Takashi Kobayashi

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.

Kunihiro Noda, Takashi Kobayashi, Kiyoshi Agusa

Comprehending the behavior of an object-oriented system solely from its source code is troublesome, owing to its dynamism. To aid comprehension, visualizing program behavior through reverse-engineered sequence diagrams from execution traces is a promising approach. However, because of the massiveness of traces, recovered diagrams tend to become very large, causing scalability issues. To address the issues, we propose an object grouping technique that horizontally summarizes a reverse-engineered sequence diagram. Our technique constructs object groups based on Pree's meta patterns, in which each group corresponds to a concept in the domain of a subject system. Visualizing interactions only among important groups, we generate a summarized sequence diagram depicting a behavioral overview of the system. Our experiment showed that our technique outperformed the state-of-the-art trace summarization technique in terms of reducing the horizontal size of reverse-engineered sequence diagrams. Regarding the quality of object grouping, our technique achieved an F-score of 0.670 and a Recall of 0.793 on average under the condition of #lifelines (i.e., the horizontal size of a sequence diagram) < 30, whereas those of the state-of-the-art technique were 0.421 and 0.670, respectively. The runtime overhead imposed by our technique was 129.2% on average, which is relatively smaller in the literature.