How do Codecademy's The online Python Tutor interpreter 45 - - PowerPoint PPT Presentation

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How do Codecademy's The online Python Tutor interpreter 45 - - PowerPoint PPT Presentation

Dec 25, 2023 136 likes •775 views

How do Codecademy's The online Python Tutor interpreter 45 million users currently has 60,000 users per month learn to code? input() input() u = 42 x = float(input()) print(x * math.e / 2)

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How do Codecademy's

45 million users

learn to code? The online Python Tutor interpreter currently has 60,000 users per month

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input()

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input()

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u = 42 x = float(input()) print(x * math.e / 2)

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u = 42 x = float(input()) print(x * math.e / 2)

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u = 42 x = float(input()) print(x * math.e / 2)

ValueError: could not convert string to float: '26,2'

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26,2

u = 42 x = float(input()) print(x * math.e / 2)

ValueError: could not convert string to float: '26,2'

29.2

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26,2

ValueError: could not convert string to float: '26,2'

29.2 $1.50

ValueError: could not convert string to float: '$1.50'

1.50 math.py/6

ValueError: could not convert string to float: 'math.py/6'

3.14

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

  • input_b

input_a

  • input_c

input_a

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

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abcd *d%# #*%*d*%

input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

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abcd *d%# #*%*d*%

input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array) KeyError: '#'

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abcd *d%# #*%*d*%

input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array) KeyError: '#'

abcd *d%# abcd

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P

  • P : program code
  • I : token_sequence
  • M : string
  • T : string → Mutation
  • R : set of Mutations
  • N : ℕ
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Error-message template applies? Apply error message template to I Duplicate I? Apply random mutation to I Run P(I) Inputs: program P, buggy input I, error message M Return minimized I, the repaired input! M

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

Current Input Error Message Action abcd *d%# #*%*d*% KeyError: '#'

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

Current Input Error Message Action abcd *d%# #*%*d*% KeyError: '#' Random Mutation: Remove random token abcd *d%#

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

Current Input Error Message Action abcd *d%# #*%*d*% KeyError: '#' Random Mutation: Remove random token abcd *d%# EOFError

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

Current Input Error Message Action abcd *d%# #*%*d*% KeyError: '#' Random Mutation: Remove random token abcd *d%# EOFError Error Message template: Generate new token - either random or from bad input abcd *d%# abcd

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input_a = input() input_b = input() input_c = input() c_array = [] dictionary = {} for i in range(len(input_a)): dictionary[input_a[i]] = input_b[i] for j in range(len(input_c)): c_array += dictionary[input_c[j]] print(c_array)

Current Input Error Message Action abcd *d%# #*%*d*% KeyError: '#' Random Mutation: Remove random token abcd *d%# EOFError Error Message template: Generate new token - either random or from bad input abcd *d%# abcd Success!

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Year Number of Input-Related Bugs 2015 1,640 2016 4,440 2017 6,949 2018 12,723 Total 25,995

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Research Question Evaluation Metric Success Criterion RQ1: How effective is InFix? % Inputs repaired >= 80% (Ahmed et al., 2018) RQ2: How high quality are InFix's repairs? Statement coverage >= 75% (Tillmann et al., 2008) Human subjective assessment of quality >= 75% the quality of human patches (Kim et al., 2013)

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Input-Error Scenarios Probes to Solve Time (sec) Year Total Repaired % Med Avg Med Avg 2015 1,640 1,582 96.5 1 2.98 0.87 1.12 2016 4,440 4,683 94.8 2 3.23 0.88 1.16 2017 6,949 6,590 94.8 2 3.47 0.90 1.23 2018 12,723 11,947 93.9 2 3.70 0.88 1.28 Total 25,995 24,559 94.5% 2 3.50 0.88 1.23

