The Quantitative System-Level of Support (QSLS) methodology enables a holistic approach to system development by providing projected metrics and facilitating collaboration across the architecture, design, and implementation phases. QSLS is built around the understanding that there is a significant and historic engineering language that describes concepts at the Architecture, design and pre-implementation levels for systems. QSLS computes detailed measurements of “Projected” Level of Support before the system is built or modified, based on AI analysis of the deep relationships between engineering elements. The “Projected” Level of Risk is then calculated as (1 - Level of Support), highlighting areas that may require additional attention. Stakeholders working with management and architects can decide on acceptable levels of risk and potential solutions to allow the system to move forward.
While current tools such as OWL, UAF, SysML, UML are used to develop Qualitative understanding (relationships) internal to a system prior to implementation, QSLS provides a Quantitative measure based on the same language and AI’s deep analysis understanding. QSLS uses AI’s ability to take the engineering language and apply linguistic tools such as linguistic correlations (based on it’s over 50 years of development) and its deep analysis of the engineering terms to compute the complex non-linear measured relationships. QSLS takes the AI measured relationships and using vector/sparse matrix math, computes the "Projected" Levels of Support for Characteristics, Characteristic Types, Quality Attributes Sub-Attributes, Quality Attributes and Business Drivers. The output of the QSLS analysis is based on the same engineering language used by OWL, UAF, SysML and UML.
When QSLS analyzes a Quality Attribute to get a value, it is the same Quality Attribute that is being considered which OWL or UAF, SysML, UML are attempting to address although they can only build a relationship and not generate a measurement for the relationship.
QSLS performs computations at the architecture, design, and pre-implementation levels. QSLS, computations look at Minimum, Maximum, Average and Median of each element computed to understand the potential variance and distribution. This provides un-biased valuable projections for architects, stakeholders and managers, allowing them to understand the system's potential strengths and weaknesses in the development process based on historical understandings of the engineering elements. These computations allows for comparisons of different approaches and possible effect of adjustments to the system. Additionally, it feeds starting estimates of Design Mechanisms and Implementation Mechanisms to designers and implementers, guiding and bounding their detailed efforts.
Architecture-identified mechanisms serve as the foundation for the development process. These mechanisms act as starting points and boundary controls for the detailed design and implementation work. By anchoring the development efforts to the architectural vision, QSLS ensures a cohesive and aligned system as the development moves forward.
Throughout the development lifecycle, architects, managers, and stakeholders can monitor progress by re-computing QSLS metrics. This continuous monitoring enables the team to make necessary adjustments as the system takes form, ensuring that it remains on track to meet its quality attribute and business driver targets.
QSLS provides an unbiased approach to system development by relying only on the architect's selection and weighting of mechanisms. AI provides the deep relationship analysis between these engineering elements, providing quantitative projections that are grounded in historical data and patterns.
QSLS does not replace the current approach of using MBSE tools (SysML, UML, UAF), but works hand in hand to provide a projected analysis of what is being defined by the MBSE tool usage. As QSLS tools grow, the connection between MBSE and QSLS computations will evolve to create simpler and more complete, non-biased understanding to provide a growing information set of deeper analysis as systems are developed.
The QSLS methodology fosters a holistic and collaborative approach to system development. By providing quantitative projections, facilitating communication across phases, and enabling continuous monitoring and adjustment, QSLS empowers teams to deliver systems that align with business objectives while effectively managing risk. This methodology brings together architects, designers, implementers, managers, and stakeholders, creating a unified team that can navigate the complexities of system development with confidence and clarity.
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