Our client was facing issues in evaluating the software stability since the current models for assessment fall short in providing a comprehensive picture of real-world reliability. While software releases might be labeled "stable" for a specific version, this doesn't clearly indicate actual stability. Moreover, these methods lack visual representations of stability data, making it difficult for stakeholders to understand an application's true stability.

Our solution addresses this gap by offering a thorough and visually intuitive representation of stability metrics, enabling better-informed decision-making.

The customer is one of the largest investment management firms providing solutions to institutions, financial professionals, and millions of individuals worldwide.

• Limited Transparency in Stability Labeling: Current methods simply label a software release as "stable" without quantifying the level of stability. This lack of detail makes it difficult to compare stability between versions.
• Absence of Visual Representation: Existing models focus on textual data, making it hard for stakeholders to grasp stability information quickly and intuitively.
• Incomplete Assessment: Current approaches might not consider all aspects of stability, potentially overlooking factors that could impact real-world performance.
• Decision-Making Difficulty: Without clear and comprehensive stability information, stakeholders, especially decision-makers, struggle to make informed choices about deployment, resource allocation, or future development efforts.

The Jewel application tackles the challenges of application stability assessment by offering a comprehensive and visually intuitive representation of stability metrics.

This section dives into two key scores displayed within the application:

• Application Stability Score: This score reflects the overall health and reliability of your application. A higher score indicates a more stable application with fewer functionality issues. The score is calculated using a sophisticated algorithm that considers various factors:

1. Average Fix Time: Measures the average time taken to resolve bugs or unexpected failures.
2. Downtime: Captures the total duration of application outages or unavailability.
3. Failed Test Cases & Suites: Tracks the number of individual test cases and entire test suites that fail during automated testing.
4. Broken Index: This value represents the historical stability of test suites based on their past execution results.
5. Variances: This feature allows users to account for temporary anomalies by marking unexpectedly failed test cases as "false positives." This ensures the Application Stability Score accurately reflects the software's true stability, unaffected by temporary issues.

• Automation Stability Score: This score specifically focuses on the reliability and effectiveness of your automated testing suite. A high score indicates that your tests are consistently identifying and reporting issues without unexpected automation errors. The calculation considers factors like:

1. Test Execution Success Rate: Measures the percentage of test cases that execute successfully without unexpected failures.
2. Flaky Test Rate: Tracks the number of tests that exhibit inconsistent behavior, sometimes passing and sometimes failing for the same reasons.
3. Test Maintenance Effort: Captures the resources and time required to maintain and update the automated test suite.

The Jewel application visually combines these scores, helping stakeholders understand application health and automated testing effectiveness. This clarity is crucial for informed decisions on software releases, resource allocation, and development strategy.

Integrated into the Jewel application, this model offers several benefits:

• Enhanced Communication and Decision-Making: Users can share detailed, visually clear stability reports, aiding stakeholders in understanding software stability for better strategic planning and resource allocation.
• Actionable Insights: Reports reflect true stability by allowing users to mark unexpected failures as false positives, preventing skewed data from temporary anomalies.
• Credible and Transparent Reporting: Users can annotate delayed bug resolutions with explanations and expected code fix dates, ensuring test cases are accurately marked and maintaining report credibility.
• Proactive Management: By addressing false positives and variances, users can proactively manage stability, mitigating known issues and maintaining overall software stability over time.