We propose a type inference algorithm for each of these type systems that can decide without type information if an expression is typable or not. Many large-scale software projects make use of issue tracking tools in order to manage problems that emerge during development. An issue report may document a problem, which is one of a fault, error, bug or a request for an additional feature. It often details in a formal manner the cause of the problem, its impact, and suggested fixes. A PITS issue report resides in the central repository and contains a detailed description of the detected problem, its impact on the other phases of the project, related issue reports, along with all the resolution entries in chronological order that are the result of issue resolving process .
PITS acts as a convenient tool for data and metrics collection.
Continuous analysis on an issue repository can help control problems early in the development life-cycle and can even help in estimating the approximate date of the next release of the software. For example, when the number of fixed defects exceeds the number of open defects in that period of time, the project can be said to be making progress towards completion. The minimum, maximum and average number of days needed to close an open issue would prove to be very important measure to monitor the progress of the ongoing project. A continuous regression analysis on the selected issue reports in the current PITS repository indicates that the time to close an open issue is significantly influenced by criticality of the issue, number of relations to other issue reports, type of impact category and the number of resolution chronology entries. The goal of our research is to build a sophisticated model to carry out the analysis that could determine the significant factors having impact on the overall progress of the project.