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Hariharan Mathavan

dblp:241/4272 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Software 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
2 papers
Debugging and program repair · 48% Software testing · 24% Operating systems · 14%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Debugging and program repair
record and replay
0.822019
RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation · ASE 2019
Towards Practical Record and Replay for Mobile Applications · DAC 2019
Software testing
test execution
0.412019
RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation · ASE 2019
Operating systems › mobile systems › mobile operating systems
android
0.112019
Towards Practical Record and Replay for Mobile Applications · DAC 2019
Program analysis
dynamic analysis
0.112019
RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation · ASE 2019
Operating systems › mobile systems
mobile operating systems
0.112019
Towards Practical Record and Replay for Mobile Applications · DAC 2019
Program analysis › dynamic analysis
runtime instrumentation
0.112019
RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation · ASE 2019

Methods — techniques the papers use, named apart from their topics

targeted runtime instrumentation · 0.4dynamic instrumentation · 0.4UI event contextualization · 0.4
YearPublicationVenuePosition
2019 Towards Practical Record and Replay for Mobile Applications
abstract
The ability to repeat the execution of a program is a fundamental requirement in evaluating computer systems and apps. Reproducing executions of mobile apps has proven difficult under real-life scenarios due to different sources of external inputs and interactive nature of the apps. We present a new practical record/replay framework for Android, RandR, which handles multiple sources of input and provides cross-device replay capabilities through a dynamic instrumentation approach. We demonstrate the feasibility of RandR by recording and replaying a set of real-world apps.
Onur Sahin, Assel Aliyeva, Hariharan Mathavan, Ayse K. Coskun, Manuel Egele
DAC3
2019 RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation
abstract
The ability to repeat the execution of a program is a fundamental requirement in many areas of computing from computer system evaluation to software engineering. Reproducing executions of mobile apps, in particular, has proven difficult under real-life scenarios due to multiple sources of external inputs and interactive nature of the apps. Previous works that provide record/replay functionality for mobile apps are restricted to particular input sources (e.g., touchscreen events) and present deployment challenges due to intrusive modifications to the underlying software stack. Moreover, due to their reliance on record and replay of device specific events, the recorded executions cannot be reliably reproduced across different platforms. In this paper, we present a new practical approach, RandR, for record and replay of Android applications. RandR captures and replays multiple sources of input (i.e., UI and network) without requiring source code (OS or app), administrative device privileges, or any special platform support. RandR achieves these qualities by instrumenting a select set of methods at runtime within an application's own sandbox. In addition, to enable portability of recorded executions across different platforms for replay, RandR contextualizes UI events as interactions with particular UI components (e.g., a button) as opposed to relying on platform specific features (e.g., screen coordinates). We demonstrate RandR's accurate cross-platform record and replay capabilities using over 30 real-world Android apps across a variety of platforms including emulators as well as commercial off-the-shelf mobile devices deployed in real life.
Onur Sahin, Assel Aliyeva, Hariharan Mathavan, Ayse K. Coskun, Manuel Egele
ASE3