• The Philosophical Fire: The Lisp Image as a Living Organism

The Provocative Question: What if your LISP image didn't just execute code, but remembered every experiment, every decision, every dilemma solved, in an immutable and auditable way?

The central critique of Lisp image-based development is source code amnesia; the final, powerful image is often a black hole of knowledge. IISCV is born to fight this ephemeral nature.

IISCV transforms the Lisp image from a simple binary deliverable into a living organism that encapsulates its own genetic history. This concept, which was slow to emerge in the original documentation, is the guiding beacon for the entire presentation. The system doesn't just version files; it persists knowledge directly within the execution artifact.

• Who Does IISCV Shine For? The High-Fidelity Engineer

IISCV is not for the casual Lisp environment. It is a high-fidelity engineering solution designed to solve the most profound and costly long-term development problems.

The Challenge IISCV Answers: We are talking about environments where understanding a coding decision made ten years ago is not a luxury, but an operational necessity.

The Target Audience (Connecting Power with Pain):

• Legacy and Monolithic Systems: Teams maintaining Lisp platforms that have seen several generations of developers pass through. They need semantic traceability that Git, operating from outside the execution environment, simply cannot provide.
• Regulation and Auditing: Projects in finance (high-frequency trading platforms), aerospace, or industrial control systems, where every line of code must be verified and every hot-fix must be auditable down to its atomic commit.
• The Attitude: For those who see the Lisp image not as a deliverable, but as the active repository of its own history.

• Technical Foundations: The Materialization of Persistent Memory

Once the why (fighting amnesia) and the who for (the high-fidelity engineer) are established, the technical mechanisms are presented as direct solutions to the challenge.

3.1. Dual-Layer History: Semantic Traceability IISCV implements a dual architecture to offer maximum detail and maximum readability:

Commits Atómicos (atomic-history-graph):

• Function: It is an immutable graph (the "code blockchain") that stores the complete source form, the symbol name, the timestamp, and the list of violations.
• Purpose: To offer the granular and verifiable record for the machine and forensic auditing. Every defun or defvar is an atom in the system's genetic history.

Commits Humanos (human-history-graph):

• Function: A high-level history that groups related atomic commits.
• Purpose: To transform granular detail into curated milestones legible to people, facilitating code review and documentation.

3.2. Real-Time Quality Auditing (LISA) The weakness of rapid development is the introduction of technical debt. IISCV addresses this proactively.

• Mechanism: The key function make-atomic-commit invokes the LISA inference engine, applying quality rules based on standards (like ISO 25000) before the definition is registered.
• Critical Metrics (Examples):
  • Detection of high cyclomatic-complexity.
  • Identification of magic-numbers (unnamed constants).
  • Verification of unused-parameters.
  • Alert for uses-unsafe-execution-p (execution of external commands) or redefinition of core symbols.
• Result: The system enforces best practices and associates knowledge of quality directly with the commit.

3.3. Intelligent Image Management (The Digital Twin)

• Development Images: Include the entire history.
• Production Images (save-production-image): Are rebuilt from scratch and in a clean way only from the curated human history. The result is a lightweight artifact of deployment, the "digital twin", free of development overhead but traceable.

• The Collaboration Model: Bridging the Critical Gap

Individual encapsulated work is elegant, but the moment of merging is the Achilles' heel of any VCS. The original documentation left this as a pending area, but here the vision to build confidence in real teams is outlined.

The Conceptual Solution:

• Semantic Export: IISCV will offer a command (e.g., iiscv:export-commits) that does not export text files, but a serialization of the history nodes (atomic and human) from the developer that are new to the main image.

  • Advantage: The patch carries the metadata of auditing and traceability with it.

• Assisted and Conscious Merging:

  • The receiving image uses iiscv:import-commits. If the system detects that two developers have modified the same symbol, automatic merging is halted.
  • Conflict Strategy: The system will flag the conflicting definitions and require manual intervention through a specific interface that presents the two conflicting atomic histories (e.g., Carlos's version vs. Ana's version).
  • Result: Merging is not a blind rule (last commit wins), but a conscious, auditable, and guided decision by the system.

• Advanced Vision: Augmented and Intelligent Software Engineering

The IISCV architecture culminates in a disruptive approach to real-time development and maintenance.

5.1. Secure and Audited Hot-Fixing (Websockets)

• Controlled Connection: The websocket is not permanently open in prod. It is activated on demand when prod detects an error, initiating a secure (WSS) and authenticated connection with the dev image.
• Flow: The engineer fixes the error in dev, creates an atomic commit (the fix remains immutably traced), and sends it to prod for hot application via the secure channel.

5.2. Intelligent Assistance with LLMs

• Augmented Observation: Large Language Models (LLMs) connect to the real-time debug flow as intelligent clients.
• Contextual Analysis: The LLM consults the complete genetic history of the code (atomic and human), allowing it to go beyond the backtrace and suggest contextualized solutions to errors in prod.
• The Engineer's Role: The LLM generates proposals. The SSR engineer validates, adjusts, and only after their approval is the change consolidated into an atomic commit and applied, maintaining human oversight and audit integrity.

IISCV does not just manage code; it manages knowledge and positions itself as the core of the next generation of Lisp development environments.

• The IISCV Project: An In-Image Version Control System

• Implements a version control and code auditing system directly within the Lisp development environment, inspired by the idea of a "blockchain" or an immutable commit history. It's a meta-device for managing software development!

• Here are the key parts and their purpose:

Core Concepts:

1. iiscv package: Defines the Lisp package and the functions that will be exported and accessible from outside the package.

