Paper Info
Title
An Incremental Software Automation Testing for Space Telemetry, Track and Command Software Systems Based on Domain Knowledge
Abstract
As a core component of the Space Telemetry, Track and Command (TT&C) system, the TT&C software's quality is the key factor to ensure the successful implementation of space TT&C missions. Due to the complexity of space TT&C missions, incremental development is adopted in which frequent testing is required. Many problems of the original manual testing are exposed, such as large consumption of human resources, low efficiency of testing and quality highly relying on human expertise. Automatic testing method is urgently required. However, the testing of the TT&C software highly depends on the domain knowledge of space TT&C missions, which is complex and professional. This hinders the direct application of existing software automatic testing methods to the TT&C software. Therefore, in this paper, we propose an automatic testing method of the TT&C software based on domain knowledge. The domain knowledge description of the space TT&C domain is defined and a set of guidance principles for eliciting domain knowledge elicitation is given. Testing cases are automatically generated and executed by combining the domain knowledge and the image recognition results. Evaluations show that our method can realize the automatic testing of space TT&C software increments with higher accuracy. Its time cost is reduced by more than 50% compared with the manual testing, and will not increase rapidly with growing software maintenance scale. The time cost in domain knowledge elicitation will not affect the testing efficiency.
Year
DOI
Venue
2022
10.1142/S021812662250133X
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
Keywords
DocType
Volume
Space telemetry, track and command system, software automation testing, domain knowledge, incremental testing, graphic interface-based software
Journal
31
Issue
ISSN
Citations 
07
0218-1266
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Bin Yin100.34
Xiaohong Chen246.55
Wanyu Li300.34
Jinyue Tian400.34