Debugging Flashcards

(19 cards)

1
Q

conventional testing

A

code first, test second

enforced by waterfall, V-model

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2
Q

conventional testing problems

A
  • testing is late in development cycle
  • testing not integrated into development
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3
Q

static checking tools

A

used to find
* using undefined variables
* unreachable/dead code
* resource leaks
* infinite loops

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4
Q

white box testing

A

uses knowledge of the code

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5
Q

black box testing

A

focuses on inputs and outputs

only good if adequately covers the input

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6
Q

test-driven development

A

test are made before any development and code is written to make the tests pass

only write new code if an automated test has failed

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7
Q

dijkstra

A

program testing can be used to show the presense of defects but never their absence

generating tests to cover every possible input is not feasible

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8
Q

tdd flow

A
  1. define what solution should do
  2. then work out how to do it
  3. add tests as part of development process
  4. repeat tests for each time code is changed
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9
Q

unit testing tools

A
  • junit
  • cppunit
  • phpunit
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10
Q

property based testing

A
  • focus on invariant properties of your code
  • forces you to capture these invariants explicitly

originally made for Haskell

specify rules that should always hold about a system and the test framework automatically generates inputs to verify those properties

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11
Q

property based testing benefits

A
  • find tricky corner cases
  • find causes for flickering tests
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12
Q

ai systems: drift

A

bias that built the model is misaligned with current data

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13
Q

bias

A

systematic difference in treatment of certain objects people or groups in comparison to others

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14
Q

automation bias

A

propensity for humans to favour suggestions from automated decision-making systems and to ignore contradictory information made without automation even if it is correct

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15
Q

human-congnitive bias

A

bias that occurs when humans are processing and interpreting information and influences judgement and decision making

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16
Q

confirmation bias

A

type of human cognitive bias that favours predicitons of ai systems that confirm pre-existing beliefs or hypothesis

17
Q

data bias

A

data properties that if unaddressed lead to ai systems that perform better or worse for different groups

18
Q

statistical bias

A

type of consistent numerical offset in an estimate relative to the true underlying value inherent to most estimates