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Microsoft Office users submit hundreds of thousands of pieces of verbatim feedback per month. How can an engineer or manager in Office find the signal in this data to make business decisions? This paper presents an overview of the Office Customer Voice (OCV) system. OCV combines classification, on-demand clustering and other machine learning techniques with a rich web UI to solve this problem. In...
Software Analytics is gaining momentum as aresult of involved empirical research in enhancing quality andproductivity of software engineering activities. There have beenrigorous research efforts in the areas of bug prediction and testingeffort prediction by making use of historical data. The problemof predicting bug fix times is an interesting problem with lotsof advantages to industry but there have...
The emerging new data types bring tremendous challenges to data mining. There is an enormous amount of high-dimensional class-imbalanced data in different fields. In this case, traditional classification methods are not appropriate because they are prone to ensure the accuracy of the majority class. Meanwhile, the curse of dimensionality makes situations more complicated. Finding a complicated classifier...
Vulnerability issues are important for any system, therefore, many testing approaches have been proposed so far. In social networking communities, there are lot of public pages and components where people might easily get access of the system inside. It is crucial to analyze the data from web applications specially data from the social networking websites to provide security to the users of the system...
Reducing the number of latent software defects is a development goal that is particularly applicable to high assurance software systems. For such systems, the software measurement and defect data is highly skewed toward the not-fault-prone program modules, i.e., the number of fault-prone modules is relatively very small. The skewed data problem, also known as class imbalance, poses a unique challenge...
Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers...
Most of the existing object-oriented design metrics and data mining techniques capture similar dimensions in the data sets, thus reflecting the fact that many of the metrics are based on similar hypotheses, properties, and principles. Accurate quality models can be built to predict the quality of object-oriented systems by using a subset of the existing object-oriented design metrics and data mining...
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