Understand the failure.
Verify the repair.
Small, reproducible software failures. The broken implementation, the fix that didn’t work, and the one that passed—preserved together.
Explore the cases ↓How results are verified ↗WHAT THE ARCHIVE CONTAINS
100840 executable cases. 20168 are open.
Every case records the implementation that fails, the fix that did not work, and the repair that passed its checks—with recorded outputs and source hashes. This release adds 100840 cases across 20168 failure mechanisms and 254 domains.
The open tier gives you the failure and the unsuccessful fix for one case in every mechanism. The remaining 80672 cases, 5 variants per mechanism, are member-only: the verified repair, its recorded checks, and the full fixture suite are held in the member archive. Read the methodology ↗
A RECORD OF WHAT WENT WRONG
Browse the archive / 100840
Two-patch source-sink dispersal with ceilings: ceiling ordering · case 01
Excess recruits above the ceiling disperse into the other patch.
Two-patch source-sink dispersal with ceilings: ceiling ordering · case 02
Excess recruits above the ceiling disperse into the other patch.
Two-patch source-sink dispersal with ceilings: ceiling ordering · case 03
Excess recruits above the ceiling disperse into the other patch.
Two-patch source-sink dispersal with ceilings: ceiling ordering · case 04
Excess recruits above the ceiling disperse into the other patch.
Two-patch source-sink dispersal with ceilings: ceiling ordering · case 05
Excess recruits above the ceiling disperse into the other patch.
Two-patch source-sink dispersal with ceilings: dispersal conservation · case 01
Dispersal creates individuals.
Two-patch source-sink dispersal with ceilings: dispersal conservation · case 02
Dispersal creates individuals.
Two-patch source-sink dispersal with ceilings: dispersal conservation · case 03
Dispersal creates individuals.
Two-patch source-sink dispersal with ceilings: dispersal conservation · case 04
Dispersal creates individuals.
Two-patch source-sink dispersal with ceilings: dispersal conservation · case 05
Dispersal creates individuals.
Two-patch source-sink dispersal with ceilings: dispersal base · case 01
Dispersal numbers ignore this year's reproduction.
Two-patch source-sink dispersal with ceilings: dispersal base · case 02
Dispersal numbers ignore this year's reproduction.
Two-patch source-sink dispersal with ceilings: dispersal base · case 03
Dispersal numbers ignore this year's reproduction.
Two-patch source-sink dispersal with ceilings: dispersal base · case 04
Dispersal numbers ignore this year's reproduction.
Two-patch source-sink dispersal with ceilings: dispersal base · case 05
Dispersal numbers ignore this year's reproduction.
Two-pass removal abundance estimate: estimator numerator · case 01
Abundance is underestimated and becomes zero when the second pass is empty.
Two-pass removal abundance estimate: estimator numerator · case 02
Abundance is underestimated and becomes zero when the second pass is empty.
Two-pass removal abundance estimate: estimator numerator · case 03
Abundance is underestimated and becomes zero when the second pass is empty.
Two-pass removal abundance estimate: estimator numerator · case 04
Abundance is underestimated and becomes zero when the second pass is empty.
Two-pass removal abundance estimate: estimator numerator · case 05
Abundance is underestimated and becomes zero when the second pass is empty.
Two-pass removal abundance estimate: catchability · case 01
Capture probability falls as depletion gets stronger.
Two-pass removal abundance estimate: catchability · case 02
Capture probability falls as depletion gets stronger.
Two-pass removal abundance estimate: catchability · case 03
Capture probability falls as depletion gets stronger.
Two-pass removal abundance estimate: catchability · case 04
Capture probability falls as depletion gets stronger.
Two-pass removal abundance estimate: catchability · case 05
Capture probability falls as depletion gets stronger.
Two-pass removal abundance estimate: variance exponent · case 01
Variance estimates explode for strongly depleted streams.
Two-pass removal abundance estimate: variance exponent · case 02
Variance estimates explode for strongly depleted streams.
Two-pass removal abundance estimate: variance exponent · case 03
Variance estimates explode for strongly depleted streams.
Two-pass removal abundance estimate: variance exponent · case 04
Variance estimates explode for strongly depleted streams.
Two-pass removal abundance estimate: variance exponent · case 05
Variance estimates explode for strongly depleted streams.
