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» A Machine Learning Approach for Statistical Software Testing
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GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 2 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
ICIRA
2009
Springer
98views Robotics» more  ICIRA 2009»
14 years 7 months ago
Robot Formations for Area Coverage
Abstract. Two algorithms for area coverage (for use in space applications) were evaluated using a simulator and then tested on a multi-robot society consisting of LEGO Mindstorms r...
Jürgen Leitner
KBSE
2002
IEEE
15 years 2 months ago
What Makes Finite-State Models More (or Less) Testable?
Finite-state machine (FSM) models are commonly used to represent software with concurrent processes. Established model checking tools can be used to automatically test FSM models,...
David Owen, Tim Menzies, Bojan Cukic
CORR
2008
Springer
66views Education» more  CORR 2008»
14 years 10 months ago
A Novel Approach to Formulae Production and Overconfidence Measurement to Reduce Risk in Spreadsheet Modelling
Research on formulae production in spreadsheets has established the practice as high risk yet unrecognised as such by industry. There are numerous software applications that are d...
Simon R. Thorne, David Ball, Zoe Lawson
ICML
2007
IEEE
15 years 10 months ago
Exponentiated gradient algorithms for log-linear structured prediction
Conditional log-linear models are a commonly used method for structured prediction. Efficient learning of parameters in these models is therefore an important problem. This paper ...
Amir Globerson, Terry Koo, Xavier Carreras, Michae...