Taiga Okuma

2papers

2 Papers

6.1SEApr 30Code
DEPTEX: Organization-First, Open Source Dependency Risk Monitoring

Henry Ruckman-Utting, Vrushal Nedungadi, Taiga Okuma et al.

Open-source software (OSS) dependencies introduce systemic risks that are difficult to manage at scale. Existing Software Composition Analysis (SCA) and reachability tools generate severe alert fatigue by treating risk as an intrinsic component property, ignoring semantic context and forcing enterprises into rigid compliance frameworks. We present Deptex, an organization-first, graph-based platform treating supply chain risk as emergent. Deptex introduces Execution Path Dominance (EPD), fusing Code Property Graph (CPG) slicing with Large Language Model (LLM) semantic verification to calculate a vulnerability's true operational blast radius. To handle bespoke compliance, Deptex abstracts governance into a programmable ``As Code'' engine, enabling security teams to natively enforce dynamic pull request policies, custom asset tiers, and external API integrations. By shifting from reactive scanning to context-aware governance, Deptex enables proactive, efficient, and aligned supply chain risk management.

8.6HCMay 11
How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy

Haidan Liu, Isabelle Kwan, Taiga Okuma et al.

As generative AI tools increasingly influence creative practice, they raise longstanding HCI questions about how creatives learn complex software and how they can be better supported. We conducted an interview study with artists and hobbyists (n=8) and a follow-up survey (n=159) to understand how this population approaches and seeks guidance for GenAI image tools. We found that creatives commonly use either self-experimentation or tutorials to explore GenAI tools, yet many struggle with confusing AI terminology. To gain further insight into creatives' learning experiences, we developed a research probe to elicit creatives' perceptions of structured guidance. Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity. Our findings highlight a central tension in supporting AI literacy for creatives: balancing guidance and promoting literacy while preserving creative freedom.