ICALEPCS2023 - List of Keywords (software-component)

Paper	Title	Other Keywords	Page
MO2BCO03	Strategy and Tools to Test Software in the SKA Project: The CSP. LMC Case	software, TANGO, controls, framework	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)

MO4BCO03	Protecting Your Controls Infrastructure Supply Chain	software, controls, operation, framework	196
	B. Copy, F. Ehm, P.J. Elson, S.T. Page, J.-B. de Martel CERN, Meyrin, Switzerland M. Pratoussy CPE Lyon, Villeurbanne, France L. Van Mol Birmingham University, Birmingham, United Kingdom
	Supply chain attacks have been constantly increasing since being first documented in 2013. Profitable and relatively simple to put in place for a potential attacker, they compromise organizations at the core of their operation. The number of high profile supply chain attacks has more than quadrupled in the last four years and the trend is expected to continue unless countermeasures are widely adopted. In the context of open science, the overwhelming reliance of scientific software development on open-source code, as well as the multiplicity of software technologies employed in large scale deployments make it increasingly difficult for asset owners to assess vulnerabilities threatening their activities. Recently introduced regulations by both the US government (White House executive order EO14028) and the EU commission (E.U. Cyber Resilience Act) mandate that both Service and Equipment suppliers of government contracts provide Software Bills of Materials (SBOM) of their commercial products in a standard and open data format. Such SBOM documents can then be used to automate the discovery, and assess the impact of, known or future vulnerabilities and how to best mitigate them. This paper will explain how CERN investigated the implementation of SBOM management in the context of its accelerator controls infrastructure, which solutions are available on the market today, and how they can be used to gradually enforce secure dependency lifecycle policies for the developer community.
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO4BCO03
About •	Received ※ 02 October 2023 — Revised ※ 10 October 2023 — Accepted ※ 14 November 2023 — Issued ※ 24 November 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TU1BCO03	Systems Modelling, AI/ML Algorithms Applied to Control Systems	monitoring, hardware, software, controls	257
	S.A. Mnisi SARAO, Cape Town, South Africa
	Funding: National Research Foundation (South Africa) The 64 receptor (with 20 more being built) radio telescope in the Karoo, South Africa, comprises a large number of devices and components connected to the Control-and-Monitoring (CAM) system via the Karoo Array Telescope Communication Protocol (KATCP). KATCP is used extensively for internal communications between CAM components and other subsystems. A KATCP interface exposes requests and sensors; sampling strategies are set on sensors, ranging from several updates per second to infrequent on-change updates. The sensor samples are of different types, from small integers to text fields. The samples and associated timestamps are permanently stored and made available for scientists, engineers and operators to query and analyze. This is a presentation on how to apply Machine Learning tools which utilize data-driven algorithms and statistical models to analyze sensor data sets and then draw inferences from identified patterns or make predictions based on them. The algorithms learn from the sensor data as they run against it, unlike traditional rules-based analytics systems that follow explicit instructions. Since this involves data preprocessing, we will go through how the MeerKAT telescope data storage infrastructure (called Katstore) manages the voluminous variety, velocity and volume of this data.
	Slides TU1BCO03 [1.647 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TU1BCO03
About •	Received ※ 06 October 2023 — Revised ※ 09 November 2023 — Accepted ※ 14 December 2023 — Issued ※ 21 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MO2BCO03

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

software, TANGO, controls, framework

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)

MO4BCO03

Protecting Your Controls Infrastructure Supply Chain

software, controls, operation, framework

196

B. Copy, F. Ehm, P.J. Elson, S.T. Page, J.-B. de Martel
CERN, Meyrin, Switzerland
M. Pratoussy
CPE Lyon, Villeurbanne, France
L. Van Mol
Birmingham University, Birmingham, United Kingdom

Supply chain attacks have been constantly increasing since being first documented in 2013. Profitable and relatively simple to put in place for a potential attacker, they compromise organizations at the core of their operation. The number of high profile supply chain attacks has more than quadrupled in the last four years and the trend is expected to continue unless countermeasures are widely adopted. In the context of open science, the overwhelming reliance of scientific software development on open-source code, as well as the multiplicity of software technologies employed in large scale deployments make it increasingly difficult for asset owners to assess vulnerabilities threatening their activities. Recently introduced regulations by both the US government (White House executive order EO14028) and the EU commission (E.U. Cyber Resilience Act) mandate that both Service and Equipment suppliers of government contracts provide Software Bills of Materials (SBOM) of their commercial products in a standard and open data format. Such SBOM documents can then be used to automate the discovery, and assess the impact of, known or future vulnerabilities and how to best mitigate them. This paper will explain how CERN investigated the implementation of SBOM management in the context of its accelerator controls infrastructure, which solutions are available on the market today, and how they can be used to gradually enforce secure dependency lifecycle policies for the developer community.

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO4BCO03

About •

Received ※ 02 October 2023 — Revised ※ 10 October 2023 — Accepted ※ 14 November 2023 — Issued ※ 24 November 2023

Cite •

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

TU1BCO03

Systems Modelling, AI/ML Algorithms Applied to Control Systems

monitoring, hardware, software, controls

257

S.A. Mnisi
SARAO, Cape Town, South Africa

Funding: National Research Foundation (South Africa)
The 64 receptor (with 20 more being built) radio telescope in the Karoo, South Africa, comprises a large number of devices and components connected to the Control-and-Monitoring (CAM) system via the Karoo Array Telescope Communication Protocol (KATCP). KATCP is used extensively for internal communications between CAM components and other subsystems. A KATCP interface exposes requests and sensors; sampling strategies are set on sensors, ranging from several updates per second to infrequent on-change updates. The sensor samples are of different types, from small integers to text fields. The samples and associated timestamps are permanently stored and made available for scientists, engineers and operators to query and analyze. This is a presentation on how to apply Machine Learning tools which utilize data-driven algorithms and statistical models to analyze sensor data sets and then draw inferences from identified patterns or make predictions based on them. The algorithms learn from the sensor data as they run against it, unlike traditional rules-based analytics systems that follow explicit instructions. Since this involves data preprocessing, we will go through how the MeerKAT telescope data storage infrastructure (called Katstore) manages the voluminous variety, velocity and volume of this data.

Slides TU1BCO03 [1.647 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TU1BCO03

About •

Received ※ 06 October 2023 — Revised ※ 09 November 2023 — Accepted ※ 14 December 2023 — Issued ※ 21 December 2023

Cite •

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