Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Test input generation for java containers using state matching
Proceedings of the 2006 international symposium on Software testing and analysis
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
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
Formal analysis of the effectiveness and predictability of random testing
Proceedings of the 19th international symposium on Software testing and analysis
Adaptation-based programming in java
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
Testing container classes: random or systematic?
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Online testing with reinforcement learning
FATES'06/RV'06 Proceedings of the First combined international conference on Formal Approaches to Software Testing and Runtime Verification
Finding common ground: choose, assert, and assume
Proceedings of the 2012 Workshop on Dynamic Analysis
Learning-Based test programming for programmers
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
Comparing non-adequate test suites using coverage criteria
Proceedings of the 2013 International Symposium on Software Testing and Analysis
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This paper introduces a new approach to test input generation, based on reinforcement learning via easy to use adaptation-based programming. In this approach, a test harness can be written with little more effort than is involved in naïve random testing. The harness will simply map choices made by the adaptation-based programming (ABP) library, rather than pseudo-random numbers, into operations and parameters. Realistic experimental evaluation over three important fine-grained coverage measures (path, shape, and predicate coverage) shows that ABP-based testing is typically competitive with, and sometimes superior to, other effective methods for testing container classes, including random testing and shape-based abstraction.