K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The ASTOOT approach to testing object-oriented programs
ACM Transactions on Software Engineering and Methodology (TOSEM)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Automatically Checking an Implementation against Its Formal Specification
IEEE Transactions on Software Engineering
QuickCheck: a lightweight tool for random testing of Haskell programs
ICFP '00 Proceedings of the fifth ACM SIGPLAN international conference on Functional programming
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Recovering documentation-to-source-code traceability links using latent semantic indexing
Proceedings of the 25th International Conference on Software Engineering
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Evolutionary test data generation: a comparison of fitness functions: Research Articles
Software—Practice & Experience
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Test input generation for java containers using state matching
Proceedings of the 2006 international symposium on Software testing and analysis
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Feedback-Directed Random Test Generation
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Nighthawk: a two-level genetic-random unit test data generator
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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The next challenge for the PROMISE community is scaling up and speeding up model generation to meet the size and time constraints of modern software development projects. There will always be a trade-off between completeness and runtime speed. Here we explore that trade-off in the context of using genetic algorithms to learn coverage models; i.e. biases in the control structures for randomized test generators. After applying feature subset selection to logs of the GA output, we find we can generate the coverage model and run the resulting test suite ten times faster while only losing 6% of the test case coverage.