Software Installation Sequence Prediction Using Historical Success Data
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Solution Overview
Problem
Conventional software installation methods are inefficient when multiple software items need to be installed, as they require manual trial and error to find a valid installation sequence, leading to increased labor and time due to the lack of clear guidance on proper sequence, especially when no complete match is found in existing data.
Innovation Solution
A software introduction support system that computes an installation success ratio by analyzing correlations between new software groups and assessed patterns, considering partial matches and contradictions in installation sequences to predict installation success and optimize the installation order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional software installation methods are used without sequence optimization, then installation can proceed without complex analysis, but installation time and labor increase significantly due to trial and error
Solution Approach 1:
The system performs preliminary analysis of software dependencies and installation sequences before actual installation begins. By pre-computing the installation sequence based on dependency relationships and historical success data, the system avoids trial-and-error during the actual installation process, significantly reducing installation time while maintaining high productivity
Solution Approach 2:
The system incorporates feedback mechanisms by referencing historical installation actual-result information and success patterns. The estimation of installation success is continuously refined by comparing current installation scenarios with previously assessed patterns, allowing the system to learn from past outcomes and improve sequence prediction accuracy over time
2Measurement precision
If complete match of installation sequences is required from existing data, then accuracy of prediction is high, but adaptability to new software combinations decreases
Solution Approach 1:
The system accepts partial matches of installation sequences from historical data rather than requiring complete matches. By estimating installation success based on partially matched patterns and combining multiple partial matches, the system can adapt to new software combinations while maintaining reasonable prediction accuracy, bridging the gap between exact matching and complete adaptability
Solution Approach 2:
The system changes the parameter of pattern matching from exact matching to similarity-based matching. By computing estimation results based on the degree of match between current software groups and historical patterns, the system can handle novel software combinations by finding similar historical cases and adapting their installation sequences, thus improving versatility while preserving accuracy through weighted estimation
3Device complexity
If manual trial and error method is used to find valid installation sequence, then no complex analysis system is needed, but labor and time consumption increase
Solution Approach 1:
The system enables self-service installation by automatically determining optimal installation sequences without requiring manual intervention or trial-and-error efforts from users. The software introduction support device autonomously analyzes dependencies, references historical data, computes success estimations, and provides recommended sequences, making the installation process easy to operate while managing the complexity internally through automated analysis
4Ease of manufacture
If installation sequence is not optimized, then no analysis of dependencies is required, but installation success rate decreases due to incorrect ordering
Solution Approach 1:
The system performs preliminary analysis of software dependency relationships and determines optimal installation sequences before actual installation begins. By pre-assessing the correct order based on dependencies and historical success patterns, the system ensures high installation success rates while maintaining simplicity in the execution phase, as the complex analysis work is completed in advance
Data Source
AI summary
A device includes a processor that performs a procedure. The procedure includes: referencing a storage section storing one or more item of introduction actual-result information associating introduction sequence information regarding respective software items with success or failure of introduction, and determining any correlations with introduction sequence information regarding respective software items included in a new software group; and, reflecting in an estimation result any determination that the introduction sequence of the respective software included in the new introduction software group partially matches the introduction sequence information, as a positive element for the success or failure associated with the partially matched introduction sequence information, and reflecting in the estimation result any determination that a differently sequenced combination of the introduction software group matches the introduction sequence information, as a negative element for the success or failure associated with the introduction sequence information that matched the differently sequenced combination.