2_use_case_name: Modernization Accelerator - Legacy Applications Chatbot & Code Conversion
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The IRS Enterprise has many systems that use a legacy coding language, which is much older than typical modern coding languages. This makes it difficult to find skilled and knowledgeable people to maintain or modernize applications. A large language model (LLM) can help speed up this translation so we can more rapidly modernize while retaining the functional objectives of the legacy code. This use case will consist of an iterative multi-phase approach to help accelerate the modernization of sample legacy code by building a Generative AI (GenAI) Sandbox environment that will allow us to ingest a subset of IRS legacy documentation to build a LLM GenAI chatbot. This will accelerate knowledge discovery for future legacy modernization initiatives. In addition, we will leverage GenAI to ingest a subset of IRS Assembler Language Code (ALC) legacy code to evaluate, validate, and optimize GenAI capabilities to not only transform legacy code into English readable pseudocode, and convert it into a modern language. This initial use case will be limited to IRS use only within a sandbox environment and not intended for production.

Existing Code to Java
LLM within IRS boundary
12_outputs: The AI accelerator outputs a document that describes the functionality of the ALC code ingested; identifying loops and conditions as well as key architectural and functional diagrams that can assist a developer understanding complex legacy code.
The Retrieval Augmented Generation (RAG) Chatbot will generate responses grounded in the files that are ingested, including confidence scores, to aide business analysis efforts
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 3/15/2024
20_date_implemented: 10/31/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: TIRNO-17-D-00004 WR2730
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: No models are being trained. Outputs are being calibrated through prompt engineering. The inputs will be supporting documentation available to the public in PDF format, as well as a subset of ALC code (.ASM files) approved for ingestion. No Personally Identifiable Information (PII) data will be used. Any additional inputs used will go through appropriate review process.

ASM code is a shorthand for machine language. Each ASM language is designed for a specific computer architecture, and sometimes an operating system.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Infrastructure is provided through innovation lab managed service.
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.