2026 5th International Conference on Artificial Intelligence and Software Engineering (ICAISE 2026)
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About this event
2026 5th International Conference on Artificial Intelligence and Software Engineering (ICAISE 2026)
The 5th International Conference on Artificial Intelligence and Software Engineering (ICAISE 2026) will be held in Tokyo, Japan, from August 21 to 23, 2026. This conference invites researchers, scientists, and scholars to contribute to an open learning environment by sharing their latest research findings, participating in discussions, and engaging with an international group of peers.
Event Overview
- Dates: August 21-23, 2026
- Venue: Tokyo, Japan (Exact venue details to be confirmed)
- Conference Type: Academic and research-focused conference
- Target Audience: Researchers, academics, industry professionals, and students in artificial intelligence and software engineering
Key Details
- Submission Open Date: October 5, 2025
- Submission Deadline: July 10, 2026
- Notification Deadline: July 20, 2026
- Registration/Final Paper Deadline: July 31, 2026
Conference Themes and Topics
The conference focuses on the intersection of artificial intelligence and software engineering. Key topics include:
- AI-Driven Software Development Processes
- Machine Learning for Software Testing and Bug Detection
- AI in Software Project Management and Estimation
- AI-Powered Code Generation and Optimization
- Explainable AI in Software Engineering
- AI for Software Quality Assurance
Conference Proceedings
Accepted papers presented at ICAISE 2026 will be published in the ACM Proceedings and indexed by Ei Compendex, Scopus, and CPCI (Web of Science).
Location Highlights
Tokyo, Japan’s capital, offers a blend of ultramodern and traditional attractions. Notable sites include:
- Meiji Shinto Shrine
- Imperial Palace
- Tokyo National Museum
- Edo-Tokyo Museum
- Tokyo Tower
- Sensō-ji Temple
Contact Information
For inquiries, please contact:
- Contact Person: Miss Snow Zhong
- Email: icaise@academic.net
- Phone Hours: 9:00-18:00, Monday to Friday (GMT+8 Time Zone)
Data sheet
2026 5th International Conference on Artificial Intelligence and Software Engineering (ICAISE 2026) – Event Attendee & Buyer Profile Analysis
Event date: August 21–23, 2026
Location: Tokyo, Kanto, Japan
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 5th International Conference on Artificial Intelligence and Software Engineering (ICAISE 2026) |
| Event Date | August 21–23, 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly confirmed on the official website at the time of research. |
| City | Tokyo |
| State / Region | Kanto |
| Country | Japan |
| Organizer | Organizer not clearly named on the official website. Official event contact listed as Miss Snow Zhong via Academic.net conference contact channel. |
| Official Event Website | icaise.org |
| Event Type | Academic and research-focused international conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Education & Training; Science & Research |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numerical attendance; high for event dates, city, country, and conference focus based on the official website. |
| Main Purpose of Event | To convene researchers, scientists, scholars, students, and selected industry professionals around artificial intelligence and software engineering research, paper presentations, technical exchange, and publication opportunities. |
About the Event
ICAISE 2026 is the 5th edition of an international conference dedicated to the intersection of artificial intelligence and software engineering. According to the official event website, the conference will take place in Tokyo, Japan from August 21 to 23, 2026 and is accepting paper submissions, poster presentations, and delegate registrations. The conference topic areas include AI-driven software development, machine learning for testing and bug detection, AI in project management, code generation, explainable AI, and software quality assurance.
From a commercial intelligence perspective, ICAISE 2026 is more research-led than procurement-led. It is most relevant for organizations targeting university researchers, R&D teams, engineering leaders, AI tooling developers, software quality teams, and innovation groups rather than high-volume product buyers. Its value for lead generation lies in thought-leadership outreach, partnership building, technical recruiting, academic collaboration, developer tooling promotion, and niche enterprise software prospecting.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers and faculty | Universities, engineering schools, AI research groups | Influence tool selection for labs, software stacks, datasets, compute resources, and collaboration platforms | High relevance for AI software vendors, developer tools, code quality platforms, and research partnerships |
| Graduate students and doctoral candidates | Universities and technical institutes | Early-stage influencers, future adopters, research contributors | Relevant for awareness, recruiting, community building, and freemium technical tools |
| Industry R&D and AI engineering teams | Software companies, AI startups, enterprise innovation teams | Can evaluate development tools, model management platforms, testing automation, and code generation solutions | Strong relevance for B2B technology suppliers |
| Software engineering leaders | Product engineering organizations, DevOps teams, QA teams | Influence purchasing around CI/CD, quality assurance, testing, observability, and secure development | High relevance for engineering productivity, testing, and automation vendors |
| Conference authors and paper presenters | Research labs, universities, technical departments, corporate research units | Thought leaders with adoption influence but not always direct budget owners | Useful for partnerships, pilots, visibility, and credibility-building |
| Technical program committee and reviewers | Academic and research institutions | High professional influence within specialist communities | Relevant for sponsorship, partnerships, and expert-network access |
| Technical publishers and indexing stakeholders | Proceedings publishers, indexing ecosystems, scholarly communications groups | Limited direct product buying; more ecosystem influence | Relevant mainly for publishing technology, academic services, and conference support providers |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Tokyo | Local faculty, students, software engineers, research labs, innovation teams | High | Tokyo is Japan’s largest business and academic hub with strong AI, software, and university presence. |
