WSDL) documents of services is less efficient due to the limited usage of data source. Recently, a number of ongoing works have indicated incorporating service tag, a textual symbol provides additional contextual and semantic information, helps to enhance the process of service discovery. However, a large number of relevant tags for Web services are difficult to obtain in practice. In this paper, we propose a Web service Tag Learning system to address this issue. WT Learning system adopts sparse learning technique to fully understand the structure of high dimensional textual information extracted from WSDL documents and tags. Meanwhile, our proposed system implements Alternative Direction Method of Multiplier (ADMM) strategy, which accelerates solving process in Big Data environment. Extensive experiments are conducted based on real-world dataset, which consists of 24,569 Web services. The results demonstrate the effectiveness of WT Learning system. Specifically, our system outperforms other state-of-the-art frameworks in tag classification and recommendation tasks, with 29.6% and 27.1% performance gaining respectively.
{"title":"Accelerated Sparse Learning on Tag Annotation for Web Service Discovery","authors":"Wei Lo, Jianwei Yin, Zhaohui Wu","doi":"10.1109/ICWS.2015.44","DOIUrl":"https://doi.org/10.1109/ICWS.2015.44","url":null,"abstract":"Learning latent features of Web services will greatly boost the ability of search engine to discover relevant services. Extracted information from Web Service Description Language (WSDL) documents of services is less efficient due to the limited usage of data source. Recently, a number of ongoing works have indicated incorporating service tag, a textual symbol provides additional contextual and semantic information, helps to enhance the process of service discovery. However, a large number of relevant tags for Web services are difficult to obtain in practice. In this paper, we propose a Web service Tag Learning system to address this issue. WT Learning system adopts sparse learning technique to fully understand the structure of high dimensional textual information extracted from WSDL documents and tags. Meanwhile, our proposed system implements Alternative Direction Method of Multiplier (ADMM) strategy, which accelerates solving process in Big Data environment. Extensive experiments are conducted based on real-world dataset, which consists of 24,569 Web services. The results demonstrate the effectiveness of WT Learning system. Specifically, our system outperforms other state-of-the-art frameworks in tag classification and recommendation tasks, with 29.6% and 27.1% performance gaining respectively.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128637184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increased number of web services advertised on the internet, it is becoming vital to resolve typical problems of service recommendation. Although service recommendation has been studied by researchers in recent years, existing methods have remarkable achievements on offering single service recommendation, not only considering functional features of web services but also non-functional features. However, the customers usually adopted composite services to satisfy complex and coarse-grained requirements. The traditional service recommendation does not have much concern about composite services. Through a long period of usage, the dependencies among composite services are hidden in historical usage records. In reality, these dependencies have great influence on the quality of service recommendation. To improve the effectiveness of service recommendation, this paper proposes a novel service recommendation approach based on service usage patterns. Firstly, the similar customer group of target customer is identified through the personal attribute based clustering and similarity of rating preference, Secondly, service usage patterns of the similar customer group are mined based on the variant of Generalized Sequential Patterns (GSP) algorithm, Thirdly, promising services are recommended for the target customer according to the matching degree between previously used services and service usage patterns, Finally, experimental results verify the efficiency and effectiveness of our approach.
{"title":"Service Recommendation Using Customer Similarity and Service Usage Pattern","authors":"Ruilin Liu, Xiaofei Xu, Zhongjie Wang","doi":"10.1109/ICWS.2015.61","DOIUrl":"https://doi.org/10.1109/ICWS.2015.61","url":null,"abstract":"With the increased number of web services advertised on the internet, it is becoming vital to resolve typical problems of service recommendation. Although service recommendation has been studied by researchers in recent years, existing methods have remarkable achievements on offering single service recommendation, not only considering functional features of web services but also non-functional features. However, the customers usually adopted composite services to satisfy complex and coarse-grained requirements. The traditional service recommendation does not have much concern about composite services. Through a long period of usage, the dependencies among composite services are hidden in historical usage records. In reality, these dependencies have great influence on the quality of service recommendation. To improve the effectiveness of service recommendation, this paper proposes a novel service recommendation approach based on service usage patterns. Firstly, the similar customer group of target customer is identified through the personal attribute based clustering and similarity of rating preference, Secondly, service usage patterns of the similar customer group are mined based on the variant of Generalized Sequential Patterns (GSP) algorithm, Thirdly, promising services are recommended for the target customer according to the matching degree between previously used services and service usage patterns, Finally, experimental results verify the efficiency and effectiveness of our approach.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115468151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a recommender system, items can be rated across multiple fields by users with varying degrees of familiarity. Hence, the ratings in a recommender system should have different recommended weights. Ratings in fields where in the user has high or low familiarity should be given high or low recommended weights, respectively. However, current recommendation algorithms ignore this problem and use the ratings indiscriminately, thus affecting the accuracy of the recommendation system. In this paper, we provide a focused study of user-familiarity degree-aware recommendation and develop a user-familiarity degree-aware latent factor model for recommendations that considers both user familiarity and item features reflected by the tagging information. We also design a user-familiarity degree-aware probability matrix factorization model, which computes the degree of familiarity of a user with the items he/she has rated. By using the user-familiarity degree, different recommended weights are given to every rating to obtain precise recommendations. The experiment results on real-world datasets show that our algorithm significantly outperforms state-of-the-art latent factor models and effectively improves the accuracy of the recommendation results.
