Jira™ Improvements for Automated Software and Hardware Reliability Analysis of Distributed Satellite Ground Stations

SUMMARY & CONCLUSIONSThis paper encourages the reader to consider different approaches for characterizing and tracking software Reliability and Availability (R&A) metrics, where reliability is a design characteristic measure of mission performance, and availability is a performance characteristic of operational capability. The primary use case examined in this paper considers large, complex distributed satellite ground stations developed using test-driven methodologies, e.g. Agile. In both cases, whether generating reliability or availability metrics, it can be quite difficult to accurately predict performance without having key data sets. Considering Atlassian’s Jira™ as an industry preferred tool for Agile development, the desire is to automate calculations using data sets from Jira™. However, the default template that Jira™ provides to document faults is inadequate for developing such an automated process without modification, i.e. formalizing as many fields as possible with dropdown menus. This simple modification enables various R&A metrics to be automated based on real data. In turn, automation helps develop fault densities, Mean Time Between Failures (MTBF), Availability (Ao), and other desired metrics.To ensure metrics are accurately and efficiently generated, it is necessary to develop and implement automation techniques in a way that aligns with software reliability activities, i.e. IEEE-1633 [1]. Failure Reporting Analysis and Corrective Action (FRACA) systems are rich data sources that can be exploited in this manner, e.g. software defects, defect density, etc. Therefore, it is recommended that test-driven methodologies and tools are adapted as soon as practical so R&A attributes can be inherently integrated into the fabric of the system design and architecture. Doing so allows for a comprehensive dataset that is needed to generate essential metrics. Once the framework is created, sample data sets are used to demonstrate proof of concepts. In summary, generating automated calculation of R&A metrics, utilizing Jira™ data, drives efficiency into the various activities and enhances the fidelity of the generated metrics. This paper walks through the framework and recommendations to implement into Jira™, the process behind utilizing that information, and results from a proof of concept.