COBOL-REKT, a toolkit for analysing and reverse-engineering COBOL

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This is an evolving toolkit of capabilities helpful for analysing and reverse engineering legacy Cobol code. Currently, the following capabilities are available: - Program / Section-level flowchart generation based on AST (SVG or PNG) - Parse Tree generation (with export to JSON) - Control Flow Tree generation (with export to JSON) - Allows embedding code comments as comment nodes in the graph - The SMOJOL Interpreter (WIP) - Injecting AST and Control Flow into Neo4J - Injecting Cobol data layouts from Data Division into Neo4J (with dependencies like MOVE, COMPUTE, etc.) + export to JSON - Injecting execution traces from the SMOJOL interpreter into Neo4J - Integration with OpenAI GPT to summarise nodes using bottom-up node traversal (AST nodes or Data Structure nodes) - Exposes a unified model (AST, CFG, Data Structures with appropriate interconnections) which can be analysed through [JGraphT]( https://jgrapht.org/ ), together with export to GraphML format and JSON. - Support for namespaces to allow unique addressing of (possibly same) graphs - ALPHA: Support for building Glossary of Variables from data structures using LLMs - ALPHA: Support for extracting Capability Graph from paragraphs of a program using LLMs - ALPHA: Injecting inter-program dependencies into Neo4J (with export to JSON) - ALPHA: Paragraph similarity map Contributions / use cases are welcome!

Visit product View on Hacker NewsAug 15, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, openai, using · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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