Category: Spec-Driven AI Development

Comprehensive overview of spec-driven AI development in enterprise

The ISO/IEC 24027 Bias in AI Explanations: Specification Implications

Posted on by
Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20095806  49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,774✗Minimum 2,000 words for a full research article. Current: 1,774
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20095806
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Explainability frameworks increasingly intertwine technical desiderata with normative commitments, yet the standards community struggles to reconcile algorithmic transparency with equitable outcomes. ISO/IEC 24027—Artificial intelligence—Explainability requirements for AI systems—offers the first international attempt to codify explanatory integrity, but its pragmatic implementation e[REDACTED]...

Show moreHide
Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20095806 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,774✗Minimum 2,000 words for a full research article. Current: 1,774
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20095806
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Spec-Driven AI Dev…Read More
Read more

Adversarial Robustness in XAI Specifications: Why Explainability Must Be Secure

Posted on by
Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20059169  66stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,380✓Minimum 2,000 words for a full research article. Current: 2,380
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20059169
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]69%✓≥60% of references from 2025–2026. Current: 69%
[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 (65 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Explainability (XAI) systems are increasingly deployed in safety-critical domains, yet their vulnerability to adversarial manipulation threatens trust and decision integrity. This article investigates the adversarial robustness of specification-based XAI mechanisms, focusing on how malicious inputs can subvert explanatory outputs without altering the underlying model behavior. We pose three cor...

Show moreHide
Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20059169 66stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,380✓Minimum 2,000 words for a full research article. Current: 2,380
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20059169
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]69%✓≥60% of references from 2025–2026. Current: 69%
[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 (65 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
Spec-Driven AI Dev…Read More
Read more

XAI Interoperability Standards: How Explanation Formats Should Be Specified

Posted on by
Academic Research
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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more

Testing Explainability Compliance: Specification-Based Testing for AI Transparency

Posted on by
Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20024998  63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]950✗Minimum 2,000 words for a full research article. Current: 950
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20024998
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[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 (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainability compliance in artificial intelligence systems demands rigorous evaluation methodologies that can verify whether AI models adhere to predefined specification criteria. This article introduces specification‑based testing (SBT) as a systematic approach to assess AI transparency, focusing on how well model outputs conform to declared functional and ethical constraints. We outline a r...

Show moreHide
Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20024998 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]950✗Minimum 2,000 words for a full research article. Current: 950
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20024998
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[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 (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Spec-Driven AI Dev…Read More
Read more

Formal Methods for XAI Verification: Proving That Explanations Are Correct

Posted on by
Academic Research
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%)

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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more

Domain-Specific XAI Standards: Healthcare, Finance, Legal, and Defense Specifications

Posted on by
Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20017366  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[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
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20017366
[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%)

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...

Show moreHide
Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20017366 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[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
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20017366
[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%)
Spec-Driven AI Dev…Read More
Read more

XAI Specification Frameworks: From Natural Language to Formal Explainability Requirements

Posted on by
Academic Research
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%)

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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more

The Spec-Driven AI Toolchain: From Specification to Deployment

Posted on by
Academic Research
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%)

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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more

Formal Specification Economics: Measuring ROI of Spec Investment

Posted on by
Academic Research
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%)

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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more

Architecting Spec-Compliant AI Systems: Patterns and Anti-Patterns

Posted on by
Academic Research
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%)

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...

Show moreHide
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%)
Spec-Driven AI Dev…Read More
Read more