Open AccessIdentifying Significant Components of Complex Software for Improving Reliability: Using Invocation Relationships and Component Characteristics
Author(s) -
Lixing Xue,
Zhan Zhang,
Decheng Zuo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1284/1/012041
Subject(s) - component (thermodynamics) , computer science , ranking (information retrieval) , invocation , reliability (semiconductor) , set (abstract data type) , component based software engineering , software , distributed computing , data mining , software system , reliability engineering , programming language , artificial intelligence , engineering , physics , sociology , anthropology , thermodynamics , power (physics) , quantum mechanics
The scales of software systems are getting larger and larger, which may make the system reliability become low. To improve their reliability, a set of important components that are able to strongly influence the system reliability are usually determined first. However, the existing determining approaches only consider the components which are often called by other components as significant ones, but overlook the components which often call others also have a strong effect on the system. Besides, the approaches all require component invocation probabilities which cannot be obtained easily in large-scale systems. To attack the problems, we propose a novel approach for identifying the significant components in complex systems. This approach includes two component ranking algorithms, which take into account not only the components that are frequently invoked, but also the components which often invoke others. The two algorithms, which do not require component invocation probabilities, can either make significant component ranking only based on the component invocation relationships or consider not only the component invocation relationships but also the component characteristics to achieve results. The significant components are selected according to the two ranking results. Extensive experiments are provided to evaluate the approach and draw comparisons with existing methods.