Original Contribution: Stacked generalization
Neural Networks
WordNet: a lexical database for English
Communications of the ACM
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Proceedings of the 28th international conference on Software engineering
Issues in stacked generalization
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Towards Automated Anomaly Report Assignment in Large Complex Systems Using Stacked Generalization
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
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Maintenance costs can be substantial for large organizations (several hundreds of programmers) with very large and complex software systems. By large we mean lines of code in the range of hundreds of thousands or millions. Our research objective is to improve the process of handling anomaly reports for large organizations. Specifically, we are addressing the problem of the manual, laborious and time consuming process of assigning anomaly reports to the correct design teams and the related issue of localizing faults in the system architecture. In large organizations, with complex systems, this is particularly problematic because the receiver of an anomaly report may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be assigned to the wrong team in the organization, causing delays and unnecessary work. We have so far developed two machine learning prototypes to validate our approach. The latest, a re-implementation and extension, of the first is being evaluated on four large systems at Ericsson AB. Our main goal is to investigate how large software development organizations can significantly improve development efficiency by replacing manual anomaly report assignment and fault localization with machine learning techniques. Our approach focuses on training machine learning systems on anomaly report databases; this is in contrast to many other approaches that are based on test case execution combined with program sampling and/or source code analysis.