Standard software analytics often involves having a large amount of data with labels in order
to commission models with acceptable performance. However, prior work has shown that …

Automatically generated static code warnings suffer from a large number of false alarms.
Hence, developers only take action on a small percent of those warnings. To better predict …

Labeling a module defective or non-defective is an expensive task. Hence, there are often
limits on how much-labeled data is available for training. Semi-supervised classifiers use far …

When data is scarce, software analytics can make many mistakes. For example, consider
learning predictors for open source project health (eg, the number of closed pull requests in …

For simulation-based systems, finding a set of test cases with the least cost by exploring
multiple goals is a complex task. Domain-specific optimization goals (eg, maximize output …

Synthetic data has been widely applied in the real world recently. One typical example is the
creation of synthetic data for privacy concerned datasets. In this scenario, synthetic data …

Software developed on public platform is a source of data that can be used to make
predictions about those projects. While the individual developing activity may be random …

This paper proposes a new challenge problem for software analytics. In the process we shall
call" software review", a panel of SMEs (subject matter experts) review examples of software …

Detecting performance anomalies is key to efficiently utilize network resources and improve
the quality of service. Researchers proposed various approaches to identify the presence of …

Testing complex simulation models can be expensive and time consuming. Current state-of-
the-art methods that explore this problem are fully-supervised; ie they require that all …