We are trying to find source code comments that help programmers understand a nontrivial part of source code. One of such examples would be explaining to assign a zero as a way to "clear" a buffer. Such comments are invaluable to programmers and identifying them correctly would be of great help. Toward this goal, we developed a method to discover explanatory code comments in a source code. We first propose eleven distinct categories of code comments. We then developed a decision-tree based classifier that can identify explanatory comments with 60% precision and 80% recall. We analyzed 2,000 GitHub projects that are written in two languages: Java and Python. This task is novel in that it focuses on a microscopic comment ("local comment") within a method or function, in contrast to the prior efforts that focused on API-or methodlevel comments. We also investigated how different category of comments is used in different projects. Our key finding is that there are two dominant types of comments: preconditional and postconditional. Our findings also suggest that many English code comments have a certain grammatical structure that are consistent across different projects.
It is useful to employ electronic medical records to improve medical studies. Based on their experience, medical workers conventionally prepare clinical pathways as guidelines for the typical flow for the medical treatment of each disease. In this study, we propose an approach for verifying existing clinical pathways and recommend variants or new pathways by analyzing historical records. We propose a method based on the application of sequential pattern mining to record logs with handling time intervals between treatments. We also focus on the efficacy of medicines instead of their names because various medicines have the same efficacy and they change dynamically. We evaluated the proposed method using actual logs and the results demonstrated that the proposed method is effective.
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