Predictive maintenance and software ageing analysis
Overview
SAGE (Software Aging and Governance Explorer) is an innovative project aimed at the early detection of errors and the reduction of maintenance costs in software projects. By combining predictive maintenance, error prediction and social analysis, SAGE supports development teams in the targeted improvement of software quality and stability.
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Project goals
- Predictive maintenance: By analyzing historical data and code changes, SAGE enables precise prediction of which code areas require maintenance before problems arise.
- Error prediction: Identification of error-prone code sections using a broad collection of metrics (e.g. bus factor, cognitive complexity, change rate).
- Social analysis: Examining code participation through social metrics such as bus factor and degree of knowledge to better distribute knowledge within the development team and minimize knowledge silos or dependencies.
Technology and methodology
- SAGE uses data-driven approaches that combine machine learning and interactive data visualization techniques. Metrics from the entire lifecycle of the code are collected and analyzed to gain comprehensive insights into the aging behavior of the software and to support decision-making.
Contact Us
Interested in a demonstration or more information? Contact us at ASchatten@sba-research.org and/or PKoenig@sba-research.org. Discover how SAGE helps development teams to ensure the quality and sustainability of their software projects in the long term.
Futher information
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