@inbook {41,
	title = {Repairing and Optimizing Hadoop hashCode Implementations},
	booktitle = {Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings},
	year = {2014},
	pages = {259{\textendash}264},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {We describe how contract violations in Java TM hashCode methods can be repaired using novel combination of semantics-preserving and generative methods, the latter being achieved via Automatic Improvement Programming. The method described is universally applicable. When applied to the Hadoop platform, it was established that it produces hashCode functions that are at least as good as the original, broken method as well as those produced by a widely-used alternative method from the {\textquoteleft}Apache Commons{\textquoteright} library.},
	isbn = {978-3-319-09940-8},
	doi = {10.1007/978-3-319-09940-8_22},
	url = {http://dx.doi.org/10.1007/978-3-319-09940-8_22},
	author = {Kocsis, Zoltan A. and Neumann, Geoff and Swan, Jerry and Epitropakis, Michael G. and Brownlee, Alexander E. I. and Haraldsson, Sami O. and Bowles, Edward},
	editor = {Le Goues, Claire and Yoo, Shin}
}
@article {15,
	title = {Root finding and approximation approaches through neural networks},
	journal = {ACM SIGSAM Bulleting},
	volume = {39},
	year = {2005},
	pages = {118-121},
	abstract = {In this paper, we propose two approaches to approximate high order multivariate polynomials and to estimate the number of roots of high order univariate polynomials. We employ high order neural networks such as Ridge Polynomial Networks and Pi -- Sigma Networks, respectively. To train the networks efficiently and effectively, we recommend the application of stochastic global optimization techniques. Finally, we propose a two step neural network based technique, to estimate the number of roots of a high order univariate polynomial.
},
	issn = {0163-5824},
	doi = {http://doi.acm.org/10.1145/1140378.1140382},
	author = {M. G. Epitropakis and M. N. Vrahatis}
}
