Extending JIRA to Enable High Volume KPI Benchmarking - Keyur Patel

1. #atlassian 2. Extending JIRA to Enable High-VolumeKPI BenchmarkingKEYUR PATEL • SOFTWARE QUALITY • INTEL • @keyurpatel_rwww.linkedin.com/in/keyurpatel00/ 3. Agenda!1. Traditional approach followed by problem statement2. Explore Jira capabilities outside of bug tracking3. Key takeaways for you to implement this in your teams 4. Problem with traditional approach!• All the data is spread across millions of XLs - limitingmanagement visibility• Lacking data traceability against code check-ins• Limited data analytics, with 2-D statistics• Slow turn around time for model changesQALABStakeholders 5. But… where do we start?"KEEP ITSIMPLESTUPIDScalable – to supportall business unitgroups across IntelBuild it FAST!Ship it OR get out ofthe way 6. Our Approach… advance the art of benchmarking across Intel. 7. 1 Jira REST 2Invent within the scope ofavailable tool set!4 3Reporting andAnalytical tools 8. Blue print!Teams / ProjectsIntel BenchmarkingLibraries (KPIs)Standardize: ConsistentQuality Standards,Metrics and CalculationMethods across IntelBenchmarking KPIs like…• User experience• Performance against key benchmarks Powerconsumption• Power consumption• Audio quality• Graphics quality• … 9. Blue print!QA Lab /Testing toolsValidation ErrorsFlat FileJira RESTweb servicesProjectSpecific BenchmarksJira importScriptTeams / ProjectsIntel BenchmarkingLibraries (KPIs)Weekly test dataStandardize: RE-USE benchmarkingConsistentQuality standards Standards,and bestMetrics methods and amongstCalculationMethods projects across across IntelIntelJIRA –Collect,Service, andReportAutomate: Eliminatemanual efforts of trackingindividual benchmarksDrop off folder 10. Project specific KPIsWeekly / Dailymeasurements fromQA LABThreadeddiscussionsHistory and recentactivityOwners, watchersand timestampSupporting files anddata 11. Blue print!EmailValidation ErrorsFlat FileQA Lab / NotificationTesting toolsDashboard: Data fed intoJira and ready fordashboard consumption(real time)RelationalDatabaseJira RESTweb servicesProjectSpecific BenchmarksJira ImportScriptTeams / ProjectsIntel BenchmarkingLibrariesWeekly test dataJira plugin chartsEnhanced data analyticsDrop off folder 12. History trend: Ability totrack historical data andanalyze KPI trends withinJira. 13. Impact"• Eliminated huge problem of data residing in distributed systems via manualcollection ( = !Spreadsheet Hell)• Single source of truth• Sleek and easy to use front-end GUI, that everyone knows• Traceability over Benchmark measurements against Code check-ins… Make benchmarking a snap! 14. Key takeaways:#atlassian• Primary goal: 100% Re-use and Maximize Automation• It’s easy to get lost in the features of the product - don’t neglect the point thatyou have to develop, deploy and most importantly maintain it easily• With any large organization, creating a standard methodology that all teams buyinto is a challenge. This is an upfront exercise which requires high levelsponsorship and direction.• Leverage plug-ins and tools from Atlassian ecosystem to avoid re-creating thewheel. E.g. Native support for triggering field transfers from parent issue to linkedissue(subtask) using Innovalog workflow extensions plugin! 15. Expanding JIRA Capabilities!• Implementing similar functionalities would be greatly benefitted by an expansionof Jira capabilities natively in data warehousing and configuration management.• A Robust, Atlassian backed ecosystem would be a key enabler for Jiracustomers to similarly expand the tool’s capabilities beyond just defect tracking 16. Thank you!KEYUR PATEL • INTEL SOFTWARE QUALITY • @keyurpatel_rwww.linkedin.com/in/keyurpatel00/I would really like to thank BlackPearl for partnering on this effort. For more details onimplementation please visit their booth. 17. Backup 18. For detailed questions onimplementation please contact!ALM Tool Development.Atlassian Experts