Transaction Impact Identification via Code Element Intersection
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Solution Overview
Problem
Testing software systems, such as ERP systems, is an expensive and labor-intensive process due to the complexity of determining which transactions are impacted by configuration changes, often requiring full regression testing that is costly and time-consuming, and identifying these impacts is difficult due to unknown dependencies.
Innovation Solution
A method and system that analyze activity and program data from multiple organizations to identify transactions likely to be impacted by configuration changes by connecting code elements and transactions across different software systems, using weighting modules to prioritize tests based on user interactions and organizational associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full regression testing is performed to validate configuration changes, then reliability of software system validation is improved, but loss of time and productivity deteriorate due to the extensive testing required
Solution Approach 1:
The patent segments the set of all transactions into two subsets: those likely to be impacted by the configuration change (identified through code element analysis and organizational knowledge) and those not likely to be impacted. This segmentation allows testers to focus only on the relevant subset, reducing testing time while maintaining validation reliability for critical areas.
Solution Approach 2:
Instead of performing complete regression testing on all transactions, the patent applies partial action by selectively testing only those transactions identified as likely to be impacted by the configuration change. This partial testing approach is sufficient to validate the configuration change without the excessive time cost of full regression testing.
2Measurement precision
If comprehensive testing of all transactions is conducted to ensure complete validation, then measurement precision of configuration change impact is improved, but device complexity and difficulty of operation worsen due to the complex analysis required to identify impacted transactions
Solution Approach 1:
The patent performs preliminary action by pre-analyzing code elements and their associations with transactions before the configuration change is made. This pre-established knowledge base of code element-transaction relationships enables precise identification of impacted transactions without requiring complex real-time analysis during the testing phase.
Solution Approach 2:
The patent introduces code elements as intermediaries that connect configuration changes to transactions. By analyzing the relationship between configuration changes and code elements, and then between code elements and transactions, the system precisely identifies impacted transactions without requiring direct complex analysis of all transaction-configuratio n change relationships.
3Adaptability or versatility
If each organization independently builds knowledge of dependencies and side effects, then adaptability to specific organizational needs is improved, but loss of time and resources worsen due to redundant independent discovery efforts
Solution Approach 1:
The patent merges the knowledge acquisition efforts of multiple organizations by collecting and analyzing code element usage patterns across different organizational contexts. This combined knowledge base captures diverse organizational needs and dependencies, allowing each organization to benefit from collective insights without independently rediscovering the same knowledge.
Solution Approach 2:
The patent creates a universal knowledge base of code element-transaction associations that serves multiple organizations with different needs. This multi-functional system adapts to specific organizational requirements while leveraging data from various organizations, reducing the time each organization needs to spend on independent knowledge acquisition.
Data Source
AI summary
System, method, and non-transitory medium for identifying a certain transaction likely to be impacted by a certain configuration change. An activity analyzer receives activity data of users belonging to different organizations and identifies, based on the activity data, a first set of code elements associated with transactions. A program analyzer receives the certain configuration change, and program data that describes at least one of the following: procedure invocations, user interface statements, and database access statements. The program analyzer identifies a second set of code elements that are influenced by the certain configuration change. An intersection module calculates an intersection between the first set of code elements and the second set of code elements. A transaction identifier identifies the certain transaction likely to be impacted by the certain configuration change based on a common code element belonging to the intersection.