Elements of information theory
Elements of information theory
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Proceedings of the Conference on The Future of Software Engineering
Proceedings of the Conference on The Future of Software Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Bayesian Graphical Models for Software Testing
IEEE Transactions on Software Engineering
Machine Learning and Software Engineering
Software Quality Control
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Latent Code Errors via Machine Learning over Program Executions
Proceedings of the 26th International Conference on Software Engineering
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
The challenges of software engineering education
Proceedings of the 27th international conference on Software engineering
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Generating a Test Strategy with Bayesian Networks and Common Sense
TAIC-PART '06 Proceedings of the Testing: Academic & Industrial Conference on Practice And Research Techniques
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Using Machine Learning to Support Debugging with Tarantula
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Structured machine learning: the next ten years
Machine Learning
On the effectiveness of early life cycle defect prediction with Bayesian Nets
Empirical Software Engineering
Novel Applications of Machine Learning in Software Testing
QSIC '08 Proceedings of the 2008 The Eighth International Conference on Quality Software
A Model Building Process for Identifying Actionable Static Analysis Alerts
ICST '09 Proceedings of the 2009 International Conference on Software Testing Verification and Validation
The road not taken: Estimating path execution frequency statically
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Software, software engineering and software engineering research: some unconventional thoughts
Journal of Computer Science and Technology
Planning and acting in partially observable stochastic domains
Artificial Intelligence
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
A prioritization approach for software test cases based on Bayesian networks
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Learning universal probabilistic models for fault localization
Proceedings of the 9th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Bayesian methods for data analysis in software engineering
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Causal inference for statistical fault localization
Proceedings of the 19th international symposium on Software testing and analysis
Prioritizing Mutation Operators Based on Importance Sampling
ISSRE '10 Proceedings of the 2010 IEEE 21st International Symposium on Software Reliability Engineering
A learning-based method for combining testing techniques
Proceedings of the 2013 International Conference on Software Engineering
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Despite significant advances in software testing research, the ability to produce reliable software products for a variety of critical applications remains an open problem. The key challenge has been the fact that each program or software product is unique, and existing methods are predominantly not capable of adapting to the observations made during program analysis. This paper makes the following claim: Bayesian reasoning methods provide an ideal research paradigm for achieving reliable and efficient software testing and program analysis. A brief overview of some popular Bayesian reasoning methods is provided, along with a justification of why they are applicable to software testing. Furthermore, some practical challenges to the widespread use of Bayesian methods are discussed, along with possible solutions to these challenges.