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Cross-organizational process mining aims to discover an entire process model across multiple organizations where their identifier (ID) systems are not managed uniformly, and each organization has an independent ID system. Cross-organizational process mining has been gaining popularity as information systems increase in complexity. However, previous methods have limitations in that they do not work well for event logs that contain only common items, or cyclic orchestrations, which indicates that the model contains loops. In this paper, we propose an accurate cross-organizational process mining technique based on a step-by-step case ID identification mechanism that uses only common items in event logs and can handle cyclic orchestrations. Step-by-step case ID identification repeats the following steps: 1) identification of case IDs based on activity connection of adjacent event pairs, and 2) extraction of additional activity connections by leveraging the newly identified case IDs. We alternately identify the most probable case ID pairs and remove events belonging to these identified case IDs from the event log, which contributes to extracting additional activity connections and narrowing down the candidates of case ID pairs. Evaluation using real-world event logs showed that the proposed method generates the process model with more than 98.4% precision and more than 94.2% recall for two datasets, outperforming previous methods.

Industrial software maintenance is critical but burdensome. Activities such as detecting duplicate bug reports are often performed manually. Herein an automated duplicate bug report detection system improves maintenance efficiency using vectorization of the contents and deep learning–based sentence embedding to calculate the similarity of the whole report from vectors of individual elements. Specifically, sentence embedding is realized using Sentence-BERT fine tuning. Additionally, its performance is experimentally compared to baseline methods to validate the proposed system. The proposed system detects duplicate bug reports more effectively than existing methods.

Background: In most software projects, new members must comprehend the features already implemented since they are usually assigned during the project period. They often read software documents (e.g., flowcharts and data models), but such documents tend not to be updated after they are created. Herein we focus on tickets issued because they are created as a project evolves and include the latest information of the implemented features. Aim: The purpose of this paper is to clarify the way of helping new members understand the implemented features of a project by using tickets. Methodology: We propose a novel method to categorize tickets by clustering and visualizing the characteristics of each category via heatmapping and principal component analysis (PCA). Our method estimates the number of categories and categorizes issue tickets (tickets) automatically. Moreover, it has two visualizations. Ticket lifetime visualization shows the time series change to review tickets quickly, while ticket feature visualization shows the relationships among ticket categories and keywords of ticket categories using heatmapping and PCA. Results: To evaluate the effectiveness of our method, we implemented a case study. Specifically, we applied our method to an industrial software development project and interviewed the project members and external experts. Furthermore, we conducted an experiment to clarify the effectiveness of our method compared with a non-tool-assist method by letting subjects comprehend the target project, which is the same as that of the case study. These studies confirm our method supports experts’ and subjects’ comprehension of the project and its features by examining the ticket category lifetimes and keywords. Implication: Newcomers during project onboarding can utilize tickets to comprehend implemented features effectively if the tickets are appropriately structured and visualized. Conclusions: The original contribution of this paper is the proposal of the project feature comprehension method by visualizing the multi-dimensional nature of requirements in an organized and structured way based on available tickets and the result of its application to the industrial project.

The global information and technology services market for 2022 was reported to be 1.3 trillion U.S. dollars (about 150 trillion yen), and it's been pointed out that the massive amount of software produced by this market demand, while bringing great value to our daily lives, has also led to a variety of problems. There are expectations that an approach that not only seeks to increase the amount of software (production) but to also reduce that amount (reduction) could help solve those problems. In this interview, we asked NTT Distinguished Researcher Shinobu Saito about his research on "Software Digital Twins.". [ABSTRACT FROM AUTHOR]