Sentiment Tracking System for Software Tester Frustration
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Solution Overview
Problem
Software development processes often fail to capture subjective tester frustration during software testing, leading to unrecorded issues due to subjective judgment on the importance of defects, which can result in missed potential problems before deployment.
Innovation Solution
A ticket issue/sentiment tracking system that correlates feedback data from testers with software instances to derive sentiment ratings, using natural language processing and sensor data to quantify frustration levels in real-time, allowing for resource allocation to address issues proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional tracking systems are used to manage testers, then device complexity is reduced, but loss of information occurs as subjective tester frustration and sentiment are not captured
Solution Approach 1:
The patent introduces sentiment analysis technology as an intermediary between testers and the tracking system. This intermediary automatically captures and quantifies tester sentiment from feedback data, converting subjective feelings into objective metrics that the tracking system can process and store, thereby preventing loss of sentiment information without requiring direct manual input from testers.
Solution Approach 2:
The patent replaces manual sentiment assessment with automated sentiment analysis technology. Instead of relying on testers to manually report frustration levels or developers to subjectively evaluate feedback importance, the system uses natural language processing and sentiment analysis algorithms to automatically extract and quantify sentiment from feedback data, eliminating the need for manual intervention in sentiment capture.
2Productivity
If manual defect prioritization is used, then device complexity is low, but productivity decreases due to inability to proactively address critical issues
Solution Approach 1:
The patent implements a feedback loop where sentiment analysis results continuously inform resource allocation decisions. The system monitors tester sentiment in real-time, automatically prioritizes defects based on sentiment intensity and frequency, and dynamically adjusts resource allocation to address high-sentiment issues first. This closed-loop feedback mechanism enables proactive defect management without manual intervention.
Solution Approach 2:
The patent transforms qualitative sentiment data into quantitative parameters that can be directly used for prioritization. By converting tester feedback into sentiment scores, intensity levels, and frequency metrics, the system creates objective parameters that automatically drive defect prioritization and resource allocation decisions, replacing subjective manual judgment with data-driven parameter-based decision making.
3Measurement precision
If subjective judgment is used to determine defect importance, then measurement precision is low, but ease of operation is high
Solution Approach 1:
The patent enables the system to automatically perform sentiment analysis and defect prioritization without requiring manual operation. The sentiment analysis technology processes feedback data autonomously, automatically extracts sentiment information, quantifies frustration levels, and generates prioritization recommendations. This self-service capability maintains ease of operation while dramatically improving measurement precision through automated objective analysis.
Data Source
AI summary
Aspects of the invention include a computer-implemented method for quantifying tester sentiment. The computer-implemented method includes receiving a first feedback data from a first tester, where the first feedback data represents feedback generated by the first tester in connection with a first instance of a first software-under-development. The first feedback data is correlated to an aspect of the first instance of the first software-under-development to derive a tester sentiment data. The tester sentiment data is used to determine a tester sentiment rating of the first instance of the software-under-development. Based on the tester sentiment rating, a resource is applied to the first instance of the first software-under-development.