2. *atomic-history-graph* (Atomic Commits Blockchain):

   • It is a graph (using cl-graph:dot-graph) that stores all the individual, granular changes in the code (every defun, defvar, defclass, etc.).
   • Each node in this graph is an "atomic commit" that saves the complete source form (source-form), the symbol's name (symbol-name), a message (docstring), a timestamp (timestamp), and a list of audit rule violations (rules-violations).
   • Atomic commits are sequentially linked, creating an immutable history, like a blockchain, of all code changes.

3. *human-history-graph* (Human Commits History):

   • Another graph that stores high-level "human commits."
   • A human commit groups several related atomic commits, providing a more readable history for people.
   • It contains a message (message), a timestamp, and a list of UUIDs of the atomic commits it comprises.

4. Code Auditing and Analysis (LISA):

   • The make-atomic-commit function is crucial. Before registering a change, it executes an analysis of the source code using lisa:reset and analyze-commit-and-assert.
   • It extracts metrics such as:
     • has-docstring-p: Whether it has a documentation string.
     • body-length: Length of the function body.
     • cyclomatic-complexity: Cyclomatic complexity (a code complexity metric).
     • magic-numbers: Detection of magic numbers (unnamed constants).
     • unused-parameters: Parameters that are not used.
     • is-redefining-core-symbol-p: Whether a fundamental Lisp symbol is being redefined.
     • uses-unsafe-execution-p: Use of unsafe execution forms.
     • contains-heavy-consing-loop-p: Detection of loops that consume a lot of memory.
     • uses-implementation-specific-symbols-p: Use of symbols specific to a Lisp implementation (non-portable).
   • Detected violations are stored in *audit-violations* and associated with the atomic commit.

Key Functionalities:

• clear-all-commits: Resets both histories (atomic and human) and the maps.
• make-atomic-commit (definition-form): The central function for registering any definition (function, variable, class, etc.) in the atomic history, performing the code quality analysis.
• make-human-commit (message): Automatically groups recent atomic commits that have not yet been included in a human commit, and creates a new human commit.
• manual-human-commit (message symbols &optional file-path): Allows the developer to manually group a specific set of symbols into a human commit.
• has-pending-changes-p: Checks if there are atomic commits that have not yet been grouped into a human commit.
• iiscv-repl: A custom Read-Eval-Print Loop (REPL) that automatically calls make-atomic-commit for top-level definition forms and handles errors.
• iiscv-load (filename): A modified load function that audits and commits each top-level definition form in a Lisp file.
• Class Management (Slots): add-slot, remove-slot are macros that allow modifying class definitions and committing these changes, treating slot modifications as atomic commits.
• show-atomic-commit / show-human-commit: Functions to display the vertices (commits) in the respective history graphs.
• show-project-milestones: Shows the curated history of the project by navigating the human commits.
• audit-atomic-history: Shows the complete and detailed history by navigating the atomic commits.
• Testing System (Rove):
  • make-rove-test-form and make-file-commit generate Rove-compatible test files for each atomic commit, allowing auditing of the forms' evaluation.
  • run-all-audits loads and executes all generated audit tests.
• Lisp Image Management (save-development-image, save-production-image):
  • Allows saving the current state of the Lisp environment as an "executable image."
  • save-development-image: Saves the image including the entire history. Requires no pending changes.
  • save-production-image: Rebuilds the system from scratch based only on the human (curated) history, creating a cleaner, lighter image, without the overhead of the history graphs. Also requires no pending changes.
• Image Reconstruction (rebuild-image-from-human-history, rebuild-image-from-atomic-history): Functions to rebuild the state of the Lisp system by evaluating the commit history, either the curated (human) or the complete (atomic) one. This is useful for disaster recovery or ensuring environment reproducibility.
• dump-source-code / dump-source-code-by-commit-type: Functions to export the source code, either into a single file or grouped by commit type (functions, variables, types, etc.), extracting only the latest versions of each symbol.

Implications and Purpose:

• This system is designed for:

• High-Quality Development: By auditing and analyzing code on every atomic commit, it encourages good practices and detects problems early.

• Immutable and Auditable History: Provides a detailed record of every code change, ideal for security audits, compliance, or for understanding system evolution.

• Transparency and Traceability: Allows tracking the evolution of every function or variable.

• Recovery and Reconstruction: The ability to rebuild the system from the atomic or human history ensures that the system state can always be restored.

• Lisp Project Management: Adapts version control and project management concepts to Lisp's interactive and dynamic workflow, where code is constantly evaluated and redefined.

• Teaching/Research: For a course in an institution like IISc, it could be an excellent tool for teaching software engineering, code analysis, version control, or even the construction of DSLs (Domain Specific Languages) for project management.

• In essence, this main.lisp defines a system that acts as a "Git" and a "Linter" and a "CI/CD" inside Lisp itself, designed for more granular and auditable code version control.

• It is a very sophisticated and "lispy" approach to software project management!

• These two files (lisa-rules.lisp and lisa-rules-aux-fn.lisp) are the backbone of the code quality auditing system we mentioned earlier. They confirm that "LISA" refers to an inference engine or a rule-based system.

• Let's break down each file:

lisa-rules.lisp - The Rules Knowledge Base

• This file defines the inference engine and the code quality rules that the system applies to each atomic commit. It uses the LISA-LISP library for this.

