An Initial Step Towards Organ Transplantation Based on GitHub Repository

Abstract

Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete one's own program, provides more convenience for developers than traditional component reuse. However, recent techniques are challenged with the lack of organs for transplantation. Hence, we conduct an empirical study on extracting organs from GitHub repository to explore transplantation based on large-scale dataset. We analyze statistics from 12 representative GitHub projects and get the conclusion that 1) there are abundant practical organs existing in commits with "add" as a key word in the comments; 2) organs in this repository mainly possess four kinds of contents; 3) approximately 70% of the organs are easy-to-transplant. Implementing our transplantation strategy for different kinds of organs, we manually extract 30 organs in three different programming languages, namely Java, Python, and C, and make unit tests for them utilizing four testing tools (two for Java, one for Python, and one for C). At last, we transplant three Java organs into a specific platform for a performance check to verify whether they can work well in the new system. All the 30 organs extracted by our strategy possess good performances in unit test with the highest passing rate reaching 97% and the lowest one still passing 80% and the three Java organs work well in the new system, providing three new functionalities for the host. All the results indicate the feasibility of organ transplantation based on open-source repository, bringing new idea for code reuse.

Dissection on Java Organs in GitHub Repositories

“…Also, suppose that another researcher is now designing an automated organ extraction tool, is it suitable to only concentrate on one commit and ignore the correlation between several commits? These two scenarios have not been solved by our previous study [10] and they indicate that the understanding of more in-depth characteristics of organs is of great importance for activities about organ research. That is exactly the motivation for us to conduct an empirical study about the characteristics of organs.…”

“…Due to the time limitation and lack of mature automated techniques, it is unpractical to analyze all the commits. Thus, we follow the keyword-based classification method in our previous work [10] which we talk about in the next, greatly simplifying our work. This drops out totally 19299 commits in our study, about 24% of the total amount of commits in our dataset.…”

“…Thus, commits can be divided into different categories according to the keywords in their comments. To obtain potential organs from commits, our previous study [10] firstly classified commits into eight categories including seven categories with keywords and another category named other which means no keyword is found in the comments of its commits. This intuition coincides perfectly with our definition of Organ as we have introduced in Section II: the code added in one commit is an organ and it achieves the functionality corresponding to its comment.…”

“…In our previous study [10], we made the first attempt to extract and transplant organs from open-source communities, aiming at remedying the problem of lacking of organs. We firstly proposed the idea that mining organs from the evolution process of open-source projects by concentrating on the contents of commits.…”

“…That is exactly the motivation for us to conduct an empirical study about the characteristics of organs. Different from our previous study [10] which can be considered as a preliminary exploration of the feasibility of organ transplantation based on open-source environment, this study concentrates more on the anatomy of the characteristics of organs in open-source environment, aiming at providing theoretical basis for future study. To this end, we selected ten large-scale Java projects with the most stars in GitHub repository and analyzed the distributions, situations, and contents of 23,871 organs from 80,409 commits.…”