ICALEPCS2023 - List of Authors (Baffa, C.)

Paper	Title	Page
MO2BCO03	Strategy and Tools to Test Software in the SKA Project: The CSP. LMC Case	34
	G. Marotta, C. Baffa, E. Giani INAF - OA Arcetri, Firenze, Italy G. Brajnik IDS, Udine, Italy M. Colciago, I. Novak Cosylab Switzerland, Brugg, Switzerland
	The Square Kilometre Array (SKA) Telescope will be one of the largest and most complex scientific instruments ever built. The development of a reliable software for monitoring and controlling its operations is critical to the success of the entire SKA project. The Local Monitoring and Control of the Central Signal Processor (CSP. LMC) is a software responsible for controlling a key subsystem of the telescope, i.e. the Central Signal Processor (CSP). The software is implemented as a "device" within the TANGO framework, written in Python. In this paper we describe a testing strategy that addresses some typical problems of such a large and complex instrument. It is a multi-level strategy, based on a combination of automated tests (unit/component/integration), in the context of CI/CD practices. Software is also tested against errors and anomalous conditions that can occur while the CSP. LMC is interacting with external subsystems, which can be simulated. The paper also discusses needs and solutions based on data mining test results. This allows us to obtain statistics of unexpected failures and to investigate their causes. Furthermore, a database containing test results supports discovery of interesting and unexpected patterns of behaviors of the tests based on correlations about different test-related events and data. This helps us to develop a deeper understanding of the code’s functioning and to find suitable solutions to minimize unexpected behaviors. In addition it can be used also to support reliability testing.
	Slides MO2BCO03 [2.336 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO2BCO03
About •	Received ※ 06 October 2023 — Revised ※ 08 October 2023 — Accepted ※ 14 November 2023 — Issued ※ 13 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Strategy and Tools to Test Software in the SKA Project: The CSP. LMC Case

34

G. Marotta, C. Baffa, E. Giani
INAF - OA Arcetri, Firenze, Italy
G. Brajnik
IDS, Udine, Italy
M. Colciago, I. Novak
Cosylab Switzerland, Brugg, Switzerland

The Square Kilometre Array (SKA) Telescope will be one of the largest and most complex scientific instruments ever built. The development of a reliable software for monitoring and controlling its operations is critical to the success of the entire SKA project. The Local Monitoring and Control of the Central Signal Processor (CSP. LMC) is a software responsible for controlling a key subsystem of the telescope, i.e. the Central Signal Processor (CSP). The software is implemented as a "device" within the TANGO framework, written in Python. In this paper we describe a testing strategy that addresses some typical problems of such a large and complex instrument. It is a multi-level strategy, based on a combination of automated tests (unit/component/integration), in the context of CI/CD practices. Software is also tested against errors and anomalous conditions that can occur while the CSP. LMC is interacting with external subsystems, which can be simulated. The paper also discusses needs and solutions based on data mining test results. This allows us to obtain statistics of unexpected failures and to investigate their causes. Furthermore, a database containing test results supports discovery of interesting and unexpected patterns of behaviors of the tests based on correlations about different test-related events and data. This helps us to develop a deeper understanding of the code’s functioning and to find suitable solutions to minimize unexpected behaviors. In addition it can be used also to support reliability testing.

Slides MO2BCO03 [2.336 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO2BCO03

About •

Received ※ 06 October 2023 — Revised ※ 08 October 2023 — Accepted ※ 14 November 2023 — Issued ※ 13 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)