| Kanto region | Attendees from Yokohama, Kawasaki, Chiba, Saitama, Tsukuba and nearby research corridors | High | Strong concentration of universities, technology firms, labs, and advanced manufacturing research organizations. |
| Japan national market | Researchers and technical delegates from major Japanese universities and software organizations | Medium to High | National pull is likely due to the international conference format and publication pathway. |
| Asia-Pacific | Regional authors, delegates, and AI/software researchers from nearby APAC countries | Medium | The “international conference” positioning and English-language academic model support regional participation. |
| International | Selected global researchers, scholars, and technical contributors | Medium | International appeal is confirmed by the official description, but actual country mix is not publicly released. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is explicitly positioned as an international conference and invites researchers, scientists, and scholars to participate from across the global research community. |
| National / Regional | Secondary practical reach | In-person attendance will likely be strongest from Japan and nearby Asia-Pacific markets due to travel convenience and academic proximity. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| No official current-year attendee, speaker-organization, sponsor, exhibitor, or buyer directory publicly released | Data availability note | The official website confirms event dates, city, CFP, registration and proceedings details, but does not publish a current-year participant list in the source material reviewed. | icaise.org | N/A until participant organizations are published | Confirmed official data gap |
Current-year buyer-side organization targeting cannot be confirmed from official attendee evidence at this stage. This limits event-confirmed list building. Prospecting can still be done using high-fit AI, software engineering, university, and R&D organizations in Japan and APAC, but those should be treated as market-fit prospects rather than confirmed attendees.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | AI Research Director | Research & Development | Director | Influences research collaborations, technical evaluations, and specialist software adoption. |
| 2 | Professor / Principal Investigator | Academic Research | Senior | Key influencer for lab tools, collaboration projects, publications, and grants. |
| 3 | Head of Software Engineering | Engineering | Director / VP | Relevant for AI-assisted development platforms, code quality, and delivery automation. |
| 4 | Director of Engineering Productivity | Engineering / DevOps | Director | High-value buyer for CI/CD, testing automation, and AI coding assistants. |
| 5 | QA Director / Software Quality Lead | Quality Assurance | Director / Manager | Directly relevant to AI-powered testing, bug detection, and quality assurance themes. |
| 6 | CTO | Executive / Technology | C-Level | Strategic technology decision-maker for AI, software engineering tools, and innovation partnerships. |
| 7 | Machine Learning Engineering Manager | AI / Engineering | Manager | Connects AI model development with software delivery workflows. |
| 8 | DevOps Director | Infrastructure / Engineering | Director | Relevant for CI/CD and AI-assisted deployment optimization. |
| 9 | Research Scientist | Research | Individual Contributor / Senior | Important technical evaluator and collaboration target. |
| 10 | Innovation Program Manager | Innovation / Strategy | Manager | Useful for pilot projects, research commercialization, and ecosystem partnerships. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core industry for enterprise software and technical service buyers | AI engineering tools, DevOps, QA automation, consulting |
| 2 | Computer Software | Direct overlap with software engineering and AI-enabled development | Code generation, developer productivity, SDLC tooling |
| 3 | Research | High alignment with research-led conference attendance | Academic partnerships, publishing support, data platforms |
| 4 | Higher Education | Universities are core participant institutions | Lab software, research tools, educational technology |
| 5 | Computer Hardware | Relevant for compute infrastructure supporting AI workloads | GPU systems, edge devices, research compute |
| 6 | Computer Networking | Supports distributed AI and development environments | Networking infrastructure for labs and engineering teams |
| 7 | Industrial Automation | AI/software engineering themes extend into applied automation | AI deployment in operational software systems |
| 8 | Electrical/Electronic Manufacturing | Relevant where embedded software and AI-assisted engineering are used | Software QA, embedded AI development support |
| 9 | Telecommunications | Large software-intensive organizations with AI engineering use cases | Code quality, network software, AI operations |
| 10 | Internet | Internet-native companies are strong adopters of AI development tooling | Developer platforms, experimentation, AI features |
| 11 | Education Management | Useful for conference-linked academic administration and research support organizations | Program support, research administration solutions |
| 12 | Information Services | Relevant to research information, data, indexing, and knowledge systems | Research databases, technical content, scholarly analytics |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website reviewed; no attendee number published | No reliable estimate should be presented without organizer evidence. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No exhibitor directory found in official source content | This appears to be a conference rather than a conventional expo. |
| Buyer count | Not publicly confirmed | Unconfirmed | No buyer program or hosted-buyer scheme published | Not a procurement-led event format. |
| Speaker count | Not publicly confirmed in source text reviewed | Unconfirmed | Speaker navigation exists on official site, but count was not available in the provided source text | Could increase later as agenda develops. |