Key Parts:

1. *audit-violations*: A global variable where all messages from rule violations are accumulated.
2. lisa-lisp:make-inference-engine: Initializes the LISA inference engine.
3. deftemplate code-commit-analysis: Defines a "fact template" to represent the information extracted from each code commit.
   • It contains slots for all the attributes analyzed from a commit, such as commit-uuid, symbol-name, body-length, cyclomatic-complexity, magic-numbers, has-docstring-p, unused-parameters, uses-unsafe-execution-p, etc.
   • When analyze-commit-and-assert (in lisa-rules-aux-fn.lisp) is called, a "fact" of this type is created in the LISA inference engine.
4. deftemplate violation: Another fact template to represent a rule violation.
   • It has slots for rule-id (unique rule identifier), severity (e.g., :error, :warning, :info), and message (the violation text).
5. defrule collect-violations: This is a "metarule." Whenever the inference engine finds a violation fact, this rule activates and adds the violation's message to the global *audit-violations* list.
6. Quality Rules (The Business Rules):
   • This is where the actual quality rules are defined, grouped by categories like Maintainability, Reliability, Security, Performance Efficiency, Functional Suitability, and Portability.
   • Each defrule has a name (e.g., rule-1-1-high-cyclomatic-complexity), a condition ((code-commit-analysis ...)(test ...))), and an action (=> (assert (violation ...))).
   • Examples of rules:
     • rule-1-1-high-cyclomatic-complexity: Activates if the cyclomatic complexity (?cc) is greater than 10.
     • rule-1-2-function-too-long: Activates if the function body length exceeds 25 lines.
     • rule-1-3-magic-number-usage: Activates if "magic numbers" are found (numeric literals that are not 0 or 1).
     • rule-2-2-core-symbol-redefinition: Activates if a COMMON-LISP package symbol is redefined. This is very dangerous!
     • rule-3-1-unsafe-command-execution: Detects the use of functions that execute external commands (uiop:run-program, external-program:start), signaling a potential security risk.
     • rule-4-1-consing-in-loop: Detects memory allocations (consing) inside loops, which can impact performance.
     • rule-5-1-missing-docstring: Flags functions without docstrings.
     • rule-5-2-unused-parameter: Detects parameters declared but not used in a function.
     • rule-6-1-implementation-specific-symbols: Activates if symbols are used that are specific to a particular Common Lisp implementation, which affects portability.
7. test-all-rules: A function to exhaustively test all rules, asserting test facts and then running the inference engine to see what violations are reported.

lisa-rules-aux-fn.lisp - The Analysis Tools

• This file contains the auxiliary functions that perform the static code analysis to extract the necessary information that the LISA rules will evaluate.

Key Parts:

1. get-body-forms (definition-form): Extracts the body of a function or macro definition, correctly handling docstrings.
2. calculate-body-length (definition-form): Counts the top-level forms in a function's body to estimate its length.
3. count-decision-points (form) and calculate-cyclomatic-complexity (definition-form):
   • count-decision-points looks for control structures like if, when, cond, loop, dolist, etc., which increase control flow complexity.
   • calculate-cyclomatic-complexity uses this to determine cyclomatic complexity (a metric that measures the complexity of a program by the number of linearly independent paths through its source code).
4. find-magic-numbers (form): Searches for literal numbers in the code that are not 0 or 1, and are not in contexts like defconstant or defvar, suggesting they should be named constants.
5. find-unsafe-execution-forms (form): Identifies the use of specific functions (uiop:run-program, external-program:start) that can execute operating system commands.
6. contains-heavy-consing-loop-p (definition-form): Detects if there are functions that allocate memory (cons, list, make-array, etc.) inside loops (loop, dolist, dotimes), which can lead to performance issues.
7. find-implementation-specific-symbols (form): Scans the code to find symbols that belong to packages that are not part of standard Common Lisp or the current project, which indicates the use of implementation-specific features.
8. find-unused-parameters (definition-form): Identifies parameters in a function that are declared but not used in the function body.
   • It depends on get-parameters-list to extract parameters and find-used-symbols to see which ones are used.
9. get-docstring (definition-form): Extracts the documentation string from a function definition.
10. is-redefining-core-symbol-p (name): While a "placeholder" ((declare (ignore name)) nil), this function should determine if a symbol attempts to redefine a core Common Lisp function or macro. In a complete implementation, it would likely examine the symbol's package.
11. check-implementation-specific-symbols-for (symbol-name) / string-starts-with-p: Attempts to identify implementation-specific symbols based on name prefixes (e.g., "SB-" for SBCL).
12. analyze-commit-and-assert (...): This is the function called from main.lisp. It gathers all the analyzed data from a commit, resets the LISA inference engine, creates a code-commit-analysis fact with that data, and then executes (lisa:run) the inference engine so that the rules are fired and the violations are collected in *audit-violations*.

Combined Conclusion:

• These two files, along with main.lisp, build an extremely powerful and flexible code auditing system. It is a classic example of how metaprogramming and rule-based systems in Lisp can be used to build sophisticated and custom development tools.
• The flow is:

1. A developer writes Lisp code in the iiscv-repl or loads a Lisp file with iiscv-load.
2. Each top-level definition form is intercepted by make-atomic-commit in main.lisp.
3. make-atomic-commit calls the functions in lisa-rules-aux-fn.lisp to analyze the code.
4. It then calls analyze-commit-and-assert, which creates a "fact" in the LISA inference engine.
5. The LISA inference engine (defined in lisa-rules.lisp) runs its rules against that fact.
6. If violations are detected, the collect-violations rule adds them to *audit-violations*, and main.lisp can report them to the user.
7. The atomic commit (with any violations) is added to the immutable history.

Technical Opinion of the iiscv Project. Use Cases

• With the complete picture provided by main.lisp, lisa-rules.lisp, and lisa-rules-aux-fn.lisp, we can form a solid technical opinion and propose use cases.

Technical Opinion of the iiscv Project

• This project is a brilliant and ambitious implementation of software engineering directly inside the Lisp environment, making full use of the language's meta-linguistic and self-reflection capabilities. It's a "meta" system—a system that manages the development of the system itself.