{"title":"User Familiar Degree Aware Recommender System","authors":"Yusheng Li, E. Haihong, Meina Song, Junde Song","doi":"10.1109/ICWS.2015.58","DOIUrl":"https://doi.org/10.1109/ICWS.2015.58","url":null,"abstract":"In a recommender system, items can be rated across multiple fields by users with varying degrees of familiarity. Hence, the ratings in a recommender system should have different recommended weights. Ratings in fields where in the user has high or low familiarity should be given high or low recommended weights, respectively. However, current recommendation algorithms ignore this problem and use the ratings indiscriminately, thus affecting the accuracy of the recommendation system. In this paper, we provide a focused study of user-familiarity degree-aware recommendation and develop a user-familiarity degree-aware latent factor model for recommendations that considers both user familiarity and item features reflected by the tagging information. We also design a user-familiarity degree-aware probability matrix factorization model, which computes the degree of familiarity of a user with the items he/she has rated. By using the user-familiarity degree, different recommended weights are given to every rating to obtain precise recommendations. The experiment results on real-world datasets show that our algorithm significantly outperforms state-of-the-art latent factor models and effectively improves the accuracy of the recommendation results.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127014287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gustavo Resende, Nilander R. M. de Moraes, S. Dias, H. T. Marques-Neto, Luis E. Zárate
Formal concept analysis is a mathematics research field introduced in the beginning of the 1980s by Rudolf Wille, that has been applied in several different knowledge areas, including Computer Science. FCA is a data analysis theory that identifies conceptual structures within data sets or formal contexts. In this work, we propose an FCA-based approach to build minimal implication rules-based computational models for social networks. As an application example, in this work we constructed canonical models using data extracted from user sessions in one of the most popular social networks in Brazil, Orkut. These models represent the patterns of access to Orkut, about a certain problem domain, and are composed by a minimal rule set.
{"title":"Canonical Computational Models Based on Formal Concept Analysis for Social Network Analysis and Representation","authors":"Gustavo Resende, Nilander R. M. de Moraes, S. Dias, H. T. Marques-Neto, Luis E. Zárate","doi":"10.1109/ICWS.2015.100","DOIUrl":"https://doi.org/10.1109/ICWS.2015.100","url":null,"abstract":"Formal concept analysis is a mathematics research field introduced in the beginning of the 1980s by Rudolf Wille, that has been applied in several different knowledge areas, including Computer Science. FCA is a data analysis theory that identifies conceptual structures within data sets or formal contexts. In this work, we propose an FCA-based approach to build minimal implication rules-based computational models for social networks. As an application example, in this work we constructed canonical models using data extracted from user sessions in one of the most popular social networks in Brazil, Orkut. These models represent the patterns of access to Orkut, about a certain problem domain, and are composed by a minimal rule set.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hongbing Wang, Bin Zou, G. Guo, Jie Zhang, Zhengping Yang
Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences, (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Experimental results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.
{"title":"Optimal and Effective Web Service Composition with Trust and User Preference","authors":"Hongbing Wang, Bin Zou, G. Guo, Jie Zhang, Zhengping Yang","doi":"10.1109/ICWS.2015.106","DOIUrl":"https://doi.org/10.1109/ICWS.2015.106","url":null,"abstract":"Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences, (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Experimental results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123552438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amina Bourouis, Kais Klai, Yamen El Touati, N. Hadj-Alouane
Automatic composition of Web services requires that the providers publish an abstract version of their Web services to a registry. They offer this abstraction instead of the complete Web service to ensure the privacy of their internal know-how and trade secrets. Many studies have offered methods to do this, but none of them is able to formally prove their ability to keep the secret information hidden. In this article we turn to the verification of opacity, a formal security property that allows not only to preserve the secret but also to formally prove that it remains hidden. In particular, we investigate if the composition of two opaque Web services is also opaque. Our work consists in verifying the opacity of the composition of two Web services through the verification of the opacity of their individual abstractions represented by Symbolic Observation Graphs.
