Shinpei Hayashi

Ryosuke Funaki, Shinpei Hayashi, Motoshi Saeki

While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edits whose changes do not depend on each other exist. We empirically investigated the impact of systematic edits on history slicing. In this study, commits in which systematic edits were detected are split between each file so that unnecessary dependencies between commits are eliminated. In several histories of open source systems, the size of history slices was reduced by 13.3-57.2% on average after splitting the commits for systematic edits.

Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi et al.

Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, and F-score, against 600 manually annotated variables from a sample of GitHub projects. The performance of our advanced approach based on F-score was superior compared to lexical matching, 0.986 and 0.942, respectively. We then create a taxonomy of the types of knowledge contained in these comments about variables of primitive types. Our study showed that developers usually documented the variables' identifiers of a numeric data type with their purpose~(69.16%) and concept~(72.75%) more than the variables' identifiers of type String which were less documented with purpose~(61.14%) and concept~(55.46%). Our findings characterise the current state of the practice of documenting primitive variables and point at areas that are often not well documented, such as the meaning of boolean variables or the purpose of fields and local variables.

Eunjong Choi, Kenji Fujiwara, Norihiro Yoshida et al.

Refactoring is the process of changing a software system in such a way that it does not alter the external behavior of the code yet improves its internal structure. Not only researchers, but also practitioners, need to know about past refactoring instances performed in a software development project. So far, a number of techniques have been proposed for automatic detection of refactoring instances. Those techniques have been presented in various international conferences and journals, however, it is difficult for researchers and practitioners to grasp the current status of studies on refactoring detection techniques. In this survey paper, we review various refactoring detection techniques, especially techniques based on change history analysis. First, we give the definition and categorization of refactoring detection methods in this paper, and then introduce refactoring detection techniques based on change history analysis. Finally, we discuss possible future research directions for refactoring detection.