Discrete delayed logistic (Hutchinson) with lag: lag index · case 01
Oscillations are weaker than the stated delay produces.
Discrete delayed logistic (Hutchinson) with lag: lag index · case 02
Oscillations are weaker than the stated delay produces.
Discrete delayed logistic (Hutchinson) with lag: lag index · case 03
Oscillations are weaker than the stated delay produces.
Discrete delayed logistic (Hutchinson) with lag: lag index · case 04
Oscillations are weaker than the stated delay produces.
Discrete delayed logistic (Hutchinson) with lag: lag index · case 05
Oscillations are weaker than the stated delay produces.
Discrete delayed logistic (Hutchinson) with lag: pre-history · case 01
Early growth explodes because the population is assumed absent before time 0.
Discrete delayed logistic (Hutchinson) with lag: pre-history · case 02
Early growth explodes because the population is assumed absent before time 0.
Discrete delayed logistic (Hutchinson) with lag: pre-history · case 03
Early growth explodes because the population is assumed absent before time 0.
Discrete delayed logistic (Hutchinson) with lag: pre-history · case 04
Early growth explodes because the population is assumed absent before time 0.
Discrete delayed logistic (Hutchinson) with lag: pre-history · case 05
Early growth explodes because the population is assumed absent before time 0.
Discrete delayed logistic (Hutchinson) with lag: growth base · case 01
Recruitment tracks the lagged population instead of current breeders.
Discrete delayed logistic (Hutchinson) with lag: growth base · case 02
Recruitment tracks the lagged population instead of current breeders.
Discrete delayed logistic (Hutchinson) with lag: growth base · case 03
Recruitment tracks the lagged population instead of current breeders.
Discrete delayed logistic (Hutchinson) with lag: growth base · case 04
Recruitment tracks the lagged population instead of current breeders.
Discrete delayed logistic (Hutchinson) with lag: growth base · case 05
Recruitment tracks the lagged population instead of current breeders.
Discrete delayed logistic (Hutchinson) with lag: overshoot reference · case 01
Overshoot is measured from the initial density instead of carrying capacity.
Discrete delayed logistic (Hutchinson) with lag: overshoot reference · case 02
Overshoot is measured from the initial density instead of carrying capacity.
Discrete delayed logistic (Hutchinson) with lag: overshoot reference · case 03
Overshoot is measured from the initial density instead of carrying capacity.
Discrete delayed logistic (Hutchinson) with lag: overshoot reference · case 04
Overshoot is measured from the initial density instead of carrying capacity.
Discrete delayed logistic (Hutchinson) with lag: overshoot reference · case 05
Overshoot is measured from the initial density instead of carrying capacity.
MacArthur-Wilson island equilibrium: distance decay · case 01
Remote islands receive more immigrants than near ones.
MacArthur-Wilson island equilibrium: distance decay · case 02
Remote islands receive more immigrants than near ones.
MacArthur-Wilson island equilibrium: distance decay · case 03
Remote islands receive more immigrants than near ones.
MacArthur-Wilson island equilibrium: distance decay · case 04
Remote islands receive more immigrants than near ones.
MacArthur-Wilson island equilibrium: distance decay · case 05
Remote islands receive more immigrants than near ones.
MacArthur-Wilson island equilibrium: area effect on extinction · case 01
Large islands lose species faster than small ones.
MacArthur-Wilson island equilibrium: area effect on extinction · case 02
Large islands lose species faster than small ones.
MacArthur-Wilson island equilibrium: area effect on extinction · case 03
Large islands lose species faster than small ones.
MacArthur-Wilson island equilibrium: area effect on extinction · case 04
Large islands lose species faster than small ones.
MacArthur-Wilson island equilibrium: area effect on extinction · case 05
Large islands lose species faster than small ones.
MacArthur-Wilson island equilibrium: equilibrium richness · case 01
Predicted richness can exceed the mainland pool.
MacArthur-Wilson island equilibrium: equilibrium richness · case 02
Predicted richness can exceed the mainland pool.
MacArthur-Wilson island equilibrium: equilibrium richness · case 03
Predicted richness can exceed the mainland pool.
MacArthur-Wilson island equilibrium: equilibrium richness · case 04
Predicted richness can exceed the mainland pool.
MacArthur-Wilson island equilibrium: equilibrium richness · case 05
Predicted richness can exceed the mainland pool.
