A Software Bill of Materials (SBOM) is becoming an essential tool for effective software dependency management. An SBOM is a list of components used in software, including details such as component names, versions, and licenses. Using SBOMs, developers can quickly identify software components and assess whether their software depends on vulnerable libraries. Numerous tools support software dependency management through SBOMs, which can be broadly categorized into two types: tools that generate SBOMs and tools that utilize SBOMs. A substantial collection of accurate SBOMs is required to evaluate tools that utilize SBOMs. However, there is no publicly available dataset specifically designed for this purpose, and research on SBOM consumption tools remains limited. In this paper, we present a dataset of SBOMs to address this gap. The dataset we constructed comprises 46 SBOMs generated from real-world Java projects, with plans to expand it to include a broader range of projects across various programming languages. Accurate and well-structured SBOMs enable researchers to evaluate the functionality of SBOM consumption tools and identify potential issues. We collected 3,271 Java projects from GitHub and generated SBOMs for 798 of them using Maven with an open-source SBOM generation tool. These SBOMs were refined through both automatic and manual corrections to ensure accuracy, currently resulting in 46 SBOMs that comply with the SPDX Lite profile, which defines minimal requirements tailored to practical workflows in industries. This process also revealed issues with the SBOM generation tools themselves. The dataset is publicly available on Zenodo (DOI: 10.5281/zenodo.14233414).

Since programming languages offer a wide variety of grammers, desired functions can be implemented in a variety of ways. We consider that there is a large amount of source code that has different implementations of the same functions, and that those can be compiled into a dataset useful for various research in software engineering. In this study, we construct a dataset of functionally equivalent Java methods from about 36 million lines of source code. The constructed dataset is available at https://zenodo.org/record/5912689.

Daylight saving time (DST) is observed in many countries and regions. DST is not considered on some software systems at the beginning of their developments, for example, software systems developed in regions where DST is not observed. However, such systems may have to consider DST at the requests of their users. Before now, there has been no study about the impacts of DST on software development. In this paper, we study the impacts of DST on software development by mining the repositories on GitHub. We analyze the date when the code related to DST is changed, and we analyze the regions where the developers applied the changes live. Furthermore, we classify the changes into some patterns.

On development using a version control system, understanding differences of source code is important. Edit scripts (in short, ES) represent differences between two versions of source code. One of the tools generating ESs is GumTree. GumTree takes two versions of source code as input and generates an ES consisting of insert, delete, update and move nodes of abstract syntax tree (in short, AST). However, the accuracy of move and update actions generated by GumTree is insufficient, which makes ESs more difficult to understand. A reason why the accuracy is insufficient is that GumTree generates ESs from only information of AST. Thus, in this research, we propose to generate easier-to-understand ESs by using not only structures of AST but also information of line differences. To evaluate our methodology, we applied it to some open source software, and we confirmed that ESs generated by our methodology are more helpful to understand the differences of source code than GumTree.

When we use code repositories, each commit should include code changes for only a single task and code changes for a single task should not be scattered over multiple commits. There are many studies on the former violation-often referred to as tangled commits- but the latter violation has been out of scope for MSR research. In this paper, we firstly investigate how much and what kinds of inappropriately partitioned commits in Java projects. Then, we propose a simple technique to detect such commits automatically. We also report evaluation results of the proposed technique.

Existing techniques have succeeded to help developers implement new code. However, they are insufficient to help to change existing code. Previous studies have proposed techniques to support bug fixes but other kinds of code changes such as function enhancements and refactorings are not supported by them. In this paper, we propose a novel system that helps developers change existing code. Unlike existing techniques, our system can support any kinds of code changes if similar code changes occurred in the past. Our research is still on very early stage and we have not have any implementation or any prototype yet. This paper introduces our research purpose, an outline of our system, and how our system is different from existing techniques.

This paper introduces a new dataset of clone references, which is a set of correct clones consisting of their locational information with their gapped lines. Bellon's dataset is one of widely used clone datasets. Bellon's dataset contains many clone references, thus the dataset is useful for comparing accuracies among clone detectors. However, Bellon's dataset does not have locational information of gapped lines. Thus, Bellon's benchmark does not evaluate some Type-3 clones correctly. In order to resolve the problem, we added locational information of gapped lines to Bellon's dataset. The new dataset is available at ``http://sdl.ist.osaka-u.ac.jp/~h-murakm/2014_clone_references_with_gaps/''.