ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / This paper presents the description of the updated Data Display Markup Language (DDML), a neutral format for data display configurations. The development of DDML is motivated by the fact that in joint service program systems, there is a critical need for common data displays to support distributed T&E missions, irrespective of the test location, data acquisition system, and display system. DDML enables standard data displays to be specified for any given system under test, irrespective of the display vendor or system in which they will be implemented. The version 3.0 of DDML represents a more mature language than the version 1.0 presented at the 2003 ITC. The updated version has been validated for completeness and robustness by developing translators between DDML and numerous vendor formats. The DDML schema has been presented to the Range Commander’s Council (RCC) Data Multiplex Committee for consideration for inclusion in the IRIG 106 standard. The DDML model will be described in terms of both the XML schema and the UML model, and various examples of DDML models will be presented. The intent of this paper is to solicit specific input from the community on this potential RCC standard.

Over the past years, many software industries have adapted the Software Product Line(SPL) as a paradigm that empowers software reuse by exploiting software similarities and managing variabil- ities to enable high-quality deliverables with a shorter time to market. Nevertheless, the lifecycle of SPL development often faces complex tasks. Creating a specific product from the product family is the main challenge. One way towards product realization is through configuration files. Still, manually creating configuration files for each product is an error-prone and time-consuming activ- ity. Therefore, this thesis proposes a variability modeling approach that shall enable an automatic generation of the configuration files for a single product. We conduct a thorough investigation on how to model variability to support automatic generation of the configuration files, introduce four essential decisions related to variability expression, features, constraints and configuration transformation, and present a number of alternative solutions to these decisions. Moreover, we identify evolution scenarios of SPL and evaluate the decisions concerning the scenarios. Finally, a validation of the approach in an industrial case study provided by Bombardier Transportation is presented.

Configuration files are a vital part of any server setup. Knowing what configuration files have been manipulated is vital in knowing what services are running and how they are configured on a system. Changes made to these configuration files must be manually documented or collected using some automated documentation tool. Both these methods rely on being implemented early for the changes to be sufficiently documented. If a system didn’t implement these methods of documentation then there is no absolute way of knowing what changes have been made to that system without doing a manual search through potentially hundreds of files to find changes. Finding all changes made to a system lacking documentation is a time-consuming and arduous task. What if the search for changes could be automated once the documentation is long overdue? This thesis aims to create an implementation that finds changes made to configuration files on a Debian system as well as Linux distributions based on Debian such as Ubuntu. The resulting implementation created is called Doc-Overdue. Doc-Overdue consists of a script written in Python and utilizes the Debian Package Management System to find reference files to compare to the configuration files on the system. The script also runs tests to aid in finding new and/or modified configuration files without a reference file available. The results are presented in a formatted comprehensive report automatically created by Doc-Overdue. The report will include change reports created by the Diff-utility that makes it easy to find exactly what changes have been made to each changed configuration file. This approach finds changes made to systems without the need for any prior documentation of the system. The implementation was tested with a custom-made script that made modifications to configuration files for Doc-Overdue to find. The test showed Doc-Overdue to have an accuracy of 87.5% in finding changed configuration files on the system. This result shows that this approach for finding configuration file changes has a lot of promise. The thesis ends with a discussion and a list of potential future work that could hopefully lead to further improvements in the field of configuration detection.

This thesis explores how large language models can be used to generate configuration files for Terraform from natural language descriptions. Few-shot and fine-tuning paradigms are evaluated on decoder-only models of varying size, including the state-of-the-art Codex model. The generated configuration files are evaluated with regard to functional correctness on a custom dataset using Terraform, to account for the large space of functionally equivalent configuration files. Results show that the largest model Codex is very capable at generating configuration files given an English description of network infrastructure even without fine-tuning. The result could be a useful tool for engineers who know Terraform fundamentals and have experience with the cloud platforms: AWS, GCP, or Azure. A future study could fine-tune Codex for Terraform using OpenAI's API or create an open source Codex-replication by fine-tuning the GPT-3 replication OPT, which in turn can be \hbox{fine-tuned}. / Denna avhandling undersöker hur stora språkmodeller kan användas till att generera konfigurationsfiler för Terraform med hjälp av språkbeskrivningar. Både few-shot och fine-tuning paradigm utvärderas på decoder-only modeller i olika storlekar, inklusive Codex. För att ta hänsyn till konfigurationsfiler som i utseende ser olika ut men som är funktionellt ekvivalenta utvärderas konfigurationsfilerna utifrån deras funktion. Resultaten visar att Codex, som är den största modellen, har förmågan att generera konfigurationsfiler givet en engelsk beskrivning av nätverksinfrastruktur, trots att Codex inte har undergått fine-tuning. Resultatet kan vara ett användbart verktyg för ingenjörer som har grundläggande kunskap om Terraform och erfarenhet av molnplattformarna: AWS, GCP eller Azure. En framtida studie skulle kunna träna Codex för Terraform med OpenAI:s API eller skapa en Codex-kopia genom att träna GPT-3 kopian OPT som i sin tur kan bli tränad för Terraform.