Monthly population with seasonal breeding window: month wrap · case 01
December is treated as month 0 and every month is shifted.
Monthly population with seasonal breeding window: month wrap · case 02
December is treated as month 0 and every month is shifted.
Monthly population with seasonal breeding window: month wrap · case 03
December is treated as month 0 and every month is shifted.
Monthly population with seasonal breeding window: month wrap · case 04
December is treated as month 0 and every month is shifted.
Monthly population with seasonal breeding window: month wrap · case 05
December is treated as month 0 and every month is shifted.
Monthly population with seasonal breeding window: wrapping window · case 01
Breeding seasons that span the new year never occur.
Monthly population with seasonal breeding window: wrapping window · case 02
Breeding seasons that span the new year never occur.
Monthly population with seasonal breeding window: wrapping window · case 03
Breeding seasons that span the new year never occur.
Monthly population with seasonal breeding window: wrapping window · case 04
Breeding seasons that span the new year never occur.
Monthly population with seasonal breeding window: wrapping window · case 05
Breeding seasons that span the new year never occur.
Monthly population with seasonal breeding window: inclusive window end · case 01
The last breeding month produces no births.
Monthly population with seasonal breeding window: inclusive window end · case 02
The last breeding month produces no births.
Monthly population with seasonal breeding window: inclusive window end · case 03
The last breeding month produces no births.
Monthly population with seasonal breeding window: inclusive window end · case 04
The last breeding month produces no births.
Monthly population with seasonal breeding window: inclusive window end · case 05
The last breeding month produces no births.
Monthly population with seasonal breeding window: survival before births · case 01
Newborns are subjected to the month's mortality.
Monthly population with seasonal breeding window: survival before births · case 02
Newborns are subjected to the month's mortality.
Monthly population with seasonal breeding window: survival before births · case 03
Newborns are subjected to the month's mortality.
Monthly population with seasonal breeding window: survival before births · case 04
Newborns are subjected to the month's mortality.
Monthly population with seasonal breeding window: survival before births · case 05
Newborns are subjected to the month's mortality.
Monthly population with seasonal breeding window: density-dependent births · case 01
Populations above the cap lose animals through negative births.
Monthly population with seasonal breeding window: density-dependent births · case 02
Populations above the cap lose animals through negative births.
Monthly population with seasonal breeding window: density-dependent births · case 03
Populations above the cap lose animals through negative births.
Monthly population with seasonal breeding window: density-dependent births · case 04
Populations above the cap lose animals through negative births.
Monthly population with seasonal breeding window: density-dependent births · case 05
Populations above the cap lose animals through negative births.
Rosenzweig-MacArthur equilibrium and enrichment stability: prey equilibrium · case 01
The prey equilibrium ignores handling time.
Rosenzweig-MacArthur equilibrium and enrichment stability: prey equilibrium · case 02
The prey equilibrium ignores handling time.
Rosenzweig-MacArthur equilibrium and enrichment stability: prey equilibrium · case 03
The prey equilibrium ignores handling time.
Rosenzweig-MacArthur equilibrium and enrichment stability: prey equilibrium · case 04
The prey equilibrium ignores handling time.
Rosenzweig-MacArthur equilibrium and enrichment stability: prey equilibrium · case 05
The prey equilibrium ignores handling time.
Rosenzweig-MacArthur equilibrium and enrichment stability: conversion feasibility · case 01
Predators that cannot convert prey fast enough are reported as persisting, or vice versa.
Rosenzweig-MacArthur equilibrium and enrichment stability: conversion feasibility · case 02
Predators that cannot convert prey fast enough are reported as persisting, or vice versa.
Rosenzweig-MacArthur equilibrium and enrichment stability: conversion feasibility · case 03
Predators that cannot convert prey fast enough are reported as persisting, or vice versa.
Rosenzweig-MacArthur equilibrium and enrichment stability: conversion feasibility · case 04
Predators that cannot convert prey fast enough are reported as persisting, or vice versa.
Rosenzweig-MacArthur equilibrium and enrichment stability: conversion feasibility · case 05
Predators that cannot convert prey fast enough are reported as persisting, or vice versa.
INSPECTABLE BY DESIGN
Every result has a runnable source.
Runnable implementations with recorded outputs, source hashes, and explicit contracts. Related variants share a failure mechanism and belong together in evaluation splits.
Read the methodology ↗