Demo Data from SIGIR 2026 / reviewers in the wild / expert
Junji Shimagaki
dblp:179/8574
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 67% Requirements engineering and software design · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › mining software repositories › app store mining
app review analysis |
0.2 | 1 | 2016 | What would users change in my app? summarizing app reviews for recommending software changes · SIGSOFT FSE 2016 |
Requirements engineering and software design
requirements elicitation |
0.2 | 1 | 2016 | What would users change in my app? summarizing app reviews for recommending software changes · SIGSOFT FSE 2016 |
Empirical software engineering › software analytics
user feedback analysis |
0.2 | 1 | 2016 | What would users change in my app? summarizing app reviews for recommending software changes · SIGSOFT FSE 2016 |
Methods — techniques the papers use, named apart from their topics
text summarization · 0.2conceptual modeling · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Automatic topic classification of test cases using text mining at an Android smartphone vendorabstractBackground: An Android smartphone is an ecosystem of applications, drivers, operating system components, and assets. The volume of the software is large and the number of test cases needed to cover the functionality of an Android system is substantial. Enormous effort has been already taken to properly quantify "what features and apps were tested and verified?". This insight is provided by dashboards that summarize test coverage and results per feature. One method to achieve this is to manually tag or label test cases with the topic or function they cover, much like function points. At the studied Android smartphone vendor, tests are labelled with manually defined tags, so-called "feature labels (FLs)", and the FLs serve to categorize 100s to 1000s test cases into 10 to 50 groups. Junji Shimagaki, Yasutaka Kamei, Naoyasu Ubayashi, Abram Hindle |
ESEM | 1 |
| 2016 | Why are Commits Being Reverted?: A Comparative Study of Industrial and Open Source ProjectsabstractSoftware development is a cyclic process of integrating new features while introducing and fixing defects. During development, commits that modify source code files are uploaded to version control systems. Occasionally, these commits need to be reverted, i.e., the code changes need to be completely backed out of the software project. While one can often speculate about the purpose of reverted commits (e.g., the commit may have caused integration or build problems), little empirical evidence exists to substantiate such claims. The goal of this paper is to better understand why commits are reverted in large software systems. To that end, we quantitatively and qualitatively study two proprietary and four open source projects to measure: (1) the proportion of commits that are reverted, (2) the amount of time that commits that are eventually reverted linger within a codebase, and (3) the most frequent reasons why commits are reverted. Our results show that 1%-5% of the commits in the studied systems are reverted. Those commits that are eventually reverted linger within the studied codebases for 1-35 days (median). Furthermore, we identify 13 common reasons for reverting commits, and observe that the frequency of reverted commits of each reason varies broadly from project to project. A complementary qualitative analysis suggests that many reverted commits could have been avoided with better team communication and change awareness. Our findings made Sony Mobile's stakeholders aware that internally reverted commits can be reduced by paying more attention to their own changes. On the other hand, externally reverted commits could be minimized only if external stakeholders are involved to improve inter-company communication or requirements elicitation. Junji Shimagaki, Yasutaka Kamei, Shane McIntosh, David Pursehouse, Naoyasu Ubayashi |
ICSME | 1 |
| 2016 | What would users change in my app? summarizing app reviews for recommending software changesabstractMobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better meeting user expectations. Thus, automated approaches have been proposed in literature with the aim of reducing the effort required for analyzing feedback contained in user reviews via automatic classification/prioritization according to specific topics. In this paper, we introduce SURF (Summarizer of User Reviews Feedback), a novel approach to condense the enormous amount of information that developers of popular apps have to manage due to user feedback received on a daily basis. SURF relies on a conceptual model for capturing user needs useful for developers performing maintenance and evolution tasks. Then it uses sophisticated summarisation techniques for summarizing thousands of reviews and generating an interactive, structured and condensed agenda of recommended software changes. We performed an end-to-end evaluation of SURF on user reviews of 17 mobile apps (5 of them developed by Sony Mobile), involving 23 developers and researchers in total. Results demonstrate high accuracy of SURF in summarizing reviews and the usefulness of the recommended changes. In evaluating our approach we found that SURF helps developers in better understanding user needs, substantially reducing the time required by developers compared to manually analyzing user (change) requests and planning future software changes. Andrea Di Sorbo, Sebastiano Panichella, Carol V. Alexandru, Junji Shimagaki, Corrado Aaron Visaggio, Gerardo Canfora, Harald C. Gall |
SIGSOFT FSE | 4 |