Software Practice Expert System Using Dynamic Models
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Solution Overview
Problem
The software development industry faces challenges in selecting and combining software development practices effectively, as existing methods lack a credible basis for assessing, learning, and improving practice choices, leading to inefficiencies and risks in project outcomes.
Innovation Solution
A data processing system integrating Dynamic Systems models and Control Systems Engineering designs to provide advice on software development practices, using contextual factors to determine appropriate practice choices and compute expected outcomes, thereby facilitating simulation and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software development practices are selected and combined based on existing methods, then project implementation can proceed, but the credibility and reliability of practice selection is insufficient leading to poor project outcomes
Solution Approach 1:
The patent segments the software development practice selection into discrete, structured components: contextual factors (9 factors categorizing project characteristics), practices (145 distinct practices organized in a taxonomy), and outcomes (27 measurable project outcomes). This segmentation transforms the complex selection process into manageable, analyzable units that can be systematically evaluated and combined.
Solution Approach 2:
The patent implements feedback mechanisms through simulated project executions that generate outcome data, which is then used to refine and improve the practice selection model. The system learns from simulated results to enhance the credibility and accuracy of practice recommendations, creating a continuous improvement cycle that increases reliability over time.
2Reliability
If comprehensive analysis and simulation of software development practices is performed, then project outcomes can be improved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining and organizing 145 software development practices into a structured taxonomy with established relationships and characteristics. Contextual factors and outcome metrics are pre-configured, allowing the system to rapidly evaluate practice selections without requiring extensive analysis during actual project planning, thus reducing time loss.
Solution Approach 2:
The patent uses simulated project executions that replicate real-world software development scenarios. These simulations copy the essential dynamics of actual projects in a controlled environment, allowing comprehensive analysis of practice outcomes without the full time and resource investment required for actual project implementation. The simulated results provide credible guidance for real project decisions.
3Productivity
If software development knowledge is made widely available through expert systems, then industry-wide improvement can be achieved, but the complexity of implementing and maintaining such systems increases
Solution Approach 1:
The patent creates a universal practice selection system that can be applied across diverse software development contexts by incorporating 9 comprehensive contextual factors that capture various project dimensions. The system handles multiple project types, sizes, and domains through a single unified framework, enabling wide industry applicability without requiring separate specialized systems for each context.
Solution Approach 2:
The system incorporates automated simulation and evaluation capabilities that allow it to self-assess and refine its own performance. The simulated project executions automatically generate outcome data that feeds back into improving the practice selection model, reducing the need for external manual maintenance and updates while enhancing industry-wide productivity.
Data Source
AI summary
A software development practices expert system and method is described. The expert system utilizes control systems engineering designs, as well as dynamic systems models to inform and guide the selection, assembly, composition, publishing and presentation, enactment, assessment, learning and analysis, refactoring, improvement and simulation of software development practices into approaches or methods to software development. The expert system collects software endeavor result data to correlate efficacy of software development practice usage, and to recalibrate dynamic systems models and control systems engineering design parameters. Such designs and models configure and improve an inference-based rule engine to provide advice to users contained within a rule repository and knowledge base.