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  ·  DOI: 10.5281/zenodo.23327624  42stabilfr·wuedophcgmx
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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 DOI: 10.5281/zenodo.23327624 42stabilfr·wuedophcgmx
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Score = Ref Trust (17 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 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  72stabilfr·wuedophcgmx
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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 72stabilfr·wuedophcgmx
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Score = Ref Trust (65 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  74stabilfr·wuedophcgmx
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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 74stabilfr·wuedophcgmx
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[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI77%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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Score = Ref Trust (69 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  74stabilfr·wuedophcgmx
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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 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 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.23462264  88stabilfr·wuedophcgmx
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This article presents 150 key facts about Using formal verification techniques to validate that explanations accurately reflect model behavior.. These facts cover various aspects of the topic, providing a comprehensive overview for researchers and practitioners. The compilation draws upon existing scholarship, including foundational work [1].

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.23462264 88stabilfr·wuedophcgmx
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[a]DOI86%✓≥80% have a Digital Object Identifier
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Score = Ref Trust (90 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 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  45stabilfr·wuedophcgmx
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Score = Ref Trust (20 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 45stabilfr·wuedophcgmx
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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
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[f]Free Access23%○≥80% are freely accessible
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Score = Ref Trust (20 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  47stabilfr·wuedophcgmx
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[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 47stabilfr·wuedophcgmx
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]2,672✓Minimum 1,000 words of article body. Current: 2,672
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]22✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 22
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18816640
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness12%○≥60% of references from 2025–2026. Current: 12%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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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 (4/4 × 30%) + Optional (2/7 × 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  61stabilfr·wuedophcgmx
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]3,032✓Minimum 1,000 words of article body. Current: 3,032
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]24✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness4%○≥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 (4/4 × 30%) + Optional (2/7 × 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 61stabilfr·wuedophcgmx
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]3,032✓Minimum 1,000 words of article body. Current: 3,032
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]24✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18745394
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness4%○≥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 (4/4 × 30%) + Optional (2/7 × 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  63stabilfr·wuedophcgmx
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]3,868✓Minimum 1,000 words of article body. Current: 3,868
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥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 (4/4 × 30%) + Optional (2/7 × 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 63stabilfr·wuedophcgmx
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]3,868✓Minimum 1,000 words of article body. Current: 3,868
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness8%○≥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 (4/4 × 30%) + Optional (2/7 × 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  59stabilfr·wuedophcgmx
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]3,793✓Minimum 1,000 words of article body. Current: 3,793
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥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 (4/4 × 30%) + Optional (2/7 × 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 59stabilfr·wuedophcgmx
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]3,793✓Minimum 1,000 words of article body. Current: 3,793
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥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 (4/4 × 30%) + Optional (2/7 × 10%)
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