Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub

Hosting over 10 million of software projects, GitHub is one of the most important data sources to study behavior of developers and software projects. However, with the increase of the size of open source datasets, the potential threats to mining these datasets have also grown. As the dataset grows, it becomes gradually unrealistic for human to confirm quality of all samples. Some studies have investigated this problem and provided solutions to avoid threats in sample selection, but some of these solutions (e.g., finding development projects) require human intervention. When the amount of data to be processed increases, these semi-automatic solutions become less useful since the effort in need for human intervention is far beyond affordable. To solve this problem, we investigated the GHTorrent dataset and proposed a method to detect public development projects. The results show that our method can effectively improve the sample selection process in two ways: (1) We provide a simple model to automatically select samples (with 0.827 precision and 0.947 recall); (2) We also offer a complex model to help researchers carefully screen samples (with 63.2% less effort than manually confirming all samples, and can achieve 0.926 precision and 0.959 recall).Comment: Accepted by the SEKE2018 Conferenc

Directed search and job rotation

In this note, we consider the impact of job rotation in a directed search model in which firm sizes are endogenously determined, and match quality is initially unknown. A large firm benefits from the opportunity of rotating workers so as to partially overcome mismatch loss. As a result, in the unique symmetric subgame perfect equilibrium, large firms have higher labor productivity and lower separation rate. In contrast to the standard directed search model with multi-vacancy firms, this model can generate a positive correlation between firm size and wage without introducing any exogenous productivity shock or imposing non-concave production function assumption.Directed Search, Job Rotation, Firm Size and Wage, Firm Size and Labor Productivity

Flight to Quality for Large Financial Institutions

Local correlation analysis is used to investigate flight to quality among large financial institutions before, during, and after the financial crisis of 2008-2009. While standard correlation captures general overall linear association, local correlation analysis more accurately captures changes in the associations in response to changing market conditions. Using raw, market-adjusted, and industry-adjusted stock returns of individual banks, we investigate the performance of troubled banks and the change in investing behavior. Investors react to noisy information from the financial difficulties encountered by banking institutions. This reaction results in flight to quality. While the traditional Pearson correlations capture general overall linear association, local correlation analysis captures changes in the association in response to changing market conditions. Thus, local correlation analysis more accurately measures changes in correlation where it matters most: in the loss tail of the distribution of financial returns; leading to more appropriate diversification, portfolio management, and within-industry implications