Category: Spec-Driven AI Development

Comprehensive overview of spec-driven AI development in enterprise

Formal Methods for XAI Verification: Proving That Explanations Are Correct

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20012331  67stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (77 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainable artificial intelligence (XAI) seeks to make model decisions transparent, yet existing approaches often produce explanations that are themselves opaque or unverified. Formal verification offers a rigorous mathematical framework to certify that an explanation accurately reflects the underlying model computation. This article investigates how formal methods can be applied to XAI to gen...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20012331 67stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources57%○≥80% from editorially reviewed sources
[t]Trusted79%○≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,014✗Minimum 2,000 words for a full research article. Current: 1,014
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20012331
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]77%✓≥60% of references from 2025–2026. Current: 77%
[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 (77 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Domain-Specific XAI Standards: Healthcare, Finance, Legal, and Defense Specifications

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20017366  58stabilfr·wdophcgmx
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[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[i]Indexed7%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
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[g]Code✓✓Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

Abstract: XAI (Explainable Artificial Intelligence) has matured into a cross-disciplinary field where domain-specific standards are essential for regulatory compliance, stakeholder trust, and operational safety. While generic XAI techniques provide post-hoc explanations, industry sectors have distinct governance requirements, data sensitivity constraints, and risk tolerance levels that demand t...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20017366 58stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[i]Indexed7%○≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]1,517✗Minimum 2,000 words for a full research article. Current: 1,517
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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]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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XAI Specification Frameworks: From Natural Language to Formal Explainability Requirements

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19986383  58stabilfr·wdophcgmx
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[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
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[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,694✗Minimum 2,000 words for a full research article. Current: 1,694
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19986383
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (68 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)

Explainable Artificial Intelligence (XAI) has emerged as a critical requirement for trustworthy AI systems, yet current approaches often treat explanations as afterthoughts rather than first-class outputs of the development process. This article proposes a specification framework for XAI that treats explainability requirements as formal specifications alongside functional requirements. We addre...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.19986383 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,694✗Minimum 2,000 words for a full research article. Current: 1,694
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19986383
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (68 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 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
[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%)

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
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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
[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%
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[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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Formal Specification Economics: Measuring ROI of Spec Investment

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18816640  35stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted28%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]2,427✓Minimum 2,000 words for a full research article. Current: 2,427
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18816640
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
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[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Formal Specification Economics: Measuring ROI of Spec Investment. Spec-Driven AI Development Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18818355 Abstract Formal specification practices in AI system development represent a significant upfront investment that enterprises must justify economically. This article presents a rigorous fra...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18816640 35stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted28%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]2,427✓Minimum 2,000 words for a full research article. Current: 2,427
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18816640
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
[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 (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Architecting Spec-Compliant AI Systems: Patterns and Anti-Patterns

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18745394  49stabilfr·wdophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,022✓Minimum 2,000 words for a full research article. Current: 3,022
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into enterprise systems demands rigorous architectural approaches that ensure reliability, maintainability, and compliance with specifications. This article explores architectural patterns that support spec-driven development of AI systems, contrasting proven design patterns with common anti-patterns that lead to technical debt. We examine contract-bas...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18745394 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,022✓Minimum 2,000 words for a full research article. Current: 3,022
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18735965  51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,846✓Minimum 2,000 words for a full research article. Current: 3,846
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[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 (51 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Armed conflict prediction represents one of the most critical challenges in computational social science and international relations. This paper presents a multi-factor machine l[REDACTED]g approach to predicting armed conflict probability at the country level, combining ensemble l[REDACTED]g methods with diverse data sources including ACLED, UCDP, World Bank economic indicators, SIPRI military...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18735965 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,846✓Minimum 2,000 words for a full research article. Current: 3,846
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[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 (51 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Development Paradigms Compared: Spec-Driven, Experiment-Driven, and Hybrid Approaches

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741619  46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]3,797✓Minimum 2,000 words for a full research article. Current: 3,797
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The development of AI systems presents unique challenges that traditional software engineering paradigms struggle to address. This article provides a comprehensive comparative analysis of four major development approaches: spec-driven development, experiment-driven development, data-centric AI, and model-centric AI. We examine each paradigm's theoretical foundations, practical workflows, and su...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741619 46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]3,797✓Minimum 2,000 words for a full research article. Current: 3,797
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Capturing AI Requirements: Beyond Functional Specifications

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730498  64stabilfr·wdophcgmx
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[s]Reviewed Sources54%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References50 refs✓Minimum 10 references required
[w]Words [REQ]2,868✓Minimum 2,000 words for a full research article. Current: 2,868
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730498
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Traditional requirements engineering approaches, developed for deterministic software systems, prove inadequate when applied to AI systems characterized by l[REDACTED]g, uncertainty, and emergent behavior. This article examines the unique challenges of capturing requirements for AI systems and proposes a structured framework that extends beyond conventional functional specifications. We explore...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730498 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources54%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References50 refs✓Minimum 10 references required
[w]Words [REQ]2,868✓Minimum 2,000 words for a full research article. Current: 2,868
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730498
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]2%✗≥60% of references from 2025–2026. Current: 2%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Specification Languages for AI: From Natural Language to Formal Methods

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18684610  63stabilfr·wdophcgmx
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[s]Reviewed Sources54%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI69%○≥80% have a Digital Object Identifier
[b]CrossRef49%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic80%✓≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References35 refs✓Minimum 10 references required
[w]Words [REQ]5,512✓Minimum 2,000 words for a full research article. Current: 5,512
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18684610
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence systems present a fundamental specification challenge: how do we precisely describe what a l[REDACTED]g system should do when its behaviour emerges from data rather than explicit programming? This article surveys the landscape of specification languages and approaches available to AI practitioners — from accessible natural language techniques like Gherkin-based behaviour...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18684610 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources54%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI69%○≥80% have a Digital Object Identifier
[b]CrossRef49%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic80%✓≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References35 refs✓Minimum 10 references required
[w]Words [REQ]5,512✓Minimum 2,000 words for a full research article. Current: 5,512
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18684610
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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