Category: Spec-Driven AI Development

Comprehensive overview of spec-driven AI development in enterprise

Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach

Posted on by
Academic Research
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...

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

Development Paradigms Compared: Spec-Driven, Experiment-Driven, and Hybrid Approaches

Posted on by
Academic Research
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...

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

Capturing AI Requirements: Beyond Functional Specifications

Posted on by
Academic Research
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%)

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...

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

Specification Languages for AI: From Natural Language to Formal Methods

Posted on by
Academic Research
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%)

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...

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

The Spec-First Revolution: Why Enterprise AI Needs Formal Specifications

Posted on by
Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666032  67stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources76%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References87 refs✓Minimum 10 references required
[w]Words [REQ]5,993✓Minimum 2,000 words for a full research article. Current: 5,993
[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 (78 × 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...

Show moreHide
Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18666032 67stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources76%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References87 refs✓Minimum 10 references required
[w]Words [REQ]5,993✓Minimum 2,000 words for a full research article. Current: 5,993
[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 (78 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Spec-Driven AI Dev…Read More
Read more