T. Menzies

Publications517
h-index53
Citations11,461
Highly Influential Citations940
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  • Publications
  • Influence
Data Mining Static Code Attributes to Learn Defect Predictors
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On the relative value of cross-company and within-company data for defect prediction
We propose a practical defect prediction approach for companies that do not track defect related data. Specifically, we investigate the applicability of cross-company (CC) data for building localized… Expand
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\{PROMISE\} Repository of empirical software engineering data
  • 560
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The \{PROMISE\} Repository of Software Engineering Databases.
  • 275
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On the value of user preferences in search-based software engineering: A case study in software product lines
Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study… Expand
  • 194
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On the Value of Ensemble Effort Estimation
Background: Despite decades of research, there is no consensus on which software effort estimation methods produce the most accurate models. Aim: Prior work has reported that, given M estimation… Expand
  • 194
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Automated severity assessment of software defect reports
  • T. Menzies, A. Marcus
  • Computer Science
  • IEEE International Conference on Software…
  • 1 September 2008
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. Proper severity… Expand
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Better cross company defect prediction
How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to… Expand
  • 134
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Scalable product line configuration: A straw to break the camel's back
Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents… Expand
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Data Mining Static Code Attributes to Learn Defect Predictors
The value of using static code attributes to learn defect predictors has been widely debated. Prior work has explored issues like the merits of "McCabes versus Halstead versus lines of code counts"… Expand
  • 276
  • 19