Machine Learning
Evolutionary testing of classes
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Using evolutionary algorithms for the unit testing of object-oriented software
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Bringing evolutionary computation to industrial applications with guide
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Industrial Scaled Automated Structural Testing with the Evolutionary Testing Tool
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Mutation-driven generation of unit tests and oracles
Proceedings of the 19th international symposium on Software testing and analysis
IEEE Transactions on Software Engineering
Black-box system testing of real-time embedded systems using random and search-based testing
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
Proceedings of the 33rd International Conference on Software Engineering
It is Not the Length That Matters, It is How You Control It
ICST '11 Proceedings of the 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation
A Principled Evaluation of the Effect of Directed Mutation on Search-Based Statistical Testing
ICSTW '11 Proceedings of the 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops
Evolutionary Generation of Whole Test Suites
QSIC '11 Proceedings of the 2011 11th International Conference on Quality Software
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Software Engineering
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Isolating failure causes through test case generation
Proceedings of the 2012 International Symposium on Software Testing and Analysis
A systematic study of automated program repair: fixing 55 out of 105 bugs for $8 each
Proceedings of the 34th International Conference on Software Engineering
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
A search-based framework for failure reproduction
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Test suite generation with memetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 2013 International Conference on Software Engineering
The impact of parameter tuning on software effort estimation using learning machines
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Information Sciences: an International Journal
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When applying search-based software engineering (SBSE) techniques one is confronted with a multitude of different parameters that need to be chosen: Which population size for a genetic algorithm? Which selection mechanism to use? What settings to use for dozens of other parameters? This problem not only troubles users who want to apply SBSE tools in practice, but also researchers performing experimentation - how to compare algorithms that can have different parameter settings? To shed light on the problem of parameters, we performed the largest empirical analysis on parameter tuning in SBSE to date, collecting and statistically analysing data from more than a million experiments. As case study, we chose test data generation, one of the most popular problems in SBSE. Our data confirm that tuning does have a critical impact on algorithmic performance, and over-fitting of parameter tuning is a dire threat to external validity of empirical analyses in SBSE. Based on this large empirical evidence, we give guidelines on how to handle parameter tuning.