IDENTIFYING SOFTWARE REGRESSIONS IN A PRODUCTION CLOUD ENVIRONMENT

Smart overview of the Invention

Modern software systems are intricate, consisting of numerous modules and components that must operate seamlessly. As these systems evolve, they undergo continuous updates to introduce new features, improve performance, or fix bugs. However, these changes can inadvertently introduce regressions, which are unexpected behaviors or failures in previously functioning components. Identifying and addressing these regressions promptly is crucial to maintaining software quality and reliability. Traditional regression identification methods, such as manual testing or automated test suites, have limitations, including being time-consuming, resource-intensive, and prone to errors.

Challenges in Cloud Environments

Cloud computing environments add complexity to the challenge of identifying software regressions. Cloud applications comprise multiple services and dynamic resources that interact across a networked infrastructure. The rapid development and deployment cycles in cloud environments, enabled by continuous integration and delivery (CI/CD) practices, further complicate regression detection. Cloud systems' distributed and ephemeral nature, along with dynamic resource allocation and auto-scaling, make it difficult for traditional methods to capture and diagnose regressions, especially those that occur under specific conditions or configurations.

Proposed Solution

This approach proposes using saved copies of actual customer workloads in production cloud environments to proactively identify software regressions. By running real customer workloads, it is possible to detect regressions and identify their causes, facilitating quicker resolution. This method aims to address the limitations of traditional testing tools, which often struggle to distinguish between regressions and intentional code changes, leading to false positives and wasted developer time.

Implementation Details

Figures and flowcharts illustrate the architecture and operation of the proposed solution. The architecture involves detecting regressions using real customer workloads, as shown in FIG. 1 and FIG. 3. The operation flowcharts in FIG. 2 and FIG. 4 detail the sequence of steps applied in the detection process. Block diagrams in FIG. 5 and FIG. 6 depict the computing system and environment components suitable for implementing this solution, highlighting how services may be offered as cloud services.

Benefits and Conclusion

Cloud providers play a crucial role in administering and maintaining cloud resources, minimizing customers' IT staffing and infrastructure needs. Frequent updates or patches, which may include hundreds of changes, are necessary to enhance software functionality. However, these updates can introduce regressions, making early detection and identification of the "culprit transaction" essential. By implementing this proactive approach, cloud customers can rely on the proper functioning of cloud systems, ensuring their work is performed efficiently and effectively.