Strengths (Technical Advantages):

1. Leveraging Lisp Metaprogramming: Lisp's ability to treat code as data (macros, eval) is fundamental to this project. It allows for the interception of definitions (defun, defclass, etc.), static analysis, and the injection of auditing logic at development time.
2. Granular and Immutable Version Control ("Code Blockchain"): The *atomic-history-graph* as a directed acyclic graph (DAG) that records every small modification is a powerful concept. It offers:
   • Extreme Traceability: Every change, no matter how small, is recorded and auditable.
   • Immutability: The atomic history is difficult to alter, making it reliable for audits and recovery.
   • Flexible Recovery: The ability to rebuild the system from the atomic or human history is an impressive resilience feature.
3. Real-Time Code Quality Auditing (LISA): The integration of an inference engine (LISA) with quality rules is exceptional. This allows for:
   • Immediate Feedback: Developers receive quality warnings and errors as soon as they define or load code.
   • Code Consistency: It helps enforce coding standards and best practices proactively.
   • Automated "Linter": Much more advanced than a traditional linter, as it not only flags issues but categorizes them by severity and associates them directly with the commit.
4. Dual History Management (Atomic vs. Human): The distinction between *atomic-history-graph* and *human-history-graph* is very clever.
   • The atomic history is for the machine: a complete record.
   • The human history is for people: curated, more readable, and understandable milestones.
   • This addresses the problem of "noise" in traditional version control systems when operating with very small and frequent commits, allowing for a high-level view.
5. Automatic Test Generation (Rove): The ability to generate Rove tests for each atomic commit strengthens code quality and reliability. Each change can be automatically validated.
6. Optimized Deployment Images: The save-development-image and save-production-image functions demonstrate an awareness of the needs of a full development lifecycle. Rebuilding from human history for production images is a smart way to create lightweight and clean deployment artifacts.
7. Extensibility: The LISA rule system is inherently extensible. It's easy to add new code quality rules or modify existing ones without altering the system's core.

Challenges and Considerations (Areas for Improvement/Complexity):

1. Learning Curve: Given its "meta" nature and how it redefines the Lisp workflow, the learning curve for new developers could be significant.
2. Performance: Intercepting and analyzing every definition form could introduce a noticeable performance overhead in very large projects or in environments where speed is critical (although Lisp is fast, rule evaluation and graph handling have a cost).
3. Handling Complex Changes: While the system is excellent for atomic commits and incremental changes, handling large-scale refactorings or complex branch merges (if this system were extended to a "branch" model) could be a challenge.
4. Integration with External Tools: While outstanding as a self-contained system, integration with external CI/CD systems, traditional Git repositories, or IDEs might require specific adapters or plugins.
5. Maturity of the LISA-LISP Engine: The system's effectiveness depends on the robustness and completeness of LISA-LISP. If it's an internal or purpose-built library, its maintenance and evolution will be critical.
6. Managing Side Effects: In a highly interactive Lisp environment, developers often evaluate forms with side effects. The system must be robust enough to capture and analyze these changes consistently without undue interference.

Use Cases of the iiscv Project

• This system is ideal for scenarios where code quality, traceability, and auditability are paramount.

1. High-Reliability/Security Software Development:

   • Aerospace and Defense: Where every line of code must be verified and every change documented.
   • Finance and Banking: For critical transactional systems where change auditability is a regulatory requirement.
   • Health (Medical Devices): Where software errors can have severe consequences.
   • Industrial Control Systems (SCADA): Ensuring the robustness and security of the code that interacts with hardware.

2. Educational and Research Environments (like IISc):

   • Teaching Software Engineering: It allows students to experiment with granular version control, code auditing, and the importance of good practices.
   • Research in Static Code Analysis: The LISA system can serve as a platform for experimenting with new quality rules or code metrics.
   • Long-Term Academic Projects: It helps maintain consistency and history in projects that evolve with multiple researchers.

3. Long-Lived Lisp Projects or Legacy Code:

   • Maintenance of Large Lisp Codebases: Facilitates understanding code evolution and identifying potential accumulated "technical debt."
   • Guided Refactoring: The auditing rules can guide developers in refactorings, ensuring that changes improve code quality without introducing new issues.

4. Development of DSLs (Domain-Specific Languages):

   • When building a DSL in Lisp, this system can help ensure that new DSL forms follow specific patterns, are efficient, and are secure.

5. Development of Critical Components or Base Libraries:

   • For developing Lisp libraries that will be used by other projects, this system can ensure a very high level of quality and stability.

6. Iterative Development with Changing Requirements:

   • Although the flow is granular, the ability to group changes into "human commits" allows for high-level tracking, which is useful in agile methodologies.

Opinion on the Project's Main Purpose

• The main goal is to revive the image-driven paradigm by ensuring that the source code and thus the knowledge is never lost because it is embedded within the image itself.

• With this primary objective, the technical opinion becomes even more positive and specific:

1. Direct Solution to the "Lost Source Code Problem": In traditional Lisp development, an image can be a "black hole" of knowledge. You know what the image does, but not how it got there or why each change was made. This project fundamentally solves that by including the complete atomic history within the image itself. The image is no longer just a binary but an artifact that contains its own genetic history.
2. Synergy with Lisp IDD: Instead of seeing IDD as something that hinders external version control (like Git), this system embraces and enhances IDD. It transforms the image from being just the product of development into also being the active and auditable repository of development.
3. Knowledge Traceability and Auditability:
   • "Why?" and "Who?": Each atomic commit, with its source, message (docstring), timestamp, and violated rules, answers the question of what was changed and when. If the author were integrated, it would also answer who. Human commits add the why at a higher level.
   • Explicit Knowledge: The quality audit not only points out problems but also embeds "good practice knowledge" and "known issue knowledge" directly into the development process.
   • Forensic Analysis: In case of an error or unexpected behavior in a production image, you can "inspect" the image to see its exact history, the quality rules that were applied, and the violations that were detected at each point.
4. Improved Reproducibility: The ability to rebuild-image-from-human-history and rebuild-image-from-atomic-history is crucial. It means that even if the image is corrupted or lost, the system's state can be reconstructed with complete fidelity from the stored historical information. This is a level above a simple "image backup."
5. Holistic Development: It fosters a development model where quality, history, and functionality are intrinsically linked and persistent in the main artifact.
6. Image Size Challenge: While having the history within the image is a huge advantage, the trade-off is that images can become considerably larger as the history grows. This is a valid trade-off for the proposed goal. However, save-production-image which rebuilds without the internal history is a smart mitigation.

