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Towards Automated Anomaly Report Assignment in Large Complex Systems using Stacked Generalization
Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
Karlstads Universitet, Sweden.
2012 (English)In: Software Testing, Verification and Validation (ICST), 2012, IEEE , 2012, p. 437-446Conference paper, Published paper (Refereed)
Abstract [en]

Maintenance costs can be substantial for organizations with very large and complex software systems. This paper describes research for reducing anomaly report turnaround time which, if successful, would contribute to reducing maintenance costs and at the same time maintaining a good customer perception. Specifically, we are addressing the problem of the manual, laborious, and inaccurate process of assigning anomaly reports to the correct design teams. In large organizations with complex systems this is particularly problematic because the receiver of the anomaly report from customer may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be wrongly routed around in the organization causing delays and unnecessary work. We have developed and validated machine learning approach, based on stacked generalization, to automatically route anomaly reports to the correct design teams in the organization. A research prototype has been implemented and evaluated on roughly one year of real anomaly reports on a large and complex system at Ericsson AB. The prediction accuracy of the automation is approaching that of humans, indicating that the anomaly report handling time could be significantly reduced by using our approach.

Place, publisher, year, edition, pages
IEEE , 2012. p. 437-446
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-93231DOI: 10.1109/ICST.2012.124ISBN: 978-1-4577-1906-6 (print)OAI: oai:DiVA.org:liu-93231DiVA, id: diva2:623540
Conference
Fifth IEEE International Conference on Software Testing, Verification and Validation (ICST 2012), 17-21 April 2012, Montreal, QC, Canada
Note

Finansierat av Ericsson AB

Available from: 2013-05-27 Created: 2013-05-27 Last updated: 2018-05-17Bibliographically approved
In thesis
1. Machine Learning-Based Bug Handling in Large-Scale Software Development
Open this publication in new window or tab >>Machine Learning-Based Bug Handling in Large-Scale Software Development
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations.

The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 120
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1936
Keyword
machine learning, bug reports, large scale software development
National Category
Engineering and Technology Software Engineering
Identifiers
urn:nbn:se:liu:diva-147059 (URN)10.3384/diss.diva-147059 (DOI)9789176853061 (ISBN)
Public defence
2018-06-12, Ada Lovelace, Hus B, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-05-17Bibliographically approved

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Broman, DavidSandahl, Kristian

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