| Sponsor count | Not publicly confirmed | Unconfirmed | Official site shows sponsor/support sections but no named list in reviewed source text | Support structure exists but cannot be quantified. |
| Historical attendance | Not publicly confirmed | Historical / prior-year evidence unavailable in reviewed text | Official news confirms prior editions occurred, but no attendance figures were disclosed | Prior editions support event continuity, not numerical scale. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| AI-driven software development | Faster coding, design support, and development efficiency | Demonstrations of AI coding assistants, architecture tools, and workflow automation | Code generation tools, IDE extensions, developer copilots |
| Software testing and bug detection | Improve test coverage, regression efficiency, and fault detection | Engage QA leaders and engineering teams around measurable quality outcomes | Test automation, static analysis, defect prediction platforms |
| AI in project estimation and management | Predict project risk, prioritize tasks, allocate resources | Outreach to engineering ops, PMO, and delivery leadership | Planning analytics, estimation software, predictive delivery tools |
| Explainable AI in software engineering | Transparency, bias mitigation, governance, accountability | Thought leadership, compliance narratives, research collaborations | Model governance, auditability, explainability platforms |
| AI for software quality assurance | Reduce defects, speed reviews, automate performance monitoring | Connect with QA and DevSecOps stakeholders | Code review AI, observability, performance testing, QA analytics |
| Research publication and academic exchange | Publish findings, gain recognition, build peer network | Position offerings through workshops, partnerships, and scholarly visibility | Research platforms, conference services, collaboration tools |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Good for AI/software research and technical solution relevance, but weaker for direct procurement-led selling. |
| Decision-maker availability | Medium | Technical influencers are likely present; budget owners may be fewer than at enterprise buyer events. |
| Data collection potential | Low | No public attendee or exhibitor list was confirmed at the time of research. |
| Apollo targeting potential | High | Even without attendee names, the event themes map well to Apollo industry, title, department, and keyword targeting. |
| Geographic targeting potential | High | Tokyo, Kanto, Japan, and APAC provide clear geographic targeting lanes. |
| Best outreach approach | Thought-leadership and technical-value outreach | Use research relevance, engineering productivity, quality improvement, or AI governance messaging rather than generic sales pitches. |
| Overall lead quality | Medium | Best for niche B2B technology outreach, academic collaboration, and R&D partnerships. |
| Best use case | Targeted prospecting, speaker/author monitoring, partnership outreach | Most suitable for precision targeting rather than mass attendee list building. |
| Limitations / risks | High caution required | Academic conference format, unconfirmed venue, and no public participant directory reduce certainty for attendee-list sales. Suitable for B2B attendee list building only in a limited, intelligence-led way. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Computer Hardware; Internet; Telecommunications; Information Services; Industrial Automation | Align prospecting with the most likely participant institution types. |
| Departments | Engineering; Information Technology; Research; Product; Innovation; Quality Assurance; DevOps | Focus on technical and research decision paths. |
| Seniority | C-Level; VP; Director; Head; Manager; Senior | Capture strategic buyers and strong technical influencers. |
| Job titles | CTO; VP Engineering; Head of Software Engineering; Director of AI; AI Research Director; Machine Learning Engineering Manager; QA Director; DevOps Director; Research Scientist; Principal Investigator; Professor; Innovation Manager | Reach the roles most aligned to conference themes. |
| Geography | Japan; Tokyo; Kanto; APAC innovation hubs | Prioritize the strongest attendance probability zones. |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Covers startups, mid-market software firms, and large research-heavy enterprises. |
| Keywords | artificial intelligence, software engineering, code generation, software testing, bug detection, CI/CD, explainable AI, software quality assurance, machine learning, developer productivity | Improve thematic relevance when attendee names are unavailable. |
| Technologies, if relevant | ML platforms, MLOps, CI/CD, test automation, static code analysis, developer tools | Useful for product-led or technology-led segmentation. |
| Revenue range, if relevant | Use open range; prioritize funded or scaled software and research-intensive organizations | Avoid over-constraining early-stage innovation companies and academic entities. |
| Company type | Private; Public; Nonprofit; Educational; Research institutions | Reflects the mixed academic and industry attendee profile. |
Suggested Apollo Search Logic: (“artificial intelligence” OR “machine learning” OR “software engineering” OR “developer tools” OR “test automation” OR “software quality” OR “code generation” OR “CI/CD” OR “explainable AI”) AND (CTO OR “VP Engineering” OR “Director of AI” OR “Head of Engineering” OR “Research Director” OR Professor OR “Principal Investigator” OR “Machine Learning Manager”) AND (Japan OR Tokyo OR APAC).
Client Fit Review Required
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ICAISE 2026 Official Website | Official event website | Confirmed event title, dates, city, country, conference themes, submission dates, registration deadline, proceedings statement, and contact details. | High |
| ICAISE 2026 Conference Venue / Program / Speaker navigation pages | Official website structure review | Verified that venue, speaker, and program sections exist on the official site, but exact venue and public participant counts were not available in the source text reviewed. | High for absence check |
| ICAISE official news items | Official event news | Confirmed prior editions took place in 2024 and 2025, supporting event continuity. | High |
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