Ting Su

Ting Su, Chengyu Zhang, Yichen Yan et al.

Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are used. Such test objectives are referred as \emph{def-use pairs}. However, the complexity of DFT still overwhelms the testers in practice. To tackle this problem, we introduce a hybrid testing framework for data-flow based test generation: (1) The core of our framework is symbolic execution (SE), enhanced by a novel guided path exploration strategy to improve testing performance; and (2) we systematically cast DFT as reachability checking in software model checking (SMC) to complement SE, yielding practical DFT that combines the two techniques' strengths. We implemented our framework for C programs on top of the state-of-the-art symbolic execution engine KLEE and instantiated with three different software model checkers. Our evaluation on the 28,354 def-use pairs collected from 33 open-source and industrial program subjects shows (1) our SE-based approach can improve DFT performance by 15$\sim$48% in terms of testing time, compared with existing search strategies; and (2) our combined approach can further reduce testing time by 20.1$\sim$93.6%, and improve data-flow coverage by 27.8$\sim$45.2% by eliminating infeasible test objectives. Compared with the SMC-based approach alone, our combined approach can also reduce testing time by 19.9$\sim$23.8%, and improve data-flow coverage by 7$\sim$10%. This combined approach also enables the cross-checking of each component for reliable and robust testing results. We have made our testing framework and benchmarks publicly available to facilitate future research.

Chengyu Zhang, Yichen Yan, Hanru Zhou et al.

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.

Lingling Fan, Ting Su, Sen Chen et al.

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist -- this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.

Hang He, Yixing Luo, Chengcheng Wan et al.

Atomicity violations in interrupt-driven programs pose a significant threat to software reliability in safety-critical systems. These violations occur when the execution sequence of operations on shared resources is disrupted by asynchronous interrupts. Detecting atomicity violations is challenging due to the vast program state space, application-level code dependencies, and complex domain-specific knowledge. In this paper, we propose CLOVER, a multi-agent framework for detecting atomicity violations in real-world interrupt-driven programs. Its plan agent orchestrates four static analysis tools to extract key information and generate code summaries. CLOVER then initializes several Expert-Judge agent pairs to detect and validate different patterns of atomicity violation, through an iterative manner. Evaluations on RaceBench, SV-COMP, and RWIP demonstrate that CLOVER achieves a precision/recall of 91.0%/96.4%, outperforming existing approaches by 33.0-117.2% on F1-score. Additionally, it identifies 12 atomicity violations in 11 real-world aerospace software projects, one of which is previously unknown.

Ting Su, Yichen Yan, Jue Wang et al.

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Obviously, functional correctness is critical for an app's success. However, in addition to crash bugs, non-