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A system and method of adaptive testing scope recommendation with cognitive learning system Disclosure Number: IPCOM000248952D
Publication Date: 2017-Jan-24
Document File: 6 page(s) / 160K

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The Prior Art Database


For the current functional testing, test scope and scenarios are designed by tester and developer manually. The quality of the design is limited by personal knowledge and experience about business logic and code logic. Under current situation, the pain point is that it is very difficult to decide test scope and design test scenarios fully covered all code changes. Especially for new comers, who have no enough experience about code or business logic. And also, for highly coupled systems, sometimes functions not often called could be easily missed. This disclosure will be a new method to improve test scenarios evaluation and test case design based on cognitive computing. Here is the explanation for the abbreviation will be appeared below: API: Application Programming Interface. Alchemylanguage: AlchemyLanguage is a collection of natural language processing APIs that help you understand sentiment, keywords, entities, high-level concepts and more. UI: User Interface. DCUT: Development Complete Unit Test QA: Quality assurance.

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A system and method of adaptive testing scope recommendation with cognitive learning system

The disclosure integrates the cognitive computing technology and tracking tool among business documents, code repository and test repository. Currently, the documentation relationship it is mentioning is most refreshed via source code management tools or project management tool. Developers and testers have to manually link the business requirement, change set and test cases in these tools. The relationship will be saved so that team members can manually find the related test cases, change set and requirement by one of them. After integrating the cognitive computing technology, the traversal method will be automatic. The method will analyze the relationship, when there is any update in the code (change set), the method will recommend the scenarios for testing purpose, and the analysis and recommendation can be updated automatically or manually. This method provides a supplement for test scenarios design and as far as possible to avoid the missing caused by man-made.

The disclosure includes one cognitive computing service and a recommendation engine. The analyzed data is loaded from code repository, business scenario documents, test case documents and test record repository. After analysis, the disclosure will generate a report or list of scenarios impacted as well as recommended test cases.

The whole process is shown in below picture:


The steps numbered in above picture is explained at below:

- Step 1: The analysis starts when developer commits the code change and changes the task-tracking-system work item status to a given one, e.g. "DCUTdone" or "Ready for build", then the process triggers a static analysis based on code, business documents and test case documents.

- Step 2: The static analysis generates a basic set of impacted business scenarios and test cases. Meanwhile, there is also a timely refreshed mapping, generated from the task tracking tool whic...