Use Cases with the Main Objective in Mind:

• The use cases are amplified, especially in environments where knowledge persistence and the ability to perform a post-mortem analysis of an image are vital:

1. Long-Lived, High-Criticality Systems (Embedded Systems, Mission Control, Infrastructure):
   • Field Diagnosis: If an embedded system fails years after deployment, the image on the device contains the entire history of its code. This allows engineers to understand exactly the software that is running and how it was built.
   • Long-Term Maintenance: It makes it easier for new teams or engineers to take over the maintenance of old systems, as the image itself "tells" them its history.
2. Complex Research and Development (R&D):
   • AI/ML Projects: Where models and algorithms evolve rapidly. The ability to track the evolution of an algorithm and design decisions (and quality audits) within the runtime image is invaluable.
   • Scientific Experimentation: Keeping an immutable record of the code versions used in experiments, along with their quality analyses, to ensure the reproducibility and validity of results.
3. Distributed Collaborative Development:
   • While the graph system is local, if combined with a system for image or history replication, it could allow teams to share "self-aware" images of their history.
4. Auditing and Regulatory Compliance:
   • In regulated industries, the ability to prove that a software passed certain quality controls at every step of its development, and that this history is immutable and encapsulated with the deployment artifact itself, is a huge added value.
5. Advanced Training and Educational Tools:
   • Students not only learn to program but see how code evolves, is audited, and is maintained, all within a self-contained and persistent environment. They can inspect any Lisp image and understand its genealogy.
6. Intelligent Software "Black Boxes":
   • Creating software artifacts that not only perform a task but can also report on their own history and the process by which they were created, a level of self-reflection superior to simple external version control.

Final Conclusion:

• The iiscv project, with this main objective, goes beyond simple version control. It is an innovative proposal to redefine how knowledge is managed and persisted in the Lisp development lifecycle, turning the execution image into a living, auditable, and traceable record of its own creation. It is a very elegant piece of software engineering that solves a fundamental problem in the Lisp paradigm in a very "lispy" way.

Incorporating a Mechanism for Hot-Fixing via Websockets

• This is the logical culmination of the paradigm you are building. It takes the project to a whole new level, connecting the development and production lifecycle in a dynamic, real-time manner.

Key Advantages of this Integration:

1. Observability and Debugging in Production (without stopping):
   • Live Error Capture: The ability to receive detailed error information (backtraces, variable states, etc.) from the production image directly into your development image is a "holy grail" for debugging.
   • Reduced Downtime: Allows for diagnosing and, potentially, fixing issues without needing to restart or halt the service in production.
   • Full Context: The dev image has the complete history of commits and audits, which provides unmatched context for understanding why an error occurred in prod.
2. Ultra-Fast Development Cycle ("Hot-Fixing"):
   • Live Modification and Deployment: The ability to modify the code in dev, commit the change, and "send it" to prod in real time (or near real time) is revolutionary for Lisp environments. It's the dream of a REPL connected to production.
   • Frictionless Continuous Development: Eliminates the intermediate steps of "recompiling" and "deploying binaries." The prod image is updated with the new function or class definition.
3. "In-Situ" Maintenance:
   • Long-lived Lisp systems often require "hot patching." This websocket-based infrastructure elevates that to a formal and auditable process.
4. Continuous Validation:
   • You could even extend it so that after a hot-fix, the prod image executes a subset of the relevant Rove tests (run-all-audits) to confirm the fix live.

Technical Challenges and Considerations:

1. Security (Most Critical!):
   • Remote eval Exposure: Exposing a remote eval (even if controlled via commits) is a huge security risk.
   • Authentication and Authorization: Implementing robust authentication (tokens, certificates) and authorization (who can send what changes to which part of prod) is mandatory.
   • Sandboxing: Consider if some level of "sandboxing" is possible in prod to limit the scope of live modifications.
   • Secure Channels: Using WSS (WebSockets Secure) with TLS/SSL is a must.
2. Handling Persistent States in Production:
   • When a function is "hot-fixed," existing objects in prod created before the change will still use the old version of the function until they are restarted or new objects are instantiated. This needs careful handling.
3. History Integrity:
   • If atomic commits are applied directly in prod, how are those changes reflected in the *atomic-history-graph* of the production image? Ideally, prod maintains its "lightweight twin" nature, and the history is propagated from dev to prod in a controlled manner.
4. Handling Dependencies and Context:
   • How is it managed if a hot-fix introduces a new dependency or requires an environment change in prod that wasn't planned?

