Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Empirical Studies of a Safe Regression Test Selection Technique
IEEE Transactions on Software Engineering
Making large-scale support vector machine learning practical
Advances in kernel methods
Quickly detecting relevant program invariants
Proceedings of the 22nd international conference on Software engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An empirical study of regression test selection techniques
ACM Transactions on Software Engineering and Methodology (TOSEM)
Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
Finding failures by cluster analysis of execution profiles
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Information Retrieval
Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
Using redundancies to find errors
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
Improving test suites via operational abstraction
Proceedings of the 25th International Conference on Software Engineering
Automated support for classifying software failure reports
Proceedings of the 25th International Conference on Software Engineering
Tree Induction for Probability-Based Ranking
Machine Learning
Automated Support for Program Refactoring using Invariants
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Empirical Evaluation of the Textual Differencing Regression Testing Technique
ICSM '98 Proceedings of the International Conference on Software Maintenance
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Reducing wasted development time via continuous testing
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
What went wrong: explaining counterexamples
SPIN'03 Proceedings of the 10th international conference on Model checking software
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Efficient incremental algorithms for dynamic detection of likely invariants
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
LearnLib: a library for automata learning and experimentation
Proceedings of the 10th international workshop on Formal methods for industrial critical systems
Applying classification techniques to remotely-collected program execution data
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
SOBER: statistical model-based bug localization
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Improved error reporting for software that uses black-box components
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
The Future of Programming Environments: Integration, Synergy, and Assistance
FOSE '07 2007 Future of Software Engineering
Context-aware statistical debugging: from bug predictors to faulty control flow paths
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
The Daikon system for dynamic detection of likely invariants
Science of Computer Programming
Predicting buggy changes inside an integrated development environment
Proceedings of the 2007 OOPSLA workshop on eclipse technology eXchange
A Learning Approach to Early Bug Prediction in Deployed Software
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Profile-guided program simplification for effective testing and analysis
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
A defect prediction method for software versioning
Software Quality Control
Algorithms for Automatically Computing the Causal Paths of Failures
FASE '09 Proceedings of the 12th International Conference on Fundamental Approaches to Software Engineering: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
Empirical Evaluation of Hunk Metrics as Bug Predictors
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Dynamic testing via automata learning
HVC'07 Proceedings of the 3rd international Haifa verification conference on Hardware and software: verification and testing
JRF-E: using model checking to give advice on eliminating memory model-related bugs
Proceedings of the IEEE/ACM international conference on Automated software engineering
Combining hardware and software instrumentation to classify program executions
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Memory indexing: canonicalizing addresses across executions
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Bayesian reasoning for software testing
Proceedings of the FSE/SDP workshop on Future of software engineering research
Generating models of infinite-state communication protocols using regular inference with abstraction
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
Cost-Sensitive decision tree learning for forensic classification
ECML'06 Proceedings of the 17th European conference on Machine Learning
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Eclat: automatic generation and classification of test inputs
ECOOP'05 Proceedings of the 19th European conference on Object-Oriented Programming
LearnLib: a library for automata learning and experimentation
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
iTree: efficiently discovering high-coverage configurations using interaction trees
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Recommender systems for manual testing: deciding how to assign tests in a test team
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems
International Journal of Adaptive, Resilient and Autonomic Systems
Is this a bug or an obsolete test?
ECOOP'13 Proceedings of the 27th European conference on Object-Oriented Programming
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This paper proposes a technique for identifying programproperties that indicate errors. The technique generates machinelearning models of program properties known to resultfrom errors, and applies these models to program propertiesof user-written code to classify and rank propertiesthat may lead the user to errors. Given a set of propertiesproduced by the program analysis, the technique selectssubset of properties that are most likely to reveal an error.An implementation, the Fault Invariant Classifier,demonstrates the efficacy of the technique. The implementationuses dynamic invariant detection to generate programproperties. It uses support vector machine and decision treelearning tools to classify those properties. In our experimentalevaluation, the technique increases the relevance(the concentration of fault-revealing properties) by a factorof 50 on average for the C programs, and 4.8 for the Javaprograms. Preliminary experience suggests that most of thefault-revealing properties do lead a programmer to an error.