Category: Spec-Driven AI Development

Comprehensive overview of spec-driven AI development in enterprise

Spec-Driven Development with AI Agents: A Brownfield Walkthrough

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Academic Research by Oleh Ivchenko  21stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

A full field guide from a live CoffeeJUG session: spec-driven development with OpenSpec on a brownfield codebase — history, method, tool internals, a full annotated walkthrough, and the practices that only show up after a few real changes.

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Academic Research by Oleh Ivchenko 21stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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Model Cards as Executable Specifications: From Documentation to Automated Compliance Testing

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21976820  60stabilfr·wdophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Model cards have emerged as a standard mechanism for documenting the capabilities, limitations, and ethical considerations of machine l[REDACTED]g systems. Despite their proliferations, model cards remain passive textual artifacts that are rarely operationalized during deployment. This article introduces a systematic methodology for converting model card specifications into executable test suit...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21976820 60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]18%✗≥60% of references from 2025–2026. Current: 18%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI System Invariants: Identifying and Encoding the Properties That Must Never Change

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21899296  62stabilfr·wdophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
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[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]46%✗≥60% of references from 2025–2026. Current: 46%
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[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence systems are increasingly deployed in safety-critical, high-stakes domains where failure can have severe societal consequences. Despite growing attention to AI safety, there remains no systematic methodology for identifying and enforcing invariant properties — characteristics that must remain unchanged across model versions, configuration updates, and deployment contexts....

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21899296 62stabilfr·wdophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,069✓Minimum 2,000 words for a full research article. Current: 2,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21899296
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]46%✗≥60% of references from 2025–2026. Current: 46%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Behavior-Driven Development for AI: Cucumber and Gherkin Patterns for LLM Systems

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Academic Research by Oleh Ivchenko  24stabilfr·wdophcgmx
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[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (35 × 60%) + Required (0/5 × 30%) + Optional (1/4 × 10%)

Behavior-Driven Development (BDD) offers a promising paradigm for structuring specifications of LLM system behavior, yet its adoption faces critical stability challenges when underlying models evolve. This article introduces a robust framework for adapting BDD to specify and test LLM system behavior, focusing on creating human-readable behavioral specifications that maintain integrity across mo...

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Academic Research by Oleh Ivchenko 24stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic60%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,919✗Minimum 2,000 words for a full research article. Current: 1,919
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (35 × 60%) + Required (0/5 × 30%) + Optional (1/4 × 10%)
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Specification Coverage Metrics for AI Systems: Adapting MC/DC and Branch Coverage

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21385452  61stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid integration of artificial intelligence (AI) into Safety‑Critical and High‑Performance Computing (HPC) domains demands formally verifiable assurance techniques that can certify model behavior against formally expressed specifications. Traditional software engineering employs code‑coverage criteria such as Modified Condition/Decision Coverage (MC/DC) and branch coverage to demonstrate t...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21385452 61stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
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[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]1,967✗Minimum 2,000 words for a full research article. Current: 1,967
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21385452
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Real-Time XAI Specifications: Performance Requirements for Production Explanations

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Academic Research by Oleh Ivchenko  76stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References65 refs✓Minimum 10 references required
[w]Words [REQ]2,290✓Minimum 2,000 words for a full research article. Current: 2,290
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid deployment of AI-driven decision systems in production environments has intensified the demand for explanation generation that is not only semantically meaningful but also temporally bounded and resource-constrained. This article establishes a formal specification framework for real-time explainability, defining precise performance requirements for latency, fidelity, and computational...

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Academic Research by Oleh Ivchenko 76stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed95%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References65 refs✓Minimum 10 references required
[w]Words [REQ]2,290✓Minimum 2,000 words for a full research article. Current: 2,290
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cross-Border AI Explanation Requirements: Specifying XAI for Multi-Jurisdictional Compliance

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Academic Research by Oleh Ivchenko  51stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI69%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic77%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,947✗Minimum 2,000 words for a full research article. Current: 1,947
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence systems are increasingly deployed across jurisdictions that impose distinct obligations on the transparency and interpretability of model decisions. While the European Union’s AI Act establishes a comprehensive framework for high‑risk AI, the United States relies on sector‑specific Executive Orders and guidance from the National Institute of Standards and Technology (NIS...

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Academic Research by Oleh Ivchenko 51stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI69%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic77%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,947✗Minimum 2,000 words for a full research article. Current: 1,947
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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XAI Interoperability Standards: How Explanation Formats Should Be Specified

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20026553  37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,366✓Minimum 2,000 words for a full research article. Current: 2,366
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20026553
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)

Explainable AI (XAI) systems generate explanations to justify model decisions, yet current standardization efforts lack coherent specifications for explanation formats. This article establishes a rigorous framework for XAI interoperability, defining mandatory components for explanation formats that ensure technical compatibility and functional validity across diverse deployment contexts. We ana...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20026553 37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,366✓Minimum 2,000 words for a full research article. Current: 2,366
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20026553
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)
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Formal Methods for XAI Verification: Proving That Explanations Are Correct

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23226369  80stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources55%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]5,120✓Minimum 2,000 words for a full research article. Current: 5,120
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23226369
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[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (89 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The deployment of machine learning models in high-stakes domains — healthcare, finance, autonomous systems — demands not only accurate predictions but also verifiable explanations. Explainable AI (XAI) methods such as SHAP, LIME, and counterfactual generators have become standard tooling, yet their outputs remain largely unvalidated: an explanation may appear plausible while misrepresenting the...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.23226369 80stabilfr·wdophcgmx
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[s]Reviewed Sources55%○≥80% from editorially reviewed sources
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[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]5,120✓Minimum 2,000 words for a full research article. Current: 5,120
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23226369
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (89 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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The Spec-Driven AI Toolchain: From Specification to Deployment

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18820121  33stabilfr·wdophcgmx
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[s]Reviewed Sources2%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef2%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic18%○≥80% from journals/conferences/preprints
[f]Free Access23%○≥80% are freely accessible
[r]References44 refs✓Minimum 10 references required
[w]Words [REQ]2,817✓Minimum 2,000 words for a full research article. Current: 2,817
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18820121
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[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
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[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
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The transition from specification-centric development to deployed AI systems requires a comprehensive toolchain that bridges the gap between formal requirements and operational machine l[REDACTED]g models. This article examines the current landscape of tools supporting spec-driven AI development, from specification authoring platforms through automated test generation to continuous validation p...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18820121 33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources2%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef2%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic18%○≥80% from journals/conferences/preprints
[f]Free Access23%○≥80% are freely accessible
[r]References44 refs✓Minimum 10 references required
[w]Words [REQ]2,817✓Minimum 2,000 words for a full research article. Current: 2,817
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18820121
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (20 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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