Understanding and Controlling Software Costs
IEEE Transactions on Software Engineering
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
IEEE Transactions on Software Engineering - Special issue on formal methods in software practice
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 24th International Conference on Software Engineering
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Ordered Binary Decision Diagrams and the Davis-Putnam Procedure
CCL '94 Proceedings of the First International Conference on Constraints in Computational Logics
Recovering documentation-to-source-code traceability links using latent semantic indexing
Proceedings of the 25th International Conference on Software Engineering
Practical Large Scale What-if Queries: Case Studies with Software Risk Assessment
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Constraint Processing
Data Mining for Very Busy People
Computer
AutoBayes: a system for generating data analysis programs from statistical models
Journal of Functional Programming
Feature Identification: A Novel Approach and a Case Study
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Constructing High Dimensional Feature Space for Time Series Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Tool Support for Parametric Analysis of Large Software Simulation Systems
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Finding robust solutions in requirements models
Automated Software Engineering
Best subset feature selection for massive mixed-type problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A baseline method for search-based software engineering
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
The inductive software engineering manifesto: principles for industrial data mining
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Symbolic execution enhanced system testing
VSTTE'12 Proceedings of the 4th international conference on Verified Software: theories, tools, experiments
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Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points.Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.