// Use when planning QA, choosing review methods, designing tests, or debugging fails. Triggers on: defects found late, tests pass but production bugs, coverage disputes, review ineffective, spending excessive time debugging.
Use when designing modules, APIs, or classes before implementation.
Use when reviewing code, assessing interfaces, during PR review, or evaluating 'is this too complex?' Triggers on: code review, design review, module complexity, interface assessment, PR review, structural analysis.
Use when code is too complex, has scattered error handling, configuration explosion, or callers doing module work. Triggers on: too complex, simplify, scattered errors, configuration proliferation, verbose error handling
Use after implementing code. Triggers on: is it done, ready to commit, verify correctness, did I miss anything, pre-commit check.
Use when code has deep nesting (3+ levels), complex conditionals, loop design questions, high cyclomatic complexity (McCabe >10), or callback hell. Symptoms: arrow-shaped code, repeated conditions, confusing loop exits, lengthy if-else chains
Use when auditing defensive code, designing barricades, choosing assertion vs error handling, or deciding correctness vs robustness strategy. Triggers on: empty catch blocks, missing input validation, assertions with side effects, wrong exception abstraction level, garbage in garbage out mentality, deadline pressure to skip validation, trusted source rationalization.
| name | cc-quality-practices |
| description | Use when planning QA, choosing review methods, designing tests, or debugging fails. Triggers on: defects found late, tests pass but production bugs, coverage disputes, review ineffective, spending excessive time debugging. |
Improving quality reduces development costs. No single defect-detection technique exceeds 75% effectiveness. Combining techniques nearly doubles detection rates.
Critical ratio: Mature organizations have 5 dirty tests for every 1 clean test.
Debugging rule: Do NOT skip to FIX without completing STABILIZE → HYPOTHESIZE → EXPERIMENT. ~50% of defect corrections are wrong the first time.
Internal quality enables external quality. Poor maintainability → can't fix defects → poor reliability.
Critical ratio: Mature organizations have 5 dirty tests for every 1 clean test. Immature organizations have the inverse.
The tendency to see what you expect to see. Causes "debugging blindness" where programmers overlook defects because they expect code to work. Good formatting, naming, and comments help break psychological set by making anomalies stand out.
Purpose: Execute quality, review, and testing checklists against code/process Triggers:
Purpose: Apply testing techniques, inspection procedures, and debugging method Triggers:
Test Case Generation (output: test case list):
Debugging Method (Scientific) - output: hypothesis + fix:
Critical clarification on EXPERIMENT: EXPERIMENT means a test that validates or invalidates your hypothesis WITHOUT changing production code. Examples: add logging/print statements, use a debugger to inspect state, write a failing unit test that exposes the suspected bug, inspect code to verify your theory. If you change production code, you've skipped to FIX—which has a >50% failure rate when done without understanding [Yourdon]. The experiment confirms your understanding; the fix applies it.
Inspection Procedure (output: defect list) - see Effective Inspections checklist:
digraph review_method {
rankdir=TB;
node [shape=box];
START [label="Need to review code" shape=doublecircle];
formal [label="Need highest\ndefect detection?\n(45-70%)" shape=diamond];
inspection [label="FORMAL INSPECTION\n• Defined roles\n• Checklists required\n• Preparation mandatory" style=filled fillcolor=lightblue];
dispersed [label="Team geographically\ndispersed?" shape=diamond];
codereading [label="CODE READING\n• Individual preparation\n• Minimal meeting\n• Async-friendly" style=filled fillcolor=lightgreen];
schedule [label="Schedule pressure\n+ need quality?" shape=diamond];
pairing [label="PAIR PROGRAMMING\n• Real-time review\n• 45% schedule reduction\n• Continuous feedback" style=filled fillcolor=lightyellow];
diverse [label="Need diverse\nviewpoints?" shape=diamond];
walkthrough [label="WALK-THROUGH\n• Author-led\n• Larger groups OK\n• Less structured" style=filled fillcolor=lightyellow];
default [label="Default:\nFORMAL INSPECTION" style=filled fillcolor=lightblue];
START -> formal;
formal -> inspection [label="yes"];
formal -> dispersed [label="no"];
dispersed -> codereading [label="yes"];
dispersed -> schedule [label="no"];
schedule -> pairing [label="yes"];
schedule -> diverse [label="no"];
diverse -> walkthrough [label="yes"];
diverse -> default [label="no"];
}
Key data: Inspections find 45-70% of defects; walk-throughs find 20-40%. Preparation finds 90% of inspection defects; the meeting only finds 10% more [Votta 1991].
