This document discusses how machine learning can be applied to various activities in software testing. It describes how machine learning works using training and test data to make predictions. Supervised and unsupervised learning techniques are discussed. Specific applications mentioned include software defect prediction, test planning, test case management, debugging, and refining blackbox test specifications. Challenges include availability of past data and finding predictable patterns, while potential steps forward include expanding machine learning to more blackbox techniques, identifying the right patterns for different test activities, algorithm analysis, and crowdsourcing.

1. MACHINE LEARNING IN SOFTWARE
TESTING
Mithun Kumar S R

2. IDENTIFY THE MOVIE
a machine can actually learn if we communicate with it

3. MACHINE LEARNING
Machine Learning is the study of computer algorithms that
improve automatically through experience
- Tom Mitchell

4. Traditional Programming
Computer
Data
Program
Output
Computer
Data
Machine Learning
Output
Program

5. HOW THIS WORKS
Training
Data
Test Data
Learning
Machine
Analyzed
data for
prediction

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. SOFTWARE TESTING
Critical task in Software development process
Overspend in time and resources
Automation limited to test execution

8. SUPERVISED LEARNING
http://www.astroml.org/sklearn_tutorial/general_concepts.html

9. UNSUPERVISED LEARNING
http://www.astroml.org/sklearn_tutorial/general_concepts.html

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. SOFTWARE TEST ACTIVITIES AND ML
Software defect prediction
Test Planning
Test case management
Debugging

12. BAYESIAN ALGORITHM FOR SOFTWARE DEFECT
PREDICTION

13. CLASSIFICATION
https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.Classification

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. LINEAR REGRESSION – DEFECT DENSITY
http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex2/ex2.html
LOC
DefectDensity

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. 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. AREAS OF APPLICATION
Machine Learning-based Software Testing: Towards a Classification Framework Mahdi Noorian1, Ebrahim Bagheri1,2, and Wheichang Du1

19. CHALLENGES
Past data availability
Predictable pattern

20. STEPS FORWARD
Black Box techniques
Finding the right patterns
Algorithm analysis for different types of test activity
Crowdsourcing

21. DO CONNECT @
MithunKumar.SR@Gmail.Com