Test Object Property Recommendation Engine
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Solution Overview
Problem
Users face challenges in determining a set of properties for a programmatic description of test objects in test automation software, as existing methods require trial and error, especially when objects have similar property/property value combinations, and objects may not be stored in object repositories or exist dynamically.
Innovation Solution
A recommendation engine within the test automation engine suggests properties for a programmatic description by performing statistical analysis on how similar test objects have been defined in prior scripts, recommending the most frequently used property combinations and providing auto-completion features to streamline the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually determine properties for programmatic descriptions of test objects, then they can identify objects in test automation, but the process requires trial and error and is time-consuming
Solution Approach 1:
The system performs preliminary statistical analysis on property combinations from prior scripts before the user needs to define a programmatic description. By pre-calculating and storing the most frequently used property combinations for each test object class, the system eliminates the need for users to perform trial and error during actual test development, thus resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The recommendation engine enables the system to serve itself by automatically analyzing historical script data and generating property combination recommendations without requiring manual intervention. The system uses its own stored script data to improve the property selection process, making the time-consuming analysis work automatic and reusable across multiple test object definitions.
2Adaptability or versatility
If users rely on object repositories to store test objects, then object identification is standardized, but objects that exist dynamically or are not stored in repositories cannot be identified
Solution Approach 1:
The system segments the property selection process into two independent parts: (1) automatic recommendation of property combinations based on statistical analysis, and (2) user validation and selection. This segmentation allows the system to handle diverse object types (dynamic and non-repository objects) without increasing overall process complexity, as the automated recommendation handles the analytical complexity while the user handles the decision-making.
3Ease of operation
If the system provides detailed property options for all test objects, then users have complete control, but users are overwhelmed by the number of options and cannot determine the correct property set
Solution Approach 1:
The recommendation engine acts as an intermediary between the comprehensive property database and the user. It filters and processes the large set of available properties by analyzing historical usage patterns, then presents a curated subset of the most relevant property combinations. This intermediary function preserves complete property information in the background while simplifying the user interface to show only the most useful options, thus resolving the contradiction between ease of operation and information loss.
Data Source
AI summary
A technique includes receiving, by a computer, user input representing creation of a first programmatic description of a first test object of source code to be tested. The technique includes, in response to receiving the user input, determining, by the computer, based on other programmatic descriptions of other test objects, a recommendation of a parameter to be used in the first programmatic description to identify the first test object. The technique includes causing, by the computer, a display of the recommendation.