Category: Spec-Driven AI Development

Comprehensive overview of spec-driven AI development in enterprise

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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The transition from specification-centric development to deployed AI systems requires a comprehensive toolchain that bridges the gap between formal requirements and operational machine learning 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 pipel...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18820121 33stabilfr·wdophcgmx
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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  36stabilfr·wdophcgmx
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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 36stabilfr·wdophcgmx
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[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted30%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
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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  51stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (51 × 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 51stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef28%○≥80% indexed in CrossRef
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[l]Academic60%○≥80% from journals/conferences/preprints
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[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,006✓Minimum 2,000 words for a full research article. Current: 3,006
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[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 (51 × 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  57stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% of references from 2025–2026. Current: 10%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 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 learning approach to predicting armed conflict probability at the country level, combining ensemble learning methods with diverse data sources including ACLED, UCDP, World Bank economic indicators, SIPRI military expendi...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18735965 57stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
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[x]Cited by0○Referenced by 0 other hub article(s)
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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  49stabilfr·wdophcgmx
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Score = Ref Trust (47 × 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 49stabilfr·wdophcgmx
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[t]Trusted56%○≥80% from verified, high-quality sources
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
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[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
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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 (47 × 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  66stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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Score = Ref Trust (76 × 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 learning, 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 beh...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730498 66stabilfr·wdophcgmx
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[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted98%✓≥80% from verified, high-quality sources
[a]DOI54%○≥80% have a Digital Object Identifier
[b]CrossRef50%○≥80% indexed in CrossRef
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[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References48 refs✓Minimum 10 references required
[w]Words [REQ]2,846✓Minimum 2,000 words for a full research article. Current: 2,846
[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 (76 × 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 by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18684610  65stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
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[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
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[w]Words [REQ]5,480✓Minimum 2,000 words for a full research article. Current: 5,480
[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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence systems present a fundamental specification challenge: how do we precisely describe what a learning 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-dri...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18684610 65stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
[b]CrossRef52%○≥80% indexed in CrossRef
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[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]5,480✓Minimum 2,000 words for a full research article. Current: 5,480
[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 (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Spec-First Revolution: Why Enterprise AI Needs Formal Specifications

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666032  69stabilfr·wdophcgmx
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[s]Reviewed Sources79%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef72%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access16%○≥80% are freely accessible
[r]References86 refs✓Minimum 10 references required
[w]Words [REQ]5,903✓Minimum 2,000 words for a full research article. Current: 5,903
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18666032
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]1%✗≥60% of references from 2025–2026. Current: 1%
[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 (80 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

timeline title Evolution of Software Specification Practices 1950s-1960s : Ad-hoc specifications : Natural language : Manual testing 1970s-1980s : Formal methods : Hoare logic, VDM, Z notation : Mathematical proofs 1990s : Design-by-contract : Preconditions, postconditions : Eiffel, JML 2000s...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18666032 69stabilfr·wdophcgmx
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[s]Reviewed Sources79%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef72%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access16%○≥80% are freely accessible
[r]References86 refs✓Minimum 10 references required
[w]Words [REQ]5,903✓Minimum 2,000 words for a full research article. Current: 5,903
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18666032
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]1%✗≥60% of references from 2025–2026. Current: 1%
[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 (80 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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