Technology Selection Frameworks

• Technology Selection Frameworks are structured methodologies that use multi-criteria analysis to compare and evaluate technology options based on performance, cost, and compatibility.
• They integrate quantitative metrics and qualitative assessments through techniques like MCDA, ANP, and optimization to support objective decision-making.
• These frameworks are applied across diverse domains—including enterprise systems, cloud architectures, and AI/ML tooling—to guide informed technology choices and mitigate risks.

A technology selection framework is a structured, multi-criteria methodology or algorithmic process that supports stakeholders in identifying, evaluating, comparing, and justifying technological alternatives—spanning software, hardware, architectures, or platforms—relative to explicit organizational context, project requirements, and strategic objectives. These frameworks integrate quantifiable metrics, qualitative assessments, decision models, and optimization techniques to address challenges inherent in complex, multi-dimensional decision-making environments across domains such as enterprise architecture, cloud infrastructure, modular system design, wireless communications, AI/ML tooling, and beyond.

1. Foundational Principles and Key Dimensions

Modern technology selection frameworks embody several central principles:

• Multi-Criteria Analysis: Rather than evaluating alternatives using a single criterion (e.g., cost), these frameworks adopt an explicitly multi-dimensional approach. Metrics commonly include performance, non-functional requirements (e.g., scalability, maintainability, security), total cost of ownership, extensibility, compliance, and in some contexts, domain-specialized metrics such as AI coding proficiency (Zhang et al., 14 Sep 2025), PPA (performance/power/area) (Roman-Vicharra et al., 15 Feb 2025), or architectural compatibility (Copei et al., 22 Aug 2025).
• Integrated Quantitative and Qualitative Judgments: Frameworks combine quantitative measures (e.g., throughput, latency, cost) with structured representations of qualitative assessments (e.g., “friendliness” of support, maturity of ecosystem, expert judgment).
• Explicit Hierarchies and Rule-Based Structures: Many frameworks introduce hierarchical models—either for system decomposition (as in modular product design (Levin, 2012, Levin, 2014)) or the decision process itself (hierarchical classifiers for problem description and method profile in MCDA frameworks (Wątróbski et al., 2018)).
• Ordinal or Ratio Scaled Scoring and Weighting: Evaluation criteria are often scored on discrete scales (e.g., 0–5, ordinal; or 0–1, normalized), with explicit weights assigned to reflect stakeholder priorities (Dube et al., 2011, Badidi, 2013). Aggregation uses linear (weighted sums) or more sophisticated multi-criteria decision-making methods (e.g., ANP in (Menzel et al., 2011)).
• Cause–Effect Linking (Inputs–Outcomes Pairs): Some frameworks couple initial business or technical inputs explicitly with outcome metrics, ensuring the traceability of how technology choices address underlying requirements and operationalize value (Dube et al., 2011).

2. Representative Methodologies and Mathematical Formalisms

Technology selection frameworks employ a range of decision models and optimization/selection algorithms, each tailored to the complexity of the selection space and the required decision process. Common methodologies include:

Methodology	Key Technical Feature	Application Example
Multi-Criteria Decision Analysis (MCDA)	Rule-based selection; hierarchical classifiers	(Wątróbski et al., 2018, Menzel et al., 2011)
Weighted Utility Aggregation	Linear/quasi-linear formulae (e.g., U = Σwᵢ Uᵢ)	(Badidi, 2013, Dube et al., 2011)
Analytic Network/Hierarchy Process (ANP/AHP)	Pairwise comparisons, ratio scale outputs	(Menzel et al., 2011, Wątróbski et al., 2018)
Morphological Synthesis	Combinatorial, compatibility-based synthesis	(Levin, 2012, Levin, 2014)
Optimization (SA, RL/PPO)	Objective-driven search in high-dimensional space	(Roman-Vicharra et al., 15 Feb 2025)
Threshold-Driven MDP/CMDP	Policy derived from threshold analysis	(Roy et al., 2017, Gao et al., 14 Oct 2024)

Mathematically, most frameworks reduce the final comparison to a (possibly weighted) score over all considered criteria: $S = \sum_{i=1}^n w_i s_i$ where $s_i$ is the (possibly normalized or ordinal) score for criterion $i$ and $w_i$ is its weight (Dube et al., 2011, Badidi, 2013).

Concepts such as AI coding proficiency introduce new formal metrics for the LLM era: $$P_l = \frac{1}{|P_{l,s}|} \sum_{p \in P_{l,s}} S_p^m$$ where $P_l$ is the proficiency for library $l$, $S_p^m$ is the multi-dimensional quality score for prompt $p$ on model $m$ (Zhang et al., 14 Sep 2025).

3. Framework Architectures and Domain Adaptations

Enterprise and Cloud Architectures

The comprehensive measurement framework for enterprise architectures (Dube et al., 2011) evaluates candidates along three integrated axes—higher order goals, non-functional requirement support, and inputs–outcomes pairs—transforming subjective and objective scores into weighted overall evaluations. The (MC2)2 framework (Menzel et al., 2011) applies a generic multi-criteria process for IT infrastructure, introducing structured scenario definition, attribute-based alternative description, requirement filtering, and ANP-based final selection.

Modular and Composite Systems

Morphological and combinatorial synthesis frameworks (Levin, 2012, Levin, 2014) address the hierarchical decomposition of composite systems, modeling technology selection as an integrated decision over tree-structured design alternatives (DAs), their ordinal priorities, and inter-component compatibilities. Aggregation and multi-stage methods support consensus-building and system evolution.

