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MACHINE LEARNING IN SOFTWARE
TESTING
Mithun Kumar S R
IDENTIFY THE MOVIE
a machine can actually learn if we communicate with it
MACHINE LEARNING
Machine Learning is the study of computer algorithms that
improve automatically through experience
- Tom ...
Traditional Programming
Computer
Data
Program
Output
Computer
Data
Machine Learning
Output
Program
HOW THIS WORKS
Training
Data
Test Data
Learning
Machine
Analyzed
data for
prediction
SOFTWARE TEST LIFE CYCLE
Pre-execution
• Test planning
• Code Review
• Test case
management
Execution
• Automated run
• De...
SOFTWARE TESTING
Critical task in Software development process
Overspend in time and resources
Automation limited to test ...
SUPERVISED LEARNING
http://www.astroml.org/sklearn_tutorial/general_concepts.html
UNSUPERVISED LEARNING
http://www.astroml.org/sklearn_tutorial/general_concepts.html
SOFTWARE TEST LIFE CYCLE
Pre-execution
• Test planning
• Code Review
• Test case
management
Execution
• Automated run
• De...
SOFTWARE TEST ACTIVITIES AND ML
Software defect prediction
Test Planning
Test case management
Debugging
BAYESIAN ALGORITHM FOR SOFTWARE DEFECT
PREDICTION
CLASSIFICATION
https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.Classification
NAÏVE BAYES ALGO
Branch Count LOC Defective
5 15 No
3 5 No
9 20 No
15 40 Yes
16 35 Yes
Branch Count = 16 LOC = 39
C = No -...
LINEAR REGRESSION – DEFECT DENSITY
http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&do...
TEST PLANNING
Database formation
Data collection
Classification of software
Analyzing the results
Test Cost prediction
Tho...
MELBA – MACHINE LEARNING BASED
REFINEMENT OF BLACKBOX TEST SPECIFICATION
Lionel C. Briand. Novel applications of machine l...
AREAS OF APPLICATION
Machine Learning-based Software Testing: Towards a Classification Framework Mahdi Noorian1, Ebrahim B...
CHALLENGES
Past data availability
Predictable pattern
STEPS FORWARD
Black Box techniques
Finding the right patterns
Algorithm analysis for different types of test activity
Crow...
DO CONNECT @
MithunKumar.SR@Gmail.Com
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Machine learning in software testing

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Machine Learning in Software Testing by Mithun S R

Published in: Engineering, Software, Technology
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Machine learning in software testing

  1. 1. MACHINE LEARNING IN SOFTWARE TESTING Mithun Kumar S R
  2. 2. IDENTIFY THE MOVIE a machine can actually learn if we communicate with it
  3. 3. MACHINE LEARNING Machine Learning is the study of computer algorithms that improve automatically through experience - Tom Mitchell
  4. 4. Traditional Programming Computer Data Program Output Computer Data Machine Learning Output Program
  5. 5. HOW THIS WORKS Training Data Test Data Learning Machine Analyzed data for prediction
  6. 6. SOFTWARE TEST LIFE CYCLE Pre-execution • Test planning • Code Review • Test case management Execution • Automated run • Defect analysis Post- execution • Debugging • Regression suite update
  7. 7. SOFTWARE TESTING Critical task in Software development process Overspend in time and resources Automation limited to test execution
  8. 8. SUPERVISED LEARNING http://www.astroml.org/sklearn_tutorial/general_concepts.html
  9. 9. UNSUPERVISED LEARNING http://www.astroml.org/sklearn_tutorial/general_concepts.html
  10. 10. SOFTWARE TEST LIFE CYCLE Pre-execution • Test planning • Code Review • Test case management Execution • Automated run • Defect analysis Post- execution • Debugging • Regression suite update
  11. 11. SOFTWARE TEST ACTIVITIES AND ML Software defect prediction Test Planning Test case management Debugging
  12. 12. BAYESIAN ALGORITHM FOR SOFTWARE DEFECT PREDICTION
  13. 13. CLASSIFICATION https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.Classification
  14. 14. NAÏVE BAYES ALGO Branch Count LOC Defective 5 15 No 3 5 No 9 20 No 15 40 Yes 16 35 Yes Branch Count = 16 LOC = 39 C = No -> 0.000000912 C = Yes -> 0.0181 Leandru Minku: Automated Software Defect Prediction Using Machine Learning
  15. 15. LINEAR REGRESSION – DEFECT DENSITY http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex2/ex2.html LOC DefectDensity
  16. 16. TEST PLANNING Database formation Data collection Classification of software Analyzing the results Test Cost prediction Thomas J. Cheatham, Jungsoon P. Yoo, and Nancy J. Wahl. Software testing: a machine learning experiment. Complexity Cost
  17. 17. MELBA – MACHINE LEARNING BASED REFINEMENT OF BLACKBOX TEST SPECIFICATION Lionel C. Briand. Novel applications of machine learning in software testing. Quality Software, International Conference on, 0:3–10, 2008.
  18. 18. AREAS OF APPLICATION Machine Learning-based Software Testing: Towards a Classification Framework Mahdi Noorian1, Ebrahim Bagheri1,2, and Wheichang Du1
  19. 19. CHALLENGES Past data availability Predictable pattern
  20. 20. STEPS FORWARD Black Box techniques Finding the right patterns Algorithm analysis for different types of test activity Crowdsourcing
  21. 21. DO CONNECT @ MithunKumar.SR@Gmail.Com

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