digraph test_strategy {
rankdir=TB;
node [shape=box];
START [label="Design test cases" shape=doublecircle];
reqs [label="Requirements\ntests defined?" shape=diamond];
reqstep [label="1. REQUIREMENTS TESTS\nOne test per requirement\nInclude security, storage,\ninstallation, reliability" style=filled fillcolor=lightcoral];
design [label="Design tests\ndefined?" shape=diamond];
designstep [label="2. DESIGN TESTS\nOne test per design element\nTest interfaces, data flows" style=filled fillcolor=lightyellow];
basis [label="Basis testing\ncomputed?" shape=diamond];
basisstep [label="3. BASIS TESTING\nMinimum = 1 + count of:\nif, while, for, and, or\nPlus case branches" style=filled fillcolor=lightgreen];
dataflow [label="Data-flow paths\ncovered?" shape=diamond];
dataflowstep [label="4. DATA-FLOW TESTS\nTest all defined-used paths\nCheck: defined-defined,\ndefined-exited, killed-used" style=filled fillcolor=lightblue];
dirty [label="Dirty tests\nadded? (5:1 ratio)" shape=diamond];
dirtystep [label="5. DIRTY TESTS\n• Too little/much data\n• Wrong kind/size\n• Uninitialized data\n• Boundary violations" style=filled fillcolor=plum];
complete [label="TEST SUITE COMPLETE\nRun with coverage monitor\nActual vs believed coverage" style=filled fillcolor=lightgray];
START -> reqs;
reqs -> reqstep [label="no"];
reqs -> design [label="yes"];
reqstep -> design;
design -> designstep [label="no"];
design -> basis [label="yes"];
designstep -> basis;
basis -> basisstep [label="no"];
basis -> dataflow [label="yes"];
basisstep -> dataflow;
dataflow -> dataflowstep [label="no"];
dataflow -> dirty [label="yes"];
dataflowstep -> dirty;
dirty -> dirtystep [label="no"];
dirty -> complete [label="yes"];
dirtystep -> complete;
}
Basis testing formula: Minimum test cases = 1 + count(if) + count(while) + count(for) + count(and) + count(or) + count(case branches). If no default case, add 1 more.
digraph debugging {
rankdir=TB;
node [shape=box];
START [label="Error reported" shape=doublecircle];
intermittent [label="Error occurs\nreliably?" shape=diamond];
stabilize [label="1. STABILIZE\n• Find simplest failing case\n• Remove irrelevant factors\n• Make 100% reproducible" style=filled fillcolor=lightcoral];
hypothesis [label="2. HYPOTHESIZE\n• Use ALL available data\n• What could cause this?\n• Check recent changes" style=filled fillcolor=lightyellow];
experiment [label="3. DESIGN EXPERIMENT\n• How to prove/disprove?\n• WITHOUT changing code\n• Add logging/debugger/test" style=filled fillcolor=lightgreen];
test [label="4. RUN TEST" style=filled fillcolor=lightblue];
proved [label="Hypothesis\nproved?" shape=diamond];
fix [label="5. FIX DEFECT\n• Fix root cause, not symptom\n• One change at a time\n• Be confident before changing" style=filled fillcolor=plum];
verify [label="6. VERIFY FIX\n• Re-run original test\n• Run regression suite\n• Add test to prevent regression" style=filled fillcolor=lightblue];
search [label="7. SEARCH FOR SIMILAR\n• Same file\n• Same developer\n• Same pattern\n• Same assumption" style=filled fillcolor=lightgray];
refine [label="Refine hypothesis\nwith new data" style=filled fillcolor=lightyellow];
timelimit [label="Time limit\nexceeded?" shape=diamond];
bruteforce [label="BRUTE FORCE\n• Full code review\n• Rewrite section\n• Binary search\n• Instrument heavily" style=filled fillcolor=lightcoral];
START -> intermittent;
intermittent -> stabilize [label="no"];
intermittent -> hypothesis [label="yes"];
stabilize -> hypothesis;
hypothesis -> experiment;
experiment -> test;
test -> proved;
proved -> fix [label="yes"];
proved -> timelimit [label="no"];
timelimit -> bruteforce [label="yes"];
timelimit -> refine [label="no"];
refine -> hypothesis;
fix -> verify;
verify -> search;
}
Critical: Do NOT skip to FIX without completing steps 1-4. ~50% of defect corrections are wrong the first time [Yourdon 1986b]. Understand the problem before fixing.
Debugging blindness: Programmers mentally "slice away" code they think is irrelevant. Sometimes the defect is in the sliced-away portion. If stuck, expand your search area.
| After | Next |
|---|---|
| Defect found | cc-refactoring-guidance |
| Design issues | cc-routine-and-class-design (CHECKER) |
| Fix verified | SEARCH for similar defects, then done |