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ICSE 2021
Mon 17 May - Sat 5 June 2021

Static data flow analysis is an integral building block for many applications, ranging from compile-time code optimization to security and privacy analysis. When assessing whether a mobile app is trustworthy, for example, analysts need to identify which of the user’s personal data is sent to external parties such as the app developer or cloud providers. Since accessing and sending data is usually done via API calls, tracking the data flow between source and sink API is often the method of choice. Precise algorithms such as IFDS help reduce the number of false positives, but also introduce significant performance penalties. With its fixpoint iteration over the program’s entire exploded supergraph, IFDS is particularly memory-intensive, consuming hundreds of megabytes or even several gigabytes for medium-sized apps.

In this paper, we present a technique called CleanDroid for reducing the memory footprint of a precise IFDS-based data flow analysis and demonstrate its effectiveness in the popular FlowDroid open-source data flow solver. CleanDroid efficiently removes edges from the jump function cache used for the IFDS fixpoint iteration without affecting termination. As we show on 600 real-world Android apps from the Google Play Store, CleanDroid reduces the average per-app memory consumption by around 63% to 78%. At the same time, CleanDroid speeds up the analysis by up to 66%.

Fri 28 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:00 - 10:55
4.1.3. Privacy in Apps: Cases from COVID-19SEIS - Software Engineering in Society / Technical Track at Blended Sessions Room 3 +12h
Chair(s): Andrea Zisman The Open University
10:00
15m
Paper
COVID-19 Vs Social Media apps: Does privacy really matter?SEIS
SEIS - Software Engineering in Society
Omar Haggag Monash University, Australia, Sherif Haggag Deakin University, Australia, John Grundy Monash University, Mohamed Abdelrazek Deakin University, Australia
Pre-print Media Attached
10:15
20m
Paper
An Empirical Assessment of Global COVID-19 Contact Tracing ApplicationsArtifact ReusableTechnical TrackArtifact Available
Technical Track
Ruoxi Sun The University of Adelaide, Wei (Zach) Wang The University of Adelaide, Minhui (Jason) Xue The University of Adelaide, Gareth Tyson Queen Mary University of London, Seyit Camtepe CSIRO Data61, Damith C. Ranasinghe The University of Adelaide
Pre-print Media Attached
10:35
20m
Paper
Sustainable Solving: Reducing The Memory Footprint of IFDS-Based Data Flow Analyses Using Intelligent Garbage CollectionTechnical Track
Technical Track
Steven Arzt Fraunhofer SIT
Pre-print Media Attached
22:00 - 22:55
4.1.3. Privacy in Apps: Cases from COVID-19Technical Track / SEIS - Software Engineering in Society at Blended Sessions Room 3
22:00
15m
Paper
COVID-19 Vs Social Media apps: Does privacy really matter?SEIS
SEIS - Software Engineering in Society
Omar Haggag Monash University, Australia, Sherif Haggag Deakin University, Australia, John Grundy Monash University, Mohamed Abdelrazek Deakin University, Australia
Pre-print Media Attached
22:15
20m
Paper
An Empirical Assessment of Global COVID-19 Contact Tracing ApplicationsArtifact ReusableTechnical TrackArtifact Available
Technical Track
Ruoxi Sun The University of Adelaide, Wei (Zach) Wang The University of Adelaide, Minhui (Jason) Xue The University of Adelaide, Gareth Tyson Queen Mary University of London, Seyit Camtepe CSIRO Data61, Damith C. Ranasinghe The University of Adelaide
Pre-print Media Attached
22:35
20m
Paper
Sustainable Solving: Reducing The Memory Footprint of IFDS-Based Data Flow Analyses Using Intelligent Garbage CollectionTechnical Track
Technical Track
Steven Arzt Fraunhofer SIT
Pre-print Media Attached