T. Menzies

Publications591
h-index57
Citations13,143
Highly Influential Citations1,054
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  • Publications
  • Influence
Data Mining Static Code Attributes to Learn Defect Predictors
  • 944
  • 160
On the relative value of cross-company and within-company data for defect prediction
TLDR
We demonstrate in this paper that the minimum number of data samples required to build effective defect predictors can be quite small and can be collected quickly within a few months. Expand
  • 448
  • 77
  • PDF
\{PROMISE\} Repository of empirical software engineering data
  • 597
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The \{PROMISE\} Repository of Software Engineering Databases.
  • 308
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On the value of user preferences in search-based software engineering: A case study in software product lines
TLDR
We find that search-based software engineering methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). Expand
  • 200
  • 27
  • PDF
Automated severity assessment of software defect reports
  • T. Menzies, A. Marcus
  • Computer Science
  • IEEE International Conference on Software…
  • 1 September 2008
TLDR
In mission critical systems, such as those developed by NASA, it is very important that the test engineers properly recognize the severity of each issue they identify during testing. Expand
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  • PDF
On the Value of Ensemble Effort Estimation
TLDR
We find that if we combine the estimates from multiple estimators, then those combined methods perform better than any single estimator. Expand
  • 201
  • 24
  • PDF
Better cross company defect prediction
TLDR
This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Expand
  • 147
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  • PDF
Scalable product line configuration: A straw to break the camel's back
TLDR
This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. Expand
  • 130
  • 22
  • PDF
Automatic query reformulations for text retrieval in software engineering
TLDR
We propose a recommender (called Refoqus) based on machine learning, which is trained with a sample of queries and relevant results, based on the properties of the query. Expand
  • 170
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