## 1 Introduction  
At the present time, current software is becoming increasingly more significant and complicated [1]. In software development projects, new members are typically assigned during the project period. As part of the project onboarding, they must acquire knowledge and context. The need to know the software under development is the main difficulty of project onboarding [2]. This often involves reading software documents to understand the implemented features of the project. However, these documents become outdated and less insightful as a project evolves. Outdated documentation demotivates newcomers [3]. Onthe other hand, issue tickets (hereafter tickets), which are created as a project evolves, include the latest information about the implemented features. In issue tracking systems (ITSs), developers can take a known issue such as a failure and a feature request and assign it a \"ticket\" [4]. Although tickets tend to contain insightful information, they are often unorganized. In theory, new members are expected to read most of the important tickets to understand the implemented features. However, this is time consuming. Tickets are typically handled one by one; however, groups of tickets often follow common patterns [5]. Our research assumes that categorizing tickets following common patterns in terms of ticket descriptions may help new members quickly read the tickets. Moreover, if each ticket category has known characteristics, the contents of the tickets can be understood efficiently and effectively. The purpose of this paper is to clarify the way of helping new members understand the implemented features of a project by using tickets. Herein we propose a novel method to categorize tickets and visualize characteristics of each category. Figure 1 overviews our method. Our method requires only managing tickets in an ITS. First, tickets are converted into vector representations to apply a clustering method. Second, the clustering method automatically categorizes the tickets and estimates the appropriate number of ticket clusters. Finally, our method visualizes the lifetime and features of the ticket categories. Our ticket feature visualization has two main aspects. First, it counts the time series change of ticket categories and displays the results as a heatmap. This helps new members quickly review tickets and identify features that they should work on first. Second, our ticket feature visualization identifies the relationships among ticket categories and keywords. The results are displayed by heatmapping and PCA. Our ticket feature visualization helps new members determine which categories are related to the features. As a case study, we applied our method to an industrial software development project [6,7] conducted in a company NTT based in Japan (hereafter, referred to as the company). In this project, 111 tickets were created and managed on Backlog, which is an ITS [8]. Furthermore, we conducted an experiment targeting the same project data as the case study. Specifically, we answer the following four research questions:  
1. Does our categorization method estimate the number of ticket categories and categorize tickets accurately? To answer this question, we categorized 111 tickets using K-means clustering and gap statistics. K-means is an unsupervised partitional clustering algorithm and one of the most widely used algorithms for grouping data [9]. K-means clustering is widely used to cluster requirement documents. Gap statistics is an automatic method to estimate the number of clusters. We evaluated the estimation accuracy for the number of ticket categories from the distribution of 200 automatic  
Figure 1: Overview of our method. estimations. Furthermore, we assessed the categorization accuracy for tickets using well-known clustering measures [10]. * Can our visualization method help new members understand the implemented features of a project? To answer this question, we interviewed two project members and three external experts.