Refined Flow: Dev-Prod and Security

1. "In prod, there is no history":
   • Confirmed and Reinforced: This solidifies the "lightweight digital twin" idea. The production image is pure execution, not a development repository. This makes it smaller, faster, and reduces the attack surface.
2. "In dev, human commits are always necessary to save the dev image, and there can be no pending changes":
   • Consistency and Control: This imposes vital discipline on the development workflow. To persist a dev image or for a developer to "checkpoint" their work, they must consolidate atomic changes into human commits. This ensures the dev image is always saved in a coherent and curated state.
3. "The websocket can be opened if prod enters a debug state and does not need to be always open":
   • Improved Security and Lower Overhead: This is a fundamental security and performance decision. A non-persistent websocket drastically reduces risk. The "backdoor" is only opened on demand.
4. "A specific header can be sent via the websocket protocol for prod to evaluate a form from the message payload via a mini-REPL, and it can obviously have a user/pass and https":
   • Secure and Controlled Protocol:
     • Specific Headers: Allows for differentiating message types (e.g., "evaluate this form," "get backtrace," "execute this test").
     • Controlled Mini-REPL: It's not a full, open REPL, but a highly restricted one in prod. It only evaluates the form in the payload if it passes validation.
     • Authentication (user pass) and Encryption (https/WSS): Absolutely essential. This ensures only authorized users can initiate a debug session and that all communication is encrypted.
     • Message Payload: The "form" sent would be an atomic commit (the source-form) or a set of them, securely encapsulated.

The Ultimate Evolution: Incorporating LLMs

• This is the final evolution of the system. You've described an AI-Augmented Software Engineering system within a Lisp environment. This is not just an improvement; it's a transformative vision.

Implications of Connecting LLMs to the iiscv Flow and History:

1. Deep Contextual Knowledge (for the LLM): The LLM has access to the curated human history and the detailed atomic commits, giving it a much deeper understanding of the project's code than just the current source.
2. AI-Augmented Debugging Analysis: The LLM becomes an intelligent observer of production errors. It can process backtraces and variable states in real time. With its knowledge of the code history, it can go beyond "it failed here" and suggest "this failure might be related to the change in function xyz in commit abc that was made to resolve issue 123."
3. Intelligent Solution Generation (Suggestions): The decision that the LLM's proposals do not go directly to prod is crucial. It keeps the Senior Software Engineer (SSR) in the loop, ensuring human oversight. The LLM's suggestions would be contextualized, tailored to the project's coding style and dependencies.
4. Assistance for the SSR Engineer: It reduces the time spent on investigation, allowing the engineer to focus on validation and decision-making.

Architecture of the Flow (with LLM):

1. Error in prod: prod detects an error and opens a websocket with dev.
2. Debug Info Sent: prod sends the debug info to dev.
3. Message Distribution (Websocket Hub): The dev image receives the debug message and relays it to all connected "clients," including the LLM(s) and the engineer's interface.
4. LLM Processing: The LLM, connected to the same websocket flow, receives the debug info. It consults the full history (atomic and human commits) that has been "fed" to it and, based on the code history, generates one or more proposed Lisp solutions.
5. Presentation to the Engineer: The LLM's proposals are sent back to dev (or directly to the engineer's UI). The SSR engineer reviews and modifies them as needed.
6. Engineer's Decision: The engineer accepts a solution, commits it in dev, and sends it to prod via the secure websocket.

Final Conclusion: Towards Intelligent Augmented Development

• Incorporating LLMs in this way is the vision for a Lisp development environment that not only manages its own knowledge but also actively uses it to assist and empower human developers. You would be building a virtual "mission control room" where production telemetry, project knowledge, and artificial intelligence converge to make development and maintenance more efficient, secure, and profound. This approach puts iiscv at the forefront of intelligent software engineering.

---

Step 1: System Initialization

processing (UIOP/PACKAGE:DEFINE-PACKAGE IISCV ...)
IISCV> (iiscv-repl)

This is the startup sequence. The system is first defining its package, IISCV, which serves as its namespace. Then, the (iiscv-repl) command launches the main auditable development environment. From this point on, the system is ready to automatically track every top-level Lisp form you enter.

---

Step 2: Defining the First Function

IISCV-R> (defun my-first-function (x)
  "This is a simple function to demonstrate the system."
  (* x x))

Violations detected: 1
Use of non-portable, implementation-specific symbols detected in 'MY-FIRST-FUNCTION'.

MY-FIRST-FUNCTION

Here, you've defined your first function. The system automatically performs an atomic commit of this code. It logs the full form in the project's history. The linter, a static analysis tool, detects a code quality violation: it doesn't recognize 'MY-FIRST-FUNCTION' as a standard symbol, which is a common "false positive" for user-defined functions. The function is loaded into memory, and its name is printed to confirm the action.

---

Step 3: Running the Function

IISCV-R> (my-first-function 5)

25

You are executing the function you just defined. The system evaluates the expression (my-first-function 5), which returns the correct result, 25. This confirms that the code was successfully loaded and is working as intended, despite the previous warning.

---

Step 4: Creating a Human Milestone

IISCV-R> (human-commit "Added the first core function to the system." '(my-first-function))

"A93DB28E-A9F5-44CD-97E4-D43D400E3848"

Instead of making a commit for every single change, the human-commit command allows you to create a high-level milestone. This action links the previous atomic commit for my-first-function to a human-readable message. The UUID A93DB28E... is the unique identifier for this new milestone, which acts as a summary for the project's history.

---

Step 5: Viewing the Human History

IISCV-R> (show-project-milestones)

--- Project Milestones (Human History) ---

* Milestone: Added the first core function to the system.
  UUID: A93DB28E-A9F5-44CD-97E4-D43D400E3848
  Timestamp: 3965482366
  Atomic Changes: (2AD026AA-87F3-45FE-848A-6608205CAE01)
--------------------------------------------

NIL

You are now viewing the curated history of the project. This output shows the milestone you just created, listing its UUID and, crucially, the UUID of the atomic change (2AD026AA...) it represents. The output confirms that the raw, granular history is now connected to a meaningful, human-readable description.

