Cascaded GUI Software Feedback Assessment
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Solution Overview
Problem
Conventional methods for assessing software product feedback lack clarity on evaluated aspects, correlation with global usage, and authenticity, making it difficult for users to determine the quality of software products.
Innovation Solution
A systematic approach using cascaded graphical user interfaces (GUIs) to capture and evaluate user feedback data at multiple levels, incorporating user role, account type, and life-cycle phase weightings, providing statistically robust and detailed average ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feedback assessment methods are used, then the feedback collection process is simple, but the clarity and detail of evaluated aspects is insufficient
Solution Approach 1:
The feedback assessment system is segmented into multiple hierarchical levels (global level, aspect level, feature level) allowing feedback to be collected and analyzed at different granularities. This segmentation enables precise evaluation of specific software aspects while maintaining an overall feedback structure, resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The system introduces multiple dimensions for feedback assessment including user role, account type, life-cycle phase, and software aspect. By adding these dimensional layers, the system achieves comprehensive and precise evaluation of software qualities beyond simple overall ratings, transforming the feedback assessment from a single-dimension to multi-dimensional analysis.
2Reliability
If simple feedback collection is used, then the feedback gathering process is easy, but the correlation between feedback data and global usage is lacking
Solution Approach 1:
The system implements a feedback mechanism that correlates user feedback with global usage statistics. By integrating usage data (number of users, usage frequency, time stamps) with feedback ratings, the system enhances the reliability of feedback assessment, allowing stakeholders to understand both what users think and how widely the software is actually used.
3Loss of information
If manual feedback research is required, then comprehensive information can be gathered, but the time and effort required is excessive
Solution Approach 1:
The system enables automatic collection, aggregation, and analysis of feedback data from multiple sources. Instead of requiring manual research, the system self-services by automatically gathering feedback, correlating it with usage data, and presenting synthesized results, thereby reducing time loss while maintaining information completeness.
Solution Approach 2:
The system merges multiple feedback sources and usage data into a unified assessment framework. By combining feedback from different users, aspects, and time periods with global usage statistics, the system provides comprehensive information automatically, eliminating the need for separate manual research efforts.
4Adaptability or versatility
If multiple software aspects are evaluated, then the evaluation becomes more comprehensive, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system segments software evaluation into distinct aspects (functionality, performance, usability, etc.) and features within each aspect. This segmentation makes it easier to detect and measure specific qualities independently while maintaining comprehensive evaluation coverage, as each aspect can be assessed separately and then aggregated.
Data Source
AI summary
In one embodiment, feedback data of a software object is received through a sequence of cascaded GUIs. The cascaded GUIs include an interaction portion to receive the feedback data from users at a plurality of feedback levels. Further, user role weightings of the users, account weightings of enterprises associated with the users and a time weighting corresponding to a life-cycle phase of the software object are retrieved. Furthermore, average rating of the software object corresponding to each feedback level is determined as a function of the user role weightings, the account weightings, the time weighting, the feedback data corresponding to a feedback level and a number of users submitted the feedback data. The determined average ratings and rating distribution corresponding to each feedback level are graphically displayed on the interaction portion associated with a next feedback level.