State of AI applied to Quality Engineering 2021-22
Section 8: Operate

Chapter 2 by Tricentis

Smart impact analysis for SAP

Business ●●●○○
Technical ●●○○○

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When new changes are introduced to an SAP system, LiveCompare answers the critical question, “what to test?” Its AI-powered capabilities identify the risks that SAP updates pose to SAP business processes, system integrations, custom code, security, governance, and more.

When it comes to testing updates to packaged applications like SAP and Salesforce, most organizations lean to one of two extremes:

  • Test everything: In this approach, testers attempt to manually execute their entire regression suite before each release — a time-consuming and often inefficient exercise.
  • Test in production: This usually involves asking key business users to do some manual testing just before the update is released, then using hypercare to fix defects quickly after problems emerge in production.

Neither extreme is ideal, obviously. A smarter option? Test the right things. With this strategy, you consider using AI-driven impact analysis to learn which objects are most at-risk from an application update, and test only those. These are the “right things” to test because they are the sources of potential production defects. By identifying the right things to test, AI-driven impact analysis eliminates unnecessary testing, which cuts the average test scope for a release by 85%. In other words, it gives you up to 100% risk reduction for only 15% of the effort. This lets you deliver higher-quality releases faster than ever before, while preventing the defects that cause business disruption.

This is the strategy that The Coca-Cola Company adopted. Coca-Cola relies heavily on SAP, releasing a high volume of custom and standard transports throughout the year. Their IT team struggled with the overwhelming amount of manual analysis required to identify every object impacted by a software change. It simply took too much time, required too many people, and put too much of the company’s operations at risk. So, they turned to automation.

They now use a smart impact analysis tool, Tricentis LiveCompare, to automatically identify the SAP objects at risk from an update. The results are impressive. By automating risk analysis, teams at Coca- Cola can tailor a test plan for each release based on the actual risks to their business in that release. By testing the right things, they are not only speeding up their testing and minimizing business risk; they are also saving as much as 40% per release.

Figure: Test scope reduction from analysis tool


This AI-driven change impact analysis is implemented in Tricentis LiveCompare. It assesses packaged apps automatically to learn about their business processes, integrations, custom code, security permissions, and configurations. Then, it investigates the actual landscape and usage data from your production systems to determine the true risks posed by an application update.

For instance, LiveCompare's examination of SAP involves detecting integration points that make use of standard SAP interfacing techniques such as IDOC and BDC. LiveCompare then alerts the engineer whenever an update puts those integrations at risk.

Figure: SAP HANA Upgrade analysis


But knowing what to test is only the beginning. The next challenge is knowing which tests to run. Through integration with test automation tools such as Tricentis Tosca, LiveCompare can intelligently select the most appropriate regression tests to run for each object that needs testing. If no test for an at-risk object is detected, it highlights the coverage gap and generates requirements for the creation of associated tests.

Figure: LiveCompare Dashboard


For example, LiveCompare can look backwards at a customer’s historical app changes to identify “hotspots” that are frequently being updated and so are at heightened risk for ongoing functional and performance issues. Because updates frequently expose the same items to risk (“Create sales order” transactions, for example), quality engineers should prioritize automating tests for these hotspots.

Figure: LiveCompare identifys hotspots


Using this strategy to identify the appropriate items to test before to releasing an update eliminates the need for post-release hypercare. Hypercare periods after an update are common in many enterprises. During these periods, emergency teams are deployed to quickly diagnose and fix defects that are causing business disruption and/or downtime. Hypercare periods are costly, disruptive, and are usually accompanied by a marked decrease in business productivity. Hypercare periods, which can last weeks, can essentially constrain the rate at which teams can deliver innovation. If each delivery requires six weeks of hypercare, it makes no difference if your development teams work on a daily, weekly, or monthly basis. Hypercare will always slow down your ability to deliver innovation.

Ultimately, AI-driven smart impact analysis speeds up the testing process while reducing the actual number of defects released into production down to near zero.

Example: US Luxury Apparel Company


  • IT faced an 8-year backlog of SAP support packs to get current​
  • SAP environment consists of 6 landscapes: ECC6, BW, SRM, XI, BOBJ, and Enterprise Portal ​
  • Each landscape has SBX, DEV, QA, and PRD systems with a single path to production for each


  • Tool to understand how custom code would be impacted by each change​
  • Analytics to understand scope of testing to be done without testing everything


  • Near-zero defects across all support packs in ECC, BW, and SRM systems​
  • Full comparison of all objects on the SPAU list prior to remediation​
  • Full implementation staggered across three landscapes, over a period of 9 months​
  • Smart impact analysis is now used daily to compare code and data across instances​

About the author

Wolfgang Platz

Wolfgang Platz

Wolfgang is the force behind innovations such as model-based automation and the linear expansion test design methodology.  The technology he developed drives Tricentis’ Continuous Testing Platform, which is recognized as the industry’s #1 solution by all top analysts. Today, he is responsible for advancing Tricentis’ vision to make enterprise continuous testing a reality across Global 2000 organizations. His most recent book is “Enterprise Continuous Testing: Transforming Testing for Agile and DevOps.”

Prior to Tricentis, Wolfgang was at Capgemini as a group head of IT development for one of the world’s largest IT insurance-development projects. There, he was responsible for architecture and implementation of life insurance policies and project management for several projects in banks.

Wolfgang holds a Master’s degree in Technical Physics as well as a Master’s degree in Business Administration from the Vienna University of Technology.

About Tricentis

Tricentis is the global leader in enterprise continuous testing, widely credited for reinventing software testing for DevOps, cloud, and enterprise applications. The Tricentis AI-powered, continuous testing platform provides a new and fundamentally different way to perform software testing. An approach that’s totally automated, fully codeless, and intelligently driven by AI. It addresses both agile development and complex enterprise apps, enabling enterprises to accelerate their digital transformation by dramatically increasing software release speed, reducing costs, and improving software quality. Tricentis has been widely recognized as the leader by all major industry analysts, including being named the leader in Gartner’s Magic Quadrant five years in a row. Tricentis has more than 1,800 customers, including the largest brands in the world, such as McKesson, Accenture, Nationwide Insurance, Allianz, Telstra, Moet-Hennessy-Louis Vuitton, and Vodafone.

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