Software DreamWorks

Focused on requirement change scenarios in automotive software development, the agent uses AI to automatically cross-check regulations, legacy defects, and upstream/downstream requirements — solving key pain points such as low manual analysis efficiency, difficulty identifying cross-document impact chains, and lack of traceability.

• Efficiency Leap: Cuts manual analysis time from 2–3 hours down to minutes 

• Accuracy & Reliability: Multi-dimensional cross-reasoning and risk grading eliminate human oversights 

• Asset Accumulation: Outputs fully traceable impact reports to support ASPICE/CCB reviews

Focused on architecture design following requirement change, the AI-powered Agent automatically trace upstream/downstream dependencies, generate architecture change proposals and review materials, and support design review sessions. It addresses key challenges such as as identifying all affected components, bridging the gap between design and review, and preventing the omission high‑risk boundary scenarios.

• Efficiency Leap: Compresses hours of architecture analysis down to minutes 

• Dual Output Mechanism: Directly generates block diagrams and design evaluation reports to support review meetings 

• Risk Coverage: Covers interface consistency, exception Fail‑Safe handling, state transition anomalies, and more; supports ASIL‑level functional safety assumption analysis 

Focused on automotive-grade test case generation, the agent integrates requirement parsing, historical case recall, and large model generation to address key pain points such as missing test cases for change points, misalignment between test cases and requirements, and inconsistent manual review quality.

• Multi-modal Input: Supports single-document, two-document diff, and natural language description inputs, precisely identifying change points and extracting test perspectives

• End-to-End Full Workflow: From requirement parsing to test case generation, automated review, execution traceability, and report output — achieving a 100% first-pass review rate

• Bi-directional Traceability: Each test case is linked to test perspectives and change points, meeting ASPICE/SWE.5 compliance requirements with 85.8% accuracy and 89.3% test point coverage

Focused on automated unit testing scenarios, the agent establishes a closed loop of "generate – compile – execute – supplement" to address key pain points such as low efficiency of manual coverage supplementation, "high coverage" numbers that don't guarantee executable quality, and slow, unstable progress in complex modules.

• Full-Cycle Closed Loop: Fully automated – from code generation to compilation, execution, and incremental supplementation 

• Effective Coverage: Emphasizes actual runtime pass rates and assertion pass rates, not just superficial metrics 

• Multi-Framework Support: Compatible with major testing frameworks such as Catch2, Google Test, and winAMS

Focused on automated deliverable review scenarios, the agent transforms manual inspection rules into batch-executable check processes. It addresses key pain points such as the high risk of missing rules due to complexity and fragmentation, the fact that pure AI "can read tables but may not always judge correctly," and the high cost of misjudging high-risk rules.

• Accuracy Assurance: Comprehensive accuracy reaches 95.45% 

• Tool + Rules + Semantics Synergy: Tools precisely extract structured information, the rule engine validates, and the model handles ambiguous semantics 

• Traceable Results: Outputs OK/NG/Manual Check results with an evidence chain, directly supporting SQA processes

Focused on complex AUTOSAR configuration scenarios, the agent leverages intelligent semantic parsing and embedded expert knowledge to address key pain points such as the semantic gap of non-standard requirements, the complexity of ARXML configuration, and tool import errors.

• Efficiency Leap: Compresses weeks of manual configuration down to minutes 

• Zero-Error Delivery: Built-in reflective error correction loop automatically resolves thousands of errors 

• Expert Knowledge Encapsulation: All mapping logic is embedded in the knowledge base for sustained reusability