Minimizing GUI event traces

Clapp, Lazaro; Bastani, Osbert; Anand, Sandeep; Aiken, Alex

doi:10.1145/2950290.2950342

Cited by 39 publications

(16 citation statements)

References 27 publications

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“…There have been several recent successes in applying (supervised) machine learning to programming languages research. For example, machine learning has been used to infer program invariants [Padhi et al 2016;, improve program analysis [Liang et al 2011;Mangal et al 2015;Raghothaman et al 2018;Raychev et al 2015] and synthesis [Balog et al 2016;Feng et al 2018Kalyan et al 2018;Lee et al 2018;Raychev et al 2016b;Schkufza et al 2013Schkufza et al , 2014, build probabilistic models of code [Bielik et al 2016;Raychev et al 2016aRaychev et al , 2014, infer specifications [Bastani et al 2017[Bastani et al , 2018bBeckman and Nori 2011;Bielik et al 2017;Heule et al 2016;Kremenek et al 2006;Livshits et al 2009], test software [Clapp et al 2016;Godefroid et al 2017;Liblit et al 2005], and select lemmas for automated Proof. First, because transitions are deterministic, we have…”

Section: Related Workmentioning

confidence: 99%

Relational verification using reinforcement learning

Chen

Wei

et al. 2019

Proc. ACM Program. Lang.

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Relational verification aims to prove properties that relate a pair of programs or two different runs of the same program. While relational properties (e.g., equivalence, non-interference) can be verified by reducing them to standard safety, there are typically many possible reduction strategies, only some of which result in successful automated verification. Motivated by this problem, we propose a new relational verification algorithm that learns useful reduction strategies using reinforcement learning. Specifically, we show how to formulate relational verification as a Markov decision process (MDP) and use reinforcement learning to synthesize an optimal policy for the underlying MDP. The learned policy is then used to guide the search for a successful verification strategy. We have implemented this approach in a tool called Coeus and evaluate it on two benchmark suites. Our evaluation shows that Coeus solves significantly more problems within a given time limit compared to multiple baselines, including two state-of-the-art relational verification tools. CCS Concepts: • Software and its engineering → Software verification; • Theory of computation → Reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Relational verification using reinforcement learning

Chen

Wei

et al. 2019

Proc. ACM Program. Lang.

Self Cite

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“…There is a large body of work on automatically testing Android applications at the level of the UI. Fully automated testing approaches include random and search-directed event generation tools [12,27,35,38,40,52,63], model-based exploration [1,2,6,10,31,43,49,62], concolic testing [3], and event generation using evolutionary algorithms [39].…”

Section: Related Workmentioning

confidence: 99%

Safe stream-based programming with refinement types

Stein

Clapp

Sridharan

et al. 2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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In stream-based programming, data sources are abstracted as a stream of values that can be manipulated via callback functions. Stream-based programming is exploding in popularity, as it provides a powerful and expressive paradigm for handling asynchronous data sources in interactive software. However, high-level stream abstractions can also make it difficult for developers to reason about control-and data-flow relationships in their programs. This is particularly impactful when asynchronous stream-based code interacts with thread-limited features such as UI frameworks that restrict UI access to a single thread, since the threading behavior of streaming constructs is often non-intuitive and insufficiently documented.In this paper, we present a type-based approach that can statically prove the thread-safety of UI accesses in stream-based software.Our key insight is that the fluent APIs of stream-processing frameworks enable the tracking of threads via type-refinement, making it possible to reason automatically about what thread a piece of code runs on -a difficult problem in general. We implement the system as an annotation-based Java typechecker for Android programs built upon the popular ReactiveX framework and evaluate its efficacy by annotating and analyzing 8 open-source apps, where we find 33 instances of unsafe UI access while incurring an annotation burden of only one annotation per 186 source lines of code. We also report on our experience applying the typechecker to two much larger apps from the Uber Technologies, Inc. codebase, where it currently runs on every code change and blocks changes that introduce potential threading bugs. CCS CONCEPTS• Software and its engineering → Software notations and tools; Formal software verification; KEYWORDS stream-based programming, refinement types, mobile applications

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“…The state-of-the-practice technique such as Monkey [95] generates pseudo-random streams of UI and system events. However, it usually generates many redundant [113] and irrelevant events [114] which do not consider current app state. So, other approaches have been proposed to systematically explore app GUIs, and generate relevant inputs.…”

Section: Aimdroidmentioning

confidence: 99%

Testing mobile applications via automated exploration of graphical user interface and textual user input generation

Arnatovich¹

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Minimizing GUI event traces

Cited by 39 publications

References 27 publications

Relational verification using reinforcement learning

Relational verification using reinforcement learning

Safe stream-based programming with refinement types

Testing mobile applications via automated exploration of graphical user interface and textual user input generation

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