{"title":"Opacity Preserving Abstraction for Web Services and Their Composition Using SOGs","authors":"Amina Bourouis, Kais Klai, Yamen El Touati, N. Hadj-Alouane","doi":"10.1109/ICWS.2015.50","DOIUrl":"https://doi.org/10.1109/ICWS.2015.50","url":null,"abstract":"Automatic composition of Web services requires that the providers publish an abstract version of their Web services to a registry. They offer this abstraction instead of the complete Web service to ensure the privacy of their internal know-how and trade secrets. Many studies have offered methods to do this, but none of them is able to formally prove their ability to keep the secret information hidden. In this article we turn to the verification of opacity, a formal security property that allows not only to preserve the secret but also to formally prove that it remains hidden. In particular, we investigate if the composition of two opaque Web services is also opaque. Our work consists in verifying the opacity of the composition of two Web services through the verification of the opacity of their individual abstractions represented by Symbolic Observation Graphs.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As various types of Internets of Things (IoT) are deployed in a wide range of areas, the need arises to utilize various IoT resources dynamically to accomplish user tasks. We call this environment an urban-scale IoT environment, where various IoT resources that are necessary to accomplish user tasks are directly connected to each other via users' mobile devices, such as their smart phones. IoT resources are utilized as resources with which to run a composite service that supports user tasks. In this urban-scale IoT environment, it is essential to create efficient binding between a service and an IoT resource so as to execute a composite service for a task successfully. In this paper, we propose a service resource allocation approach which minimizes data transmissions between users' mobile devices and which effectively deal with the constraints of these types of environments. We transformed the resource allocation problem into a variant of the degree-constrained minimum spanning tree problem and applied a genetic algorithm to reduce the time needed to produce a near-optimal solution. We also defined a fitness function and an encoding scheme to apply the genetic algorithm in an efficient manner. The proposed approach shows a 97% success rate on average when used to find near-optimal solutions. In addition, it takes significantly less time than the brute force approach.
{"title":"An Efficient Resource Allocation Approach Based on a Genetic Algorithm for Composite Services in IoT Environments","authors":"Minhyeop Kim, In-Young Ko","doi":"10.1109/ICWS.2015.78","DOIUrl":"https://doi.org/10.1109/ICWS.2015.78","url":null,"abstract":"As various types of Internets of Things (IoT) are deployed in a wide range of areas, the need arises to utilize various IoT resources dynamically to accomplish user tasks. We call this environment an urban-scale IoT environment, where various IoT resources that are necessary to accomplish user tasks are directly connected to each other via users' mobile devices, such as their smart phones. IoT resources are utilized as resources with which to run a composite service that supports user tasks. In this urban-scale IoT environment, it is essential to create efficient binding between a service and an IoT resource so as to execute a composite service for a task successfully. In this paper, we propose a service resource allocation approach which minimizes data transmissions between users' mobile devices and which effectively deal with the constraints of these types of environments. We transformed the resource allocation problem into a variant of the degree-constrained minimum spanning tree problem and applied a genetic algorithm to reduce the time needed to produce a near-optimal solution. We also defined a fitness function and an encoding scheme to apply the genetic algorithm in an efficient manner. The proposed approach shows a 97% success rate on average when used to find near-optimal solutions. In addition, it takes significantly less time than the brute force approach.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach by applying clustering on web services before applying collaborative filtering (named services clustering QoS prediction, SCQP). The clustering process cluster web services in to service clusters in which services have the same physical environment. Then the similarity between users is calculated based on these service clusters instead of individual services. So that there are more information to be used when calculate the similarity and it will contribute to elevate the prediction precision. The experimental results showed that our hybrid approach could not only achieve higher prediction precision, but also reduce the computation time than other collaborative filtering based prediction methods.
