ω\omegaTest: WebView-Oriented Testing for Android Applications

WebView is a UI widget that helps integrate web applications into the native context of Android apps. It provides powerful mechanisms for bi-directional interactions between the native-end (Java) and the web-end (JavaScript) of an Android app. However, these interaction mechanisms are complicated and have induced various types of bugs. To mitigate the problem, various techniques have been proposed to detect WebView-induced bugs via dynamic analysis, which heavily relies on executing tests to explore WebView behaviors. Unfortunately, these techniques either require manual effort or adopt random test generation approaches, which are not able to effectively explore diverse WebView behaviors. In this paper, we study the problem of test generation for WebViews in Android apps. Effective test generation for WebViews requires identifying the essential program properties to be covered by the generated tests. To this end, we propose WebView-specific properties to characterize WebView behaviors, and devise a cross-language dynamic analysis method to identify these properties. We develop $\omega$Test, a test generation technique that searches for event sequences covering the identified WebView-specific properties. An evaluation on 74 real-world open-/closed-source Android apps shows that $\omega$Test can cover diverse WebView behaviors and detect WebView-induced bugs effectively. $\omega$Test detected 36 previously-unknown bugs. From the 22 bugs that we have reported to the app developers, 13 bugs were confirmed, 9 of which were fixed.Comment: Accepted by the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023

Fuzzing Deep Learning Compilers with HirGen

Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of compiled DL models. A recent bug study shows that the optimization of high-level intermediate representation (IR) is the most error-prone compilation stage. Bugs in this stage are accountable for 44.92% of the whole collected ones. However, existing testing techniques do not consider high-level optimization related features (e.g. high-level IR), and are therefore weak in exposing bugs at this stage. To bridge this gap, we propose HirGen, an automated testing technique that aims to effectively expose coding mistakes in the optimization of high-level IR. The design of HirGen includes 1) three coverage criteria to generate diverse and valid computational graphs; 2) full use of high-level IRs language features to generate diverse IRs; 3) three test oracles inspired from both differential testing and metamorphic testing. HirGen has successfully detected 21 bugs that occur at TVM, with 17 bugs confirmed and 12 fixed. Further, we construct four baselines using the state-of-the-art DL compiler fuzzers that can cover the high-level optimization stage. Our experiment results show that HirGen can detect 10 crashes and inconsistencies that cannot be detected by the baselines in 48 hours. We further validate the usefulness of our proposed coverage criteria and test oracles in evaluation

Programming by Example Made Easy

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee's effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers.Comment: Accepted by ACM Transactions on Software Engineering and Methodolog

MEMO: Coverage-guided Model Generation For Deep Learning Library Testing

Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications. A few techniques have thereby been proposed to test DL libraries by generating DL models as test inputs. Then these techniques feed those DL models to DL libraries for making inferences, in order to exercise DL libraries modules related to a DL model's execution. However, the test effectiveness of these techniques is constrained by the diversity of generated DL models. Our investigation finds that these techniques can cover at most 11.7% of layer pairs (i.e., call sequence between two layer APIs) and 55.8% of layer parameters (e.g., "padding" in Conv2D). As a result, we find that many bugs arising from specific layer pairs and parameters can be missed by existing techniques. In view of the limitations of existing DL library testing techniques, we propose MEMO to efficiently generate diverse DL models by exploring layer types, layer pairs, and layer parameters. MEMO: (1) designs an initial model reduction technique to boost test efficiency without compromising model diversity; and (2) designs a set of mutation operators for a customized Markov Chain Monte Carlo (MCMC) algorithm to explore new layer types, layer pairs, and layer parameters. We evaluate MEMO on seven popular DL libraries, including four for model execution (TensorFlow, PyTorch and MXNet, and ONNX) and three for model conversions (Keras-MXNet, TF2ONNX, ONNX2PyTorch). The evaluation result shows that MEMO outperforms recent works by covering 10.3% more layer pairs, 15.3% more layer parameters, and 2.3% library branches. Moreover, MEMO detects 29 new bugs in the latest version of DL libraries, with 17 of them confirmed by DL library developers, and 5 of those confirmed bugs have been fixed.Comment: 11 pages, 8 figure

An investigation of chirped fibre Bragg gratings Fabry-Perot interferometer for sensing applications

Fibre interferometer configurations such as the Michelson and Fabry-Perot (FP) have been formed using uniformed and chirped Fibre Bragg Gratings (FBG) acting as partial reflectors. As well as increasing the dynamic range of the interferometer, chirped FBGs are dispersive elements which can allow tuning of the response of the interferometers to measurements such as strain and temperature. In a chirped FBG, the resonance condition of the FBG varies along the FBG’s length. Each wavelength is reflected from different portion of the FBG, which imparts a different group delay to the different components of the incident light. The implication of the wavelength dependence resonance position is that there is a large movement of the resonance position when the incident wavelength is changed. A chirped FBG FP can be configured in which the large movement of the reflection positions in the respective FBGs forming the cavity changes in such a way that the sensitivity of the cavity can be enhanced or reduced. The FP filter response can be tailored through the extent of chirp. In this project a theoretical model of the in fibre interferometers formed using chirped FBGs is presented. The model indicates that it is possible to form FP cavities with varying sensitivity to strain and temperature by appropriate choice of chirp parameters and cavity length. An experimental demonstration of a chirped FBG FP cavity with reduced sensitivity to strain. This scheme offers flexibility in determining the sensitivity of the FP sensor to strain, not only through the gauge length but also via the parameters of the chirped FBG pairs, allowing the use of long or short gauge length sensors. It is possible to configure the system to exhibit enhanced sensitivity to strain or alternatively, to have reduced or even zero strain sensitivity. This ability to tailor the sensitivity of the FP via the FBG parameters will enhance the capabilities of FP sensor system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo