Paper Info
Title
Generating structured test data with specific properties using nested Monte-Carlo search.
Abstract
Software acting on complex data structures can be challenging to test: it is di ffi cult to generate diverse test data that satis fi es structural constraints while simultaneously exhibiting properties, such as a particular size, that the test engineer believes will be e ff ective in detecting faults. In our previous work we introduced GodelTest, a framework for generating such data structures using non-deterministic programs, and combined it with Di ff erential Evolution to optimize the generation process.Monte-Carlo Tree Search (MCTS) is a search technique that has shown great success in playing games that can be represented as a sequence of decisions. In this paper we apply Nested Monte-Carlo Search, a single-player variant of MCTS, to the sequence of decisions made by the generating programs used by GodelTest, and show that this combination can e ffi ciently generate random data structures which exhibit the speci fi c properties that the test engineer requires. We compare the results to Boltzmann sampling, an analytical approach to generating random combinatorial data structures.
Year
DOI
Venue
2014
10.1145/2576768.2598339
GECCO
Keywords
Field
DocType
natural sciences,software engineering,computer science,computer and information science,search based software engineering,data structures,software testing
Computer science,Theoretical computer science,Test engineer,Software,Artificial intelligence,Data structure,Monte Carlo method,Algorithm,Differential evolution,Test data,Machine learning,Test data generation,Search-based software engineering
Conference
Citations 
PageRank 
References 
9
0.52
9
Authors
2
Name
Order
Citations
PageRank
Simon M. Poulding113610.72
Robert Feldt2965.77