{"title":"User-QoS-Based Web Service Clustering for QoS Prediction","authors":"Fu Chen, Shijin Yuan, Bin Mu","doi":"10.1109/ICWS.2015.83","DOIUrl":"https://doi.org/10.1109/ICWS.2015.83","url":null,"abstract":"QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach by applying clustering on web services before applying collaborative filtering (named services clustering QoS prediction, SCQP). The clustering process cluster web services in to service clusters in which services have the same physical environment. Then the similarity between users is calculated based on these service clusters instead of individual services. So that there are more information to be used when calculate the similarity and it will contribute to elevate the prediction precision. The experimental results showed that our hybrid approach could not only achieve higher prediction precision, but also reduce the computation time than other collaborative filtering based prediction methods.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"111 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114048759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Users visit web services and compose them to accomplish on-line tasks. Normally, users enter the same information into various web services to finish such tasks. However, repetitively typing the same information into services is unnecessary and decreases the service composition efficiency. In this paper, we propose a context-aware ranking approach to recommend previous user inputs into input parameters and save users from repetitive typing. We develop five different ranking features constructed from various types of information, such as user contexts. We adopt a learning-to-rank approach, a machine learning technology automatically constructing the ranking model, and integrate our ranking features into a state-of-the-art learning-to-rank framework. Our approach learns the information of interactions between input parameters and user inputs to reuse user inputs under different contexts. Through an empirical study on 960 real services, our approach outperforms two baseline approaches on ranking values to input parameters of composed services. Moreover, we observe that textual information affects the ranking most and the contextual information of location matters the most to ranking among various types of contextual data.
{"title":"Learning to Reuse User Inputs in Service Composition","authors":"Shaohua Wang, Ying Zou, J. Ng, Tinny Ng","doi":"10.1109/ICWS.2015.97","DOIUrl":"https://doi.org/10.1109/ICWS.2015.97","url":null,"abstract":"Users visit web services and compose them to accomplish on-line tasks. Normally, users enter the same information into various web services to finish such tasks. However, repetitively typing the same information into services is unnecessary and decreases the service composition efficiency. In this paper, we propose a context-aware ranking approach to recommend previous user inputs into input parameters and save users from repetitive typing. We develop five different ranking features constructed from various types of information, such as user contexts. We adopt a learning-to-rank approach, a machine learning technology automatically constructing the ranking model, and integrate our ranking features into a state-of-the-art learning-to-rank framework. Our approach learns the information of interactions between input parameters and user inputs to reuse user inputs under different contexts. Through an empirical study on 960 real services, our approach outperforms two baseline approaches on ranking values to input parameters of composed services. Moreover, we observe that textual information affects the ranking most and the contextual information of location matters the most to ranking among various types of contextual data.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A web-based service consists of layers of programs (components) in the technology stack. Analyzing program executions of these components separately allows service vendors to acquire insights into specific program behaviors or problems in these components, thereby pinpointing areas of improvement in their offering services. Many existing approaches for testing as a service take an orchestration approach that splits components under test and the analysis services into a set of distributed modules communicating through message-based approaches. In this paper, we present the first work in providing dynamic analysis as a service using a virtual machine (VM)-based approach on dynamic data race detection. Such a detection needs to track a huge number of events performed by each thread of a program execution of a service component, making such an analysis unsuitable to use message passing to transit huge numbers of events individually. In our model, we instruct VMs to perform holistic dynamic race detections on service components and only transfer the detection results to our service selection component. With such result data as the guidance, the service selection component accordingly selects VM instances to fulfill subsequent analysis requests. The experimental results show that our model is feasible.
{"title":"Architecturing Dynamic Data Race Detection as a Cloud-Based Service","authors":"Changjiang Jia, Chunbai Yang, W. Chan","doi":"10.1109/ICWS.2015.54","DOIUrl":"https://doi.org/10.1109/ICWS.2015.54","url":null,"abstract":"A web-based service consists of layers of programs (components) in the technology stack. Analyzing program executions of these components separately allows service vendors to acquire insights into specific program behaviors or problems in these components, thereby pinpointing areas of improvement in their offering services. Many existing approaches for testing as a service take an orchestration approach that splits components under test and the analysis services into a set of distributed modules communicating through message-based approaches. In this paper, we present the first work in providing dynamic analysis as a service using a virtual machine (VM)-based approach on dynamic data race detection. Such a detection needs to track a huge number of events performed by each thread of a program execution of a service component, making such an analysis unsuitable to use message passing to transit huge numbers of events individually. In our model, we instruct VMs to perform holistic dynamic race detections on service components and only transfer the detection results to our service selection component. With such result data as the guidance, the service selection component accordingly selects VM instances to fulfill subsequent analysis requests. The experimental results show that our model is feasible.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：