Part VI Appendices

Chapter 15 – Appendix B: Failure Mode Gallery

This appendix catalogues common failure modes encountered when building and operating Software Development as Code (SDaC) loops. Understanding these patterns helps in designing robust systems, writing effective Mission Objects, and debugging issues when they arise. Each entry describes the failure, its typical causes, provides an example, and suggests diagnostic and mitigation strategies.

Slice Too Large or Too Small

What it looks like

The generative system (e.g., your Map or Updater) produces output that is either overwhelmingly broad and unfocused, or excessively granular and ineffective for the given task.

Why it happens

This typically stems from an imbalanced mission for your generative step, often embedded in the system’s Mission Object.

Example

Consider an Updater whose mission is to “refactor the UserPreferences module to improve performance.”

Diagnostics

Mitigation

Thrash

What it looks like

The SDaC loop gets stuck in a cycle where the Updater repeatedly makes and undoes changes, or makes minor, ineffective adjustments that don’t lead to a resolution. This can manifest as:

Why it happens

Thrash typically results from a mismatch or conflict between the mission, the Updater’s capabilities, and the validation rules.

Example

An Updater is tasked with “ensuring all new functions have docstrings.” A Validator enforces the presence of docstrings and also a ‘maximum line length of 80 characters.’

  1. Updater: Adds a docstring to a function, but the docstring makes the line exceed 80 characters.

    def calculate_sum(a, b):
    """This function calculates the sum of two numbers, a and b, and returns the result.""" # Line too long
    return a + b

  2. Validator: Fails due to line length.

  3. Updater: Tries to Refine the line length, but in doing so, either removes part of the docstring (making it too short or invalid) or formats it in a way that the Validator still finds problematic, or simply tries to wrap it without realizing that the original docstring content itself is too verbose.

    def calculate_sum(a, b):
    """Calculates the sum of two numbers.""" # Valid line, but less descriptive. Original mission was not to shorten it.
    return a + b

    (Or, if the Updater focuses only on the line length, it might remove the docstring entirely, failing the original docstring mission.)

This cycle repeats, with the Updater failing to satisfy both constraints simultaneously, or making partial Refinements that don’t stick.

Diagnostics

Mitigation

Map-Updater Invents Structure

What it looks like

The generative system (typically the Map or Updater) introduces new file paths, directories, data structures, or architectural patterns that were not explicitly part of its mission, were not present in the provided context, or deviate significantly from established project conventions. This often leads to:

Why it happens

This is a form of Stochastic Drift or over-creativity, often stemming from an overly broad mission or insufficient guardrails.

Example

An Updater is tasked with “adding a new NotificationService to handle email alerts.” The existing project structure has services in src/app/services/ and configurations in src/app/config/.

Instead of adding notification_service.py to src/app/services/ and its config to src/app/config/, the Updater creates:

new_feature/
|-- notification_service_v2.py
|-- notification_config.yaml
`-- templates/
    `-- email_template.html

This new new_feature/ directory and its contents are entirely outside the established project structure. The build system doesn’t pick up notification_service_v2.py, notification_config.yaml is in a new format, and templates/ duplicates existing templating mechanisms.

Diagnostics

Mitigation

Validator False Positives

What it looks like

A Validator incorrectly flags a correct, acceptable, or intended change as an error. This can lead to:

Why it happens

False positives arise when the Validator’s rules are misaligned with the project’s actual requirements, the intent of the generative step, or the capabilities of the system.

Example

An Updater is tasked with “optimizing string concatenation” in a Python file. It changes s = "a" + "b" + "c" to s = f"a{b}c" for better readability and performance with variables.

A static analysis Validator is configured with a rule that flags f-strings as “too new” or “not preferred” if the codebase primarily uses older .format() calls, leading to a false positive even though f-strings are standard Python 3.6+ practice.

--- a/src/utils/string_formatter.py
+++ b/src/utils/string_formatter.py
@@ -1,3 +1,3 @@
 def format_message(name, event):
-    return "Hello, " + name + "! Welcome to " + event + "."
+    return f"Hello, {name}! Welcome to {event}." # Validator flags this line: "Avoid f-strings; use .format() instead."

Diagnostics

Mitigation

Actionable: What you can do this week

  1. Start a “Failure Log”: Begin a simple text file or spreadsheet where you record any unexpected or problematic behaviors you observe from your SDaC loop. For each entry, describe the observed failure, the generative step involved (Map, Updater, Validator), and your initial hypothesis about the cause.

  2. Inspect Diffs and Traces: For the next few automated changes your system attempts, don’t just look at the final outcome. Examine the full diff and, if available, any intermediate outputs or reasoning from your generative models. Look for patterns related to “slice size” (too big, too small) or unexpected structural changes.

  3. Review Validator Outputs: Pay close attention to the specific error messages from your Validators. If an automated change is rejected, ask yourself: Is this error message truly indicative of a problem, or could it be a false positive given the context of the change?

  4. Refine Your Core Mission Objects: Based on your observations from steps 2 and 3, review the Mission Objects for your Map and Updater. Try adding or refining constraints related to scope, file paths, or expected output structure to explicitly guide the generative models away from the failure modes discussed here. For example, add a line like “Only modify files within src/feature_x/” if you’re seeing overreach.