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Simone Paolo Mottadelli

dblp:269/8047 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Software testing › mobile application testing
android app testing
0.412020
Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020
Software testing
fault detection
0.412020
Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020
Software testing
test generation
0.412020
Data loss detector: automatically revealing data loss bugs in Android apps · ISSTA 2020
Web and mobile security
mobile application security
0.112020
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
YearPublicationVenuePosition
2022 Deep Attributed Graph Embeddings
Elisabetta Fersini, Simone Paolo Mottadelli, Michele Carbonera, Enza Messina
MDAI2
2020 Data loss detector: automatically revealing data loss bugs in Android apps
abstract
Android 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
ISSTA2