Demo Data from VLDB 2026 / reviewers in the wild / expert
Simone Paolo Mottadelli
dblp:269/8047
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1
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 |
Software testing · 100% | |
| Network and information security
1 paper |
Web and mobile security · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › mobile application testing
android app testing |
0.4 | 1 | 2020 | Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020 |
Software testing
fault detection |
0.4 | 1 | 2020 | Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020 |
Software testing
test generation |
0.4 | 1 | 2020 | Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020 |
Web and mobile security
mobile application security |
0.1 | 1 | 2020 | Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020 |
Methods — techniques the papers use, named apart from their topics
test case generation · 0.9oracle strategies · 0.9exploration strategy · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Deep Attributed Graph Embeddings
Elisabetta Fersini, Simone Paolo Mottadelli, Michele Carbonera, Enza Messina |
MDAI | 2 |
| 2020 | Data loss detector: automatically revealing data loss bugs in Android appsabstractAndroid apps must work correctly even if their execution is interrupted by external events. For instance, an app must work properly even if a phone call is received, or after its layout is redrawn because the smartphone has been rotated. Since these events may require destroying, when the execution is interrupted, and recreating, when the execution is resumed, the foreground activity of the app, the only way to prevent the loss of state information is to save and restore it. This behavior must be explicitly implemented by app developers, who often miss to implement it properly, releasing apps affected by data loss problems, that is, apps that may lose state information when their execution is interrupted. Although several techniques can be used to automatically generate test cases for Android apps, the obtained test cases seldom include the interactions and the checks necessary to exercise and reveal data loss faults. To address this problem, this paper presents Data Loss Detector (DLD), a test case generation technique that integrates an exploration strategy, data-loss-revealing actions, and two customized oracle strategies for the detection of data loss failures. DLD revealed 75% of the faults in a benchmark of 54 Android app releases affected by 110 known data loss faults, and also revealed unknown data loss problems, outperforming competing approaches. Oliviero Riganelli, Simone Paolo Mottadelli, Claudio Rota, Daniela Micucci, Leonardo Mariani |
ISSTA | 2 |