Service Evolution Analytics: Change and Evolution Mining of a Distributed System

Chaturvedi, Animesh; Tiwari, Aruna; Binkley, David; Chaturvedi, Shubhangi

doi:10.1109/tem.2020.2987641

Cited by 10 publications

(3 citation statements)

References 49 publications

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“…Looking forward to publishing a complete study of Stable CGERs mining and CGESs mining, which will provide intrinsic details of the approaches and demonstrate detailed study of 10 evolving software systems. Other related works by the author on System Evolution Analytics for Hadoop-HDFS repository [34]- [41] and Cloud Service Evolution Analytics [42]- [45].…”

Section: Discussionmentioning

confidence: 99%

Call Graph Evolution Analytics over a Version Series of an Evolving Software System

Chaturvedi

2022

Preprint

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Software evolution analytics can be supported by generating and comparing call graph evolution information over versions of a software system. Call Graph evolution analytics can assist a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from a set of Evolving Call Graphs ECG = {𝐶𝐺 1 , 𝐶𝐺 2 , ...𝐶𝐺 𝑁 } representing a Version Series VS = {𝑉 1 , 𝑉 2 , ...𝑉 𝑁 } of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. This is demonstrated with detailed results for evolving call graphs of Maven-Core's version series. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; • Information systems → Data mining.

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Section: Discussionmentioning

confidence: 99%

Call Graph Evolution Analytics over a Version Series of an Evolving Software System

Chaturvedi

2022

Preprint

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“…An engineer charged with evolution or maintenance will benefit from changeability and evolvability research. [102] utilizes evolution mining and change mining in the context of emerging distributed networks. First, we propose the Service Transition Classified dependent Interface Slicing algorithm which mines update information from two iterations of the distributed system of evolution.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey of Data Mining Activities in Distributed Systems

Mohammad

Yasin

Salih

et al. 2021

AJRCoS

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Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

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“…The purpose of the general data storage service is to allow applications to classify non-database data such as pictures, text, and compressed packages uploaded by users through SVM, and then store them through this service, so that these contents can be managed uniformly and reduce campus file data loss. The danger and migration difficulty [18][19].…”

Section: General Data Storage Service Based On Svmmentioning

confidence: 99%

Campus Distributed System Platform Considering Support Vector Machine Algorithm

2021

DPS

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With the increase of enrollment scale, the traditional management model of the school has brought huge pressure, and various business processes are flooded with all aspects of education and teaching management. In order to solve these serious problems, the introduction of information technology and the use of network technology, Web technology, to standardize various business processes has become an imperative task. Distributed processing technology decouples and splits the business content in the original single software system. According to business requirements, these split subtasks are distributed to processing nodes with different business processing capabilities for processing, realizing the task processing. Efficiency, perfect for handling campus business. Therefore, this paper designs a distributed system(DS) for the business management of the campus, builds a network topology map through the campus network, and then classifies the campus data according to the characteristics of the support vector machine algorithm classification to achieve a unified and orderly information management. In this paper, the data collection efficiency of the DS is tested under different nodes, and the results show that with the multiplication of collection nodes, the average number of web pages per second is multiplied, and the total time consumption is multiplied.

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Service Evolution Analytics: Change and Evolution Mining of a Distributed System

Cited by 10 publications

References 49 publications

Call Graph Evolution Analytics over a Version Series of an Evolving Software System

Call Graph Evolution Analytics over a Version Series of an Evolving Software System

A Survey of Data Mining Activities in Distributed Systems

Campus Distributed System Platform Considering Support Vector Machine Algorithm

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