Running ‘testthat.R’ [72s/99s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(psycho)
  > 
  > test_check("psycho")
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 5.8e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.58 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
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  Chain 1: 
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  Chain 1:                0.977589 seconds (Sampling)
  Chain 1:                3.33712 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 8.6e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1:                2.64575 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 5.9e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.59 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
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  Chain 1: 
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  Chain 1:                0.156644 seconds (Total)
  Chain 1: 
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 6.4e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
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  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1:    100         -403.800             1.000            1.000
  Chain 1:    200         -199.756             1.011            1.021
  Chain 1:    300         -195.131             0.682            1.000
  Chain 1:    400         -195.234             0.511            1.000
  Chain 1:    500         -194.906             0.409            0.024
  Chain 1:    600         -195.149             0.341            0.024
  Chain 1:    700         -195.587             0.293            0.002   MEDIAN ELBO CONVERGED
  Chain 1: 
  Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
  Chain 1: COMPLETED.
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 6.8e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
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  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1:    100       -93424.409             1.000            1.000
  Chain 1:    200         -590.743            79.074          157.147
  Chain 1:    300         -522.796            52.759            1.000
  Chain 1:    400         -197.216            39.982            1.651
  Chain 1:    500         -200.744            31.989            1.000
  Chain 1:    600         -197.522            26.660            1.000
  Chain 1:    700         -200.282            22.854            0.130
  Chain 1:    800         -197.453            19.999            0.130
  Chain 1:    900         -195.347            17.778            0.018
  Chain 1:   1000         -196.061            16.000            0.018
  Chain 1:   1100         -198.630            15.902            0.016   MAY BE DIVERGING... INSPECT ELBO
  Chain 1:   1200         -195.774             0.188            0.015
  Chain 1:   1300         -196.035             0.176            0.014
  Chain 1:   1400         -195.408             0.011            0.014
  Chain 1:   1500         -196.486             0.010            0.013   MEAN ELBO CONVERGED
  Chain 1: 
  Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
  Chain 1: COMPLETED.
  ── 1. Failure: If it works. (@test-analyze.stanreg.R#89)  ──────────────────────
  round(values$effects$Sepal.Width$median, 2) not equal to -0.12.
  1/1 mismatches
  [1] -0.33 - -0.12 == -0.21
  
  The Crawford-Howell (1998) t-test suggests no dissociation between test X and test Y (t(5) = 2.10, p = 0.09<b0>). The patient's score on test X is not significantly altered compared to its score on test Y.
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000152 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.52 seconds.
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  Chain 1:                1.21603 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 7.6e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.76 seconds.
  Chain 2: Adjust your expectations accordingly!
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  Chain 2:                1.34816 seconds (Total)
  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 7.9e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.79 seconds.
  Chain 3: Adjust your expectations accordingly!
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 7.8e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.78 seconds.
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 4.4e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 1.8e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 1.8e-05 seconds
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 2.7e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
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  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 4.5e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
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  Chain 1: 
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 2.7e-05 seconds
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  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  Chain 3: 
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  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  Chain 4: 
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 2.4e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
  Chain 2: Adjust your expectations accordingly!
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 2.2e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 2e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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  Chain 4: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 6.7e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.67 seconds.
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 2.9e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 3.1e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 3.4e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
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  Chain 4: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000164 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.64 seconds.
  Chain 1: Adjust your expectations accordingly!
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 0.000105 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.05 seconds.
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 0.000119 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.19 seconds.
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 0.000116 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.16 seconds.
  Chain 4: Adjust your expectations accordingly!
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  Chain 4: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 6e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: WARNING: There aren't enough warmup iterations to fit the
  Chain 1:          three stages of adaptation as currently configured.
  Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 1:          the given number of warmup iterations:
  Chain 1:            init_buffer = 7
  Chain 1:            adapt_window = 38
  Chain 1:            term_buffer = 5
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 3.3e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
  Chain 2: Adjust your expectations accordingly!
  Chain 2: 
  Chain 2: 
  Chain 2: WARNING: There aren't enough warmup iterations to fit the
  Chain 2:          three stages of adaptation as currently configured.
  Chain 2:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 2:          the given number of warmup iterations:
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 3.4e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
  Chain 3: Adjust your expectations accordingly!
  Chain 3: 
  Chain 3: 
  Chain 3: WARNING: There aren't enough warmup iterations to fit the
  Chain 3:          three stages of adaptation as currently configured.
  Chain 3:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 3:          the given number of warmup iterations:
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 2.8e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
  Chain 4: Adjust your expectations accordingly!
  Chain 4: 
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  Chain 4:          three stages of adaptation as currently configured.
  Chain 4:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 4:          the given number of warmup iterations:
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  Chain 4: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 7.3e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.73 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: WARNING: There aren't enough warmup iterations to fit the
  Chain 1:          three stages of adaptation as currently configured.
  Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 1:          the given number of warmup iterations:
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  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 3.6e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
  Chain 2: Adjust your expectations accordingly!
  Chain 2: 
  Chain 2: 
  Chain 2: WARNING: There aren't enough warmup iterations to fit the
  Chain 2:          three stages of adaptation as currently configured.
  Chain 2:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 2:          the given number of warmup iterations:
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 3.7e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
  Chain 3: Adjust your expectations accordingly!
  Chain 3: 
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  Chain 3:          three stages of adaptation as currently configured.
  Chain 3:          Reducing each adaptation stage to 15%/75%/10% of
  Chain 3:          the given number of warmup iterations:
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 3.7e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
  Chain 4: Adjust your expectations accordingly!
  Chain 4: 
  Chain 4: 
  Chain 4: WARNING: There aren't enough warmup iterations to fit the
  Chain 4:          three stages of adaptation as currently configured.
  Chain 4:          Reducing each adaptation stage to 15%/75%/10% of
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  Chain 4:  Elapsed Time: 0.168158 seconds (Warm-up)
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  Chain 4:                0.416696 seconds (Total)
  Chain 4: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Chain 1: 
  Chain 1: Gradient evaluation took 5.6e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
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  Chain 1:                0.279791 seconds (Total)
  Chain 1: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  Chain 2: 
  Chain 2: Gradient evaluation took 4e-05 seconds
  Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
  Chain 2: Adjust your expectations accordingly!
  Chain 2: 
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  Chain 2: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  Chain 3: 
  Chain 3: Gradient evaluation took 2.5e-05 seconds
  Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
  Chain 3: Adjust your expectations accordingly!
  Chain 3: 
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  Chain 3: 
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  Chain 4: 
  Chain 4: Gradient evaluation took 2.6e-05 seconds
  Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
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  Chain 4: 
  .0%
  .33%
  .67%
  .100%
  1
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 6.3e-05 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
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  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1:    100         -403.800             1.000            1.000
  Chain 1:    200         -199.756             1.011            1.021
  Chain 1:    300         -195.131             0.682            1.000
  Chain 1:    400         -195.234             0.511            1.000
  Chain 1:    500         -194.906             0.409            0.024
  Chain 1:    600         -195.149             0.341            0.024
  Chain 1:    700         -195.587             0.293            0.002   MEDIAN ELBO CONVERGED
  Chain 1: 
  Chain 1: Drawing a sample of size 1000 from the approximate posterior... 
  Chain 1: COMPLETED.
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 182 SKIPPED: 0 FAILED: 1
  1. Failure: If it works. (@test-analyze.stanreg.R#89) 
  
  Error: testthat unit tests failed
  Execution halted