Empirically Detecting False Test Alarms Using Association Rules

@article{Herzig2015EmpiricallyDF,
  title={Empirically Detecting False Test Alarms Using Association Rules},
  author={Kim Herzig and Nachiappan Nagappan},
  journal={2015 IEEE/ACM 37th IEEE International Conference on Software Engineering},
  year={2015},
  volume={2},
  pages={39-48}
}
Applying code changes to software systems and testing these code changes can be a complex task that involves many different types of software testing strategies, e.g. system and integration tests. However, not all test failures reported during code integration are hinting towards code defects. Testing large systems such as the Microsoft Windows operating system requires complex test infrastructures, which may lead to test failures caused by faulty tests and test infrastructure issues. Such… CONTINUE READING
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Key Quantitative Results

  • Performing more than 10,000 classifications for each product, our model shows a mean precision between 0.85 and 0.90 predicting between 34% and 48% of all false test alarms.

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