Wireless and Networked Environments

Radio Access Technology (RAT) selection in heterogeneous networks leverages MDPs, CMDPs, and threshold-based policies (Roy et al., 2017, Gao et al., 14 Oct 2024). Criteria include system throughput, blocking probability, channel state, and activity-aware rate constraints. Optimization formulations are embedded within dynamic, state-driven algorithms that trade system-level objectives against user experience constraints.

Decision Support for Method Selection

Generalized frameworks for MCDA (Wątróbski et al., 2018) treat the method selection problem itself as a hierarchical, rule-based multi-criteria process. By encoding problem and method descriptors in classifiers and handling uncertainty explicitly, these frameworks ensure that the recommendation space reflects both the completeness and the granularity of decision context.

Platform and Stack Selection in the LLM Era

The rise of LLM-assisted development introduces AI coding proficiency as an explicit, measured criterion for technology selection frameworks. This dimension, empirically benchmarked across libraries and models (Zhang et al., 14 Sep 2025), is now recognized as a key determinant of engineering productivity and maintainability in AI-supported workflows.

4. Comparative Features and Application Domains

The specific instantiations of technology selection frameworks vary by context, but key comparative dimensions observed across research include:

• Breadth of Criteria: From focused infrastructure evaluation (cost, security, performance) (Menzel et al., 2011), to modular system-level synthesis (compatibility, configuration, trajectory) (Levin, 2012, Levin, 2014), to end-to-end architecture pattern integration (Copei et al., 22 Aug 2025).
• Role of Optimization: Some frameworks use combinatorial or continuous optimization (e.g., SA, PPO in floorplanning (Roman-Vicharra et al., 15 Feb 2025)), others rely on explicit rule bases or multi-level scoring/aggregation.
• Support for Uncertainty: Advanced frameworks integrate explicit modeling of missing or imprecise data (e.g., “unknown” classifier values in MCDA (Wątróbski et al., 2018)), enabling robust recommendations even under incomplete information.
• Tooling and Automation: Practical deployment is often supported by online expert systems, decision-support GUIs, or web-based evaluation tools (e.g., MCDA expert system (Wątróbski et al., 2018), DLT design tool (Momčilović et al., 2023)).
• Adaptability: Many frameworks are built for reuse and extensibility, allowing domain-specific or context-sensitive weights, criteria, and integration with organizational processes (Menzel et al., 2011, Levin, 2014, Ullah et al., 2020).

5. Limitations, Risks, and Mitigation Strategies

Several limitations and emergent risks have been empirically established:

• Subjectivity and Data Collection: The effectiveness of frameworks reliant on qualitative input may be undermined by bias or incomplete knowledge (Menzel et al., 2011, Wątróbski et al., 2018). Group evaluation and expert validation are recommended to counteract this.
• Complexity and Configuration Overhead: Multi-stage or highly parametrized frameworks (e.g., those using ANP or optimization layers) may require advanced expertise to configure and interpret (Menzel et al., 2011, Roman-Vicharra et al., 15 Feb 2025).
• Tool/Pattern Overload: Abstracting selection to the architectural pattern (as in CAPI (Copei et al., 22 Aug 2025)) can reduce complexity; overlaying decision trees with contraindication checks further curtails unnecessary recommendations.
• Risk of Technological Monoculture: Integrating LLM proficiency without mitigation may concentrate adoption on a handful of well-exposed libraries, reducing diversity and increasing systemic risk (Matthew effect (Zhang et al., 14 Sep 2025)). Mitigation strategies include explicit inclusion of proficiency scores in frameworks, prompt engineering, improved documentation, and library–model collaboration.

6. Future Development and Evolution

Several lines of advancement are identified as priorities:

• Integration of New Metrics: Incorporation of AI coding proficiency, agent/AI efficiency, or new performance/yield models (in semiconductors) reflects the ongoing evolution of what constitutes “fitness” for a given technology (Zhang et al., 14 Sep 2025, Roman-Vicharra et al., 15 Feb 2025).
• Greater Automation and Iterative Tool Support: Expansion of expert systems, traceable visualizations, and agile re-evaluation tools are highlighted as requirements for sustained alignment with practice and dynamic business environments (Momčilović et al., 2023, Copei et al., 22 Aug 2025, Jimenez et al., 2022).
• Enhanced Modeling of Uncertainty and Domain Constraints: Probabilistic and scenario-based extensions are proposed to confront real-world uncertainty and enhance decision robustness (Wątróbski et al., 2018).
• Broader Domain Adaptability: Technology selection frameworks are being re-adapted to multi-die hardware (Roman-Vicharra et al., 15 Feb 2025), IoT platform discrimination (Ullah et al., 2020), DLT design (Momčilović et al., 2023), and robotics HRI (Jimenez et al., 2022), gesturing toward universal applicability with suitable domain parametrizations.

7. Summary and Outlook

Contemporary technology selection frameworks operationalize a rigorous, multi-phase decision process grounded in hierarchical decomposition, composite scoring, and adaptive optimization. By integrating quantifiable and qualitative criteria—ranging from performance and cost to compatibility and AI coding proficiency—these frameworks enable structured, evidence-based technology decisions that accommodate both organizational objectives and rapidly evolving technical landscapes. Limitations relating to subjectivity, configuration complexity, and system-level risks are addressed through strategic mitigation strategies including group decision processes, tool-based support, and ongoing refinement of evaluation criteria. As the complexity and scope of technologies increase, and as AI becomes a primary participant in development workflows, technology selection frameworks are correspondingly evolving to remain a central pillar in robust engineering and architectural decision-making.