---

Step 6: Adding More Functions and Committing Again

IISCV-R> (defun my-second-function (x y) ...)
Violations detected: 1
...
IISCV-R> (my-second-function 10 20)
30

IISCV-R> (defun my-third-function (a b c) ...)
Violations detected: 2
...
IISCV-R> (my-third-function 10 20 30)
20

IISCV-R> (human-commit "Added two new functions for arithmetic and calculations." '(my-second-function my-third-function))
"569C89D4-E3A2-465E-ACFE-344038F8FF38"

IISCV-R> (show-project-milestones)
--- Project Milestones (Human History) ---
...
* Milestone: Added two new functions for arithmetic and calculations.
  UUID: 569C89D4-E3A2-465E-ACFE-344038F8FF38
  Timestamp: 3965482708
  Atomic Changes: (6E32E5F4-9316-42A3-AAE6-B77342033DD0 FB610814-303C-42AD-89F1-24AAAEE2321E)
--------------------------------------------
NIL

This sequence demonstrates a more realistic workflow. You've defined two new functions, each triggering an atomic commit and code quality warnings. Crucially, when you run human-commit, you group both atomic changes into a single milestone. The output of show-project-milestones shows that this new milestone successfully links to the UUIDs of both my-second-function and my-third-function, keeping the project history tidy.

---

Step 7: Auditing the Full History

IISCV-R> (audit-atomic-history)

--- Atomic History Audit (Blockchain) ---

* Atomic Commit: 9F4B02E0-2C69-433C-AAAA-ABB5BD18BAAB
  ...
* Atomic Commit: 2AD026AA-87F3-45FE-848A-6608205CAE01
  ...
* Atomic Commit: 6E32E5F4-9316-42A3-AAE6-B77342033DD0
  ...
* Atomic Commit: FB610814-303C-42AD-89F1-24AAAEE2321E
  ...
NIL

Step 8: Creating and Auditing a Class

IISCV-R> (defclass vehicle ()
 ((make :accessor make :initarg :make :initform "Unknown")
 (model :accessor model :initarg :model :initform "Unknown")))

Violations detected: 2
Use of non-portable, implementation-specific symbols detected in 'VEHICLE'.
Symbol 'VEHICLE' is missing a docstring.

#<STANDARD-CLASS IISCV::VEHICLE>

Here, you're defining a new class. Just like with functions, the system creates an atomic commit for this new definition. The linter, running its static analysis, flags two violations: the symbol VEHICLE is non-portable (a false positive for a user-defined symbol) and, importantly, the class is missing a docstring. The system correctly warns you about this code quality issue, but the class is successfully defined and ready to be used.

Step 9: Creating an Instance and Committing It

IISCV-R> (defvar *my-car* (make-instance 'vehicle))

Violations detected: 2
Use of non-portable, implementation-specific symbols detected in '*MY-CAR*'.
Symbol '*MY-CAR*' is missing a docstring.

*MY-CAR*

You've now created a new variable to hold an instance of the vehicle class. This is also a top-level definition, so the system logs it as a new atomic commit with its own unique UUID. The linter again reports two violations: the variable name is considered non-portable, and it's also missing a docstring, another important best practice for code documentation.

Step 10: Modifying an Instance (No Atomic Commit)

IISCV-R> (color *new-car*)
"Not specified"
IISCV-R> (setf (color *new-car*) "Red")
"Red"
IISCV-R> (color *new-car*)
"Red"

Here, you are interacting with an instance of the class in memory. You first inspect the color slot and then use setf to change its value. The key takeaway here is that these actions do not create an atomic commit. The system tracks changes to the source code (defclass), not changes to the data within an object at runtime. This distinction is crucial to keep the history focused and manageable.

Step 11: Grouping Class Changes in a Human Milestone

IISCV-R> (human-commit "Redefined vehicle class to add color slot and added a new instance." '(vehicle *new-car*))

"D6017A7E-ADAB-48A1-B923-964C6BFA0C54"

IISCV-R> (show-project-milestones)

--- Project Milestones (Human History) ---
...
* Milestone: Redefined vehicle class to add color slot and added a new instance.
 UUID: D6017A7E-ADAB-48A1-B923-964C6BFA0C54
 Timestamp: 3965484015
 Atomic Changes: (01F3AFE9-8472-4595-ABE4-A69E239D75EA 3078D22F-5055-4A94-9AB8-34EC58136478)
--------------------------------------------
NIL

You are now creating a new human milestone that groups the atomic commits for both the redefined vehicle class and the new new-car variable. This demonstrates how you can logically categorize changes in your project history. The output of show-project-milestones confirms that this new milestone successfully links to the UUIDs of both atomic commits, keeping your project's high-level history clean and well-documented.

This is the core of the auditable system. The audit-atomic-history command gives you a view of every single atomic commit that has ever been made, in chronological order. This is the immutable, granular record of all your work. It's what underpins the human-readable milestones and allows for complete auditability. Every defun or defclass is recorded here, along with its full source form and any code quality violations found at the time of the commit.