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Input-Error Scenarios Probes to Solve Time (sec) Year Total Repaired % Med Avg Med Avg 2015 1,640 1,582 96.5 1 2.98 0.87 1.12 2016 4,440 4,683 94.8 2 3.23 0.88 1.16 2017 6,949 6,590 94.8 2 3.47 0.90 1.23 2018 12,723 11,947 93.9 2 3.70 0.88 1.28 Total 25,995 24,559 94.5% 2 3.50 0.88 1.23

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Input-Error Scenarios Probes to Solve Time (sec) Year Total Repaired % Med Avg Med Avg 2015 1,640 1,582 96.5 1 2.98 0.87 1.12 2016 4,440 4,683 94.8 2 3.23 0.88 1.16 2017 6,949 6,590 94.8 2 3.47 0.90 1.23 2018 12,723 11,947 93.9 2 3.70 0.88 1.28 Total 25,995 24,559 94.5% 2 3.50 0.88 1.23

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Input-Error Scenarios Probes to Solve Time (sec) Year Total Repaired % Med Avg Med Avg 2015 1,640 1,582 96.5 1 2.98 0.87 1.12 2016 4,440 4,683 94.8 2 3.23 0.88 1.16 2017 6,949 6,590 94.8 2 3.47 0.90 1.23 2018 12,723 11,947 93.9 2 3.70 0.88 1.28 Total 25,995 24,559 94.5% 2 3.50 0.88 1.23

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Input-Error Scenarios Probes to Solve Time (sec) Year Total Repaired %Repaired Med Avg Med Avg 2015 1,640 1,582 96.5 1 2.98 0.87 1.12 2016 4,440 4,683 94.8 2 3.23 0.88 1.16 2017 6,949 6,590 94.8 2 3.47 0.90 1.23 2018 12,723 11,947 93.9 2 3.70 0.88 1.28 Total 25,995 24,559 94.5% 2 3.50 0.88 1.23

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p

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T

ValueError: invalid literal for int() with base 10: 'x' 'x' ValueError: could not convert string to float: 'x' 'x' ValueError: too many / not enough values to unpack EOFError: EOF when reading a line

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R

Insert a token Split delimited list Swap a token Remove a token Empty the input

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  • ValueError

○ ValueError: invalid literal for int ○ ValueError: could not convert string to float ○ ValueError: not enough/too many values to unpack

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Number of Threads 1 2 3 4 5 1 30.8% 36.4% 39.9% 42.6% 44.6% 5 64.1% 72.7% 77.3% 80.3% 82.6% 10 73.6% 81.0% 84.5% 86.7% 88.4% 20 80.5% 86.1% 88.8% 90.6% 91.7% 30 83.1% 88.2% 90.5% 92.0% 93.0% 60 86.7% 91.0% 92.7% 93.8% 94.5% 500 92.5% 94.5% 95.3% 95.8% 96.1% Maximum Number of Probes

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Number of Threads 1 2 3 4 5 1 30.8% 36.4% 39.9% 42.6% 44.6% 5 64.1% 72.7% 77.3% 80.3% 82.6% 10 73.6% 81.0% 84.5% 86.7% 88.4% 20 80.5% 86.1% 88.8% 90.6% 91.7% 30 83.1% 88.2% 90.5% 92.0% 93.0% 60 86.7% 91.0% 92.7% 93.8% 94.5% 500 92.5% 94.5% 95.3% 95.8% 96.1% Maximum Number of Probes

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Number of Threads 1 2 3 4 5 1 30.8% 36.4% 39.9% 42.6% 44.6% 5 64.1% 72.7% 77.3% 80.3% 82.6% 10 73.6% 81.0% 84.5% 86.7% 88.4% 20 80.5% 86.1% 88.8% 90.6% 91.7% 30 83.1% 88.2% 90.5% 92.0% 93.0% 60 86.7% 91.0% 92.7% 93.8% 94.5% 500 92.5% 94.5% 95.3% 95.8% 96.1% Maximum Number of Probes

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○ k = 0.71 Helpfulness of InFix’s repairs depending on experience:

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