TALP Analytics for Software Time Complexity Prediction
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Solution Overview
Problem
Current methods for decomposing software source code into executable and analyzable components, such as time-affecting linear pathways (TALPs), face challenges in accurately predicting processing time and resource allocation due to temporal ambiguities and the complexity of loop structures, especially when dealing with varying input variables and multiple processing elements.
Innovation Solution
The extension of TALP analytics includes the use of advanced curve-fitting methods with T-polynomials, which incorporate first and second derivatives, automatic table expansion, and the generation of prediction polynomials to analyze execution pathways, memory allocation, and resource usage, allowing for sensitivity analysis and multi-variable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If TALP decomposition is applied to software source code, then executable and analyzable components are produced, but temporal ambiguities and loop structure complexity reduce prediction accuracy
Solution Approach 1:
The patent segments the software system into discrete TALP components, each representing a specific execution pathway. This segmentation allows complex software to be broken down into manageable units that can be analyzed independently, resolving the contradiction between ease of decomposition and prediction accuracy by ensuring each segment is simple enough to predict precisely while collectively forming the complete system behavior.
Solution Approach 2:
The patent introduces multiple parameters including input variable attributes, loop iteration counts, and processing element quantities to refine predictions. By changing from single-parameter to multi-parameter prediction models, the system achieves higher measurement precision for processing time while maintaining the benefits of TALP decomposition.
2Reliability
If loop structures are included in TALP pathways, then end-to-end processing time can be tracked, but the complexity of analyzing and predicting execution increases
Solution Approach 1:
The patent employs feedback mechanisms where predicted processing times from TALP components are aggregated and compared against actual execution times. This feedback loop allows the system to refine its predictions and account for loop structure complexities, improving reliability while managing analysis complexity through iterative improvement.
Solution Approach 2:
The TALP framework is designed to be universal, handling multiple types of loop structures (for loops, while loops, do-while loops) and nested loops through a single unified analysis approach. This multi-functionality reduces the complexity of analyzing different loop patterns while maintaining accurate end-to-end processing time tracking.
3Measurement precision
If multiple input variables and processing elements are considered, then prediction accuracy improves, but the computational resources and analysis time required increase
Solution Approach 1:
The patent performs preliminary analysis by pre-computing and storing characteristics of TALP pathways, including their processing time profiles and resource requirements. This preliminary action allows the system to make accurate multi-variable predictions without requiring extensive computational resources during actual prediction, as the heavy lifting is done in advance during the decomposition and characterization phases.
Data Source
AI summary
Concepts of time-affecting linear pathways (TALPs) decomposed from existing application source code, algorithms, processes, software modules, and functions, are extended. For instance, T-polynomials can be expanded to define when the interaction of high-order polynomials can be treated as if they were linear functions using a new type of T-polynomial. The number of inherent analytics that are extractable from TALPs of an algorithm or source code can be expanded to include the prediction polynomials of advanced time complexity, advanced space complexity, resource complexity, and output complexity along with their inverses. An overlay to the TALP execution pathway is defined, allowing for input variable sensitivity analysis. Further, automatic detection and quantification of context variables are provided for more accurate sensor analysis.