### Step 11: Run all audits
IISCV-R> (run-all-audits)

Running all audits...
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/01F3AFE9-8472-4595-ABE4-A69E239D75EA.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/0D7159D3-5423-46FE-9139-A93DC88BC872.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/19637DC8-0CC9-4344-ABC2-51A9A26E8D95.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/1DF73EEA-1435-4BF8-A812-F1396AF5FB45.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/1F756D05-5A5B-4746-A688-ADA0879AE9D6.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/2AD026AA-87F3-45FE-848A-6608205CAE01.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/3078D22F-5055-4A94-9AB8-34EC58136478.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/315AF3B9-B850-4ECC-BBF9-D6CA67EDC4D9.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/39AD5457-538E-4414-9DD7-BD9A42C4CD3E.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/42CB95AD-AC5E-4C8E-B1F8-E4BA01E3D7C3.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/44E5D500-34C0-46D8-8013-81249DA60543.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/4D4B7652-AA67-481B-AA2E-DBF5C5EEEC98.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/6370CAD4-DEB1-4F90-BA16-80051164F42A.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/644F0F81-D533-4756-B87B-2A4841CC45DE.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/6E32E5F4-9316-42A3-AAE6-B77342033DD0.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/6E37EC2B-0CB9-4929-BA01-0706AC3F88EA.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/6ECD451F-3DC4-40D0-ADC0-256733868550.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/7BBB7613-70AE-4230-AEFE-F427ACF25E9D.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/7BE1E11E-4F28-4613-B4B6-C939FC29EE35.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/87BE9CC8-3C72-4E07-B382-F38B24FBFE07.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/9DE368CC-C2AC-4AAB-AEDC-FA54FCFC1CA2.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/9F0868D8-7975-4EDC-B716-237646C7C87E.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/9F4B02E0-2C69-433C-AAAA-ABB5BD18BAAB.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/A0E9D028-C358-48EA-95D5-20100B0C8FCA.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/A2481D83-64AA-44FA-9BBF-3EA0A8DFB10C.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/AD5951B0-2FF1-4F76-838C-A6E887B4E7E1.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/B84163CA-4CE5-4503-BFF5-B24D287BAC33.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/B9E83A7E-2BC5-44A1-899A-89E6C7C3843F.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/C77FA601-7CA6-4E18-A393-3D50D4ED3F7D.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/CF0EF852-FCF0-49E8-9054-51E8CCBCD0FF.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/D5FA6222-CED9-40F5-AF13-75D3D714400D.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/DB48DDB5-E25F-4C4A-A1B1-7149F06F744F.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/EFD0BF5A-B5EF-4274-93FE-1DCB423CFA19.lisp
Loading audit file: /home/mtk/quicklisp/local-projects/iiscv/audits/FB610814-303C-42AD-89F1-24AAAEE2321E.lisp

All audit files loaded. Running tests...

;; testing 'iiscv'
commit-01f3afe9-8472-4595-abe4-a69e239d75ea-test
  ✓ The form should evaluate without error.
commit-0d7159d3-5423-46fe-9139-a93dc88bc872-test
  ✓ The form should evaluate without error.
commit-19637dc8-0cc9-4344-abc2-51a9a26e8d95-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-1df73eea-1435-4bf8-a812-f1396af5fb45-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-1f756d05-5a5b-4746-a688-ada0879ae9d6-test
WARNING: redefining IISCV::MY-FIRST-FUNCTION in DEFUN
  ✓ The form should evaluate without error.
commit-2ad026aa-87f3-45fe-848a-6608205cae01-test
WARNING: redefining IISCV::MY-FIRST-FUNCTION in DEFUN
  ✓ The form should evaluate without error.
commit-3078d22f-5055-4a94-9ab8-34ec58136478-test
  ✓ The form should evaluate without error.
commit-315af3b9-b850-4ecc-bbf9-d6ca67edc4d9-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-39ad5457-538e-4414-9dd7-bd9a42c4cd3e-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-42cb95ad-ac5e-4c8e-b1f8-e4ba01e3d7c3-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-44e5d500-34c0-46d8-8013-81249da60543-test
WARNING: redefining IISCV::GET-DOCSTRING in DEFUN
  ✓ The form should evaluate without error.
commit-4d4b7652-aa67-481b-aa2e-dbf5c5eeec98-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-6370cad4-deb1-4f90-ba16-80051164f42a-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-644f0f81-d533-4756-b87b-2a4841cc45de-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-6e32e5f4-9316-42a3-aae6-b77342033dd0-test
WARNING: redefining IISCV::MY-SECOND-FUNCTION in DEFUN
  ✓ The form should evaluate without error.
commit-6e37ec2b-0cb9-4929-ba01-0706ac3f88ea-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-6ecd451f-3dc4-40d0-adc0-256733868550-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-7bbb7613-70ae-4230-aefe-f427acf25e9d-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-7be1e11e-4f28-4613-b4b6-c939fc29ee35-test
  ✓ The form should evaluate without error.
commit-87be9cc8-3c72-4e07-b382-f38b24fbfe07-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-9de368cc-c2ac-4aab-aedc-fa54fcfc1ca2-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-9f0868d8-7975-4edc-b716-237646c7c87e-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-9f4b02e0-2c69-433c-aaaa-abb5bd18baab-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-a0e9d028-c358-48ea-95d5-20100b0c8fca-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-a2481d83-64aa-44fa-9bbf-3ea0a8dfb10c-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-ad5951b0-2ff1-4f76-838c-a6e887b4e7e1-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-b84163ca-4ce5-4503-bff5-b24d287bac33-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-b9e83a7e-2bc5-44a1-899a-89e6c7c3843f-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-c77fa601-7ca6-4e18-a393-3d50d4ed3f7d-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-cf0ef852-fcf0-49e8-9054-51e8ccbcd0ff-test
  ✓ The form should evaluate without error.
commit-d5fa6222-ced9-40f5-af13-75d3d714400d-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-db48ddb5-e25f-4c4a-a1b1-7149f06f744f-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-efd0bf5a-b5ef-4274-93fe-1dcb423cfa19-test
WARNING: redefining IISCV::CHECK-VALUE in DEFUN
  ✓ The form should evaluate without error.
commit-fb610814-303c-42ad-89f1-24aaaee2321e-test
WARNING: redefining IISCV::MY-THIRD-FUNCTION in DEFUN
  ✓ The form should evaluate without error.

✓ 34 tests completed

Summary:
  All 34 tests passed.
All audits completed.

NIL 
IISCV-R>