EDBT 2026 Demo / reviewers in the wild / expert
Mihir Mathur
dblp:228/5734
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2
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
2 papers |
Software maintenance and evolution · 35% Empirical software engineering · 30% Program verification · 20% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program verification › model checking › state space exploration
reachability analysis |
0.4 | 1 | 2020 | WebJShrink: a web service for debloating Java bytecode · ESEC/SIGSOFT FSE 2020 |
Software maintenance and evolution › software reengineering
software debloating |
0.4 | 1 | 2020 | WebJShrink: a web service for debloating Java bytecode · ESEC/SIGSOFT FSE 2020 |
Software maintenance and evolution
API usage |
0.3 | 1 | 2018 | Augmenting stack overflow with API usage patterns mined from GitHub · ESEC/SIGSOFT FSE 2018 |
Program analysis › error detection
API usage error detection |
0.3 | 1 | 2018 | Augmenting stack overflow with API usage patterns mined from GitHub · ESEC/SIGSOFT FSE 2018 |
Empirical software engineering › mining software repositories
API usage patterns |
0.3 | 1 | 2018 | Augmenting stack overflow with API usage patterns mined from GitHub · ESEC/SIGSOFT FSE 2018 |
Empirical software engineering
mining software repositories |
0.3 | 1 | 2018 | Augmenting stack overflow with API usage patterns mined from GitHub · ESEC/SIGSOFT FSE 2018 |
Methods — techniques the papers use, named apart from their topics
visual analytics · 0.9static analysis · 0.9dynamic analysis · 0.9pattern mining · 0.3chrome extension · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | WebJShrink: a web service for debloating Java bytecodeabstractAs software projects grow in complexity, they come packaged with under-utilized libraries and therefore become bloated. Though several software debloating tools exist, none of them help developers gain insights into how under-utilized those libraries are nor help developers build confidence in the behavior preservation of software after debloating. To bridge this gap, we developed WebJShrink, a visual analytics tool for analyzing and pruning bloated software projects. WebJShrink is built on JShrink which uses static and dynamic reachability analysis to determine the extent of software bloat. WebJShrink provides rich visualizations of the bloat lurking within a target project's internal structure. It then removes unused features, and returns a safer, slimmer variant of the software project. To illustrate the target project's behavior preservation, WebJShrink examines the debloated software with its JUnit tests and visualizes the test results. In evaluating WebJShrink against 26 real world systems, we found WebJShrink could reduce software size by up to 42%, 11% on average, while still passing 100% of unit tests after debloating. We provide a video demonstrating WebJShrink at https://youtu.be/yzVzcd-MJ1w. Konner Macias, Mihir Mathur, Bobby R. Bruce, Tianyi Zhang 0001, Miryung Kim |
ESEC/SIGSOFT FSE | 2 |
| 2018 | Augmenting stack overflow with API usage patterns mined from GitHubabstractProgrammers often consult Q&A websites such as Stack Overflow (SO) to learn new APIs. However, online code snippets are not always complete or reliable in terms of API usage. To assess online code snippets, we build a Chrome extension, ExampleCheck that detects API usage violations in SO posts using API usage patterns mined from 380K GitHub projects. It quantifies how many GitHub examples follow common API usage and illustrates how to remedy the detected violation in a given SO snippet. With ExampleCheck, programmers can easily identify the pitfalls of a given SO snippet and learn how much it deviates from common API usage patterns in GitHub. The demo video is at https://youtu.be/WOnN-wQZsH0. Anastasia Schaadhardt, Tianyi Zhang 0001, Mihir Mathur, Miryung Kim |
ESEC/SIGSOFT FSE | 3 |