Testing in the Age of Machine Learning
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Slides used in Agile Testing Conference hosted by KnowledgeHut in Pune, India in March 2017. The slides talk about the Testing Challenge posed by Machine Learning applications and some suggested approaches to point us in the right direction
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This document discusses using a genetic algorithm to develop a machine learning model for predicting fault-prone software classes. It begins by introducing software reliability and fault prediction. It then explains that a genetic algorithm is a search technique that evaluates potential solutions, keeps the best ones, and generates new solutions iteratively. The algorithm uses software metrics like coupling, cohesion, inheritance, and size as inputs to classify classes as faulty or fault-free with 80.14% accuracy, helping to identify areas for improvement.
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- 1. @vinaya1980 @hrishikarekar TESTING IN THE AGE OF MACHINE LEARNING March 2016
- 2. https://www.youtube.com/watch?v=DCgHsxISE0Q W H E R E A R E W E H E A D E D httpsweforum.org/agenda/2017/01/worried- about-ai-taking-your-job-its-already-happening-in- japan?utm_content=buffera14af&utm_medium=s ocial&utm_source=twitter.com&utm_campaign=b uffer
- 3. THE WORLD OF ARTIFICIAL INTELLIGENCE NLP Neural Networks MACHINE LEARNING DEEP LEARNING …..
- 4. THE FUTURE IS ALREADY HERE Google RankBrain Assistants
- 5. HOW DOES IT WORK Supervised • Labelled data • Given new data, predict outcome • Classification Unsupervised • No labels • Find hidden structures • Clustering Reinforcement • Decision process • Actions are rewarded or punished • Learns to optimize rewards
- 7. SUPERVISED LEARNING - CLASSIFCATION http://www.mikedeff.in/MLIntro.PNG
- 8. INTO THE REALM OF PROBABILITIES Y = f ( x ) Y ≈ f ( x ) What is scrum ? { "Prediction": { "details": { "Algorithm": "SGD", "PredictiveModelType": "MULTICLASS" }, "predictedLabel": "definition", "predictedScores": { "advantages": 0.0001860455668065697, "characteristics": 0.00006915141420904547, "compare": 0.00017757616296876222, "definition": 0.9970965385437012, "disadvantages": 0.0000534967657586094, } } } Can you tell me about scrum ? { "Prediction": { "details": { "Algorithm": "SGD", "PredictiveModelType": "MULTICLASS" }, "predictedLabel": "definition", "predictedScores": { "advantages": 0.01977257989346981, "characteristics": 0.022757112979888916, "compare": 0.008386141620576382, "definition": 0.21092116832733154, "disadvantages": 0.04002799838781357 } } }
- 9. TOLERANCE LEVELS Y ≈ f ( x ) Know the probability that is within acceptable limits
- 10. EVALUATE WITH DIFFERENT MODELS Evaluate against a set of algorithms to iterate towards a model that’s closest representation and for further tuning https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png?__s=h4reg8jqwyg4sz3bzdqf
- 11. EVALUATION – DATA SET APPROACHES Random split • 70% train, 30% test K-fold cross validation Split into 3 datasets • #1 Train on 1 and 2, test on 3 • #2 Train on 2 and 3, test on 1 • #3 Train on 1 and 3, test on 2 Never use the same dataset for training and evaluating
- 12. ITERATIVE – LEARNING PROCESS Be prepared to throw the model and start again
- 13. MODEL ACCURACY - CONFUSION MATRIX Strive for better models, not 100% accurate.
- 14. OVERFITTING
- 15. MODEL IS AS GOOD AS THE TRAINING DATA If all of the algorithms perform poorly, • it maybe worth considering if there is a lack of learning structure in the data set • some transformation needed to make the structure more learnable • remove unnecessary noise - stop words are typically removed because they cause unnecessary noise)
- 16. SUMMARY Machine Learning applications demand a shift in testing approach • Use objective acceptance levels to evaluate the application • Express test outcomes in statistical terms • Have a high level understanding of the underlying working of the application