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 post-change architecture design in automotive software development, the agent uses AI to automatically trace upstream/downstream dependencies, generate new architecture solutions, and support design reviews — addressing key pain points such as incomplete impact identification, disconnected design and review processes, and missed high-risk boundary scenarios.

Efficiency Leap: Reduces hours of manual architecture analysis down to minutes 

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

Risk Coverage: Checks interface consistency, anomaly values, and state machine deadlocks in line with ASIL B conservative standards

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

Focused on automotive-grade code review scenarios, the agent integrates rule engines, knowledge graphs, and large language models to address key pain points such as inconsistent review standards, focusing only on "code quality" rather than "requirement validation," and unstable detection of high-risk issues.

Six-Dimensional Review Criteria: Delivers structured conclusions based on correctness, compliance, performance, security, readability, and maintainability 

Requirement Cross-Validation: Not only checks code quality but also verifies whether the code truly implements the detailed design requirements 

Traceable & Classifiable: Each identified issue is backed with evidence and risk classification, creating a unified review baseline

Focused on test design scenarios following requirement changes, the agent establishes a test design production line based on "change-point driven + knowledge reuse + template-based output." It addresses key pain points such as long manual design cycles, test coverage depending on individual experience, and the difficulty of reusing historical expertise.

Efficiency Leap: Compresses 1–2 days of manual test design for a single module down to minutes 

Comprehensive Coverage: Automatically covers boundary, exception, timing, and hardware interface conditions 

Standardized Delivery: Outputs engineering-ready test cases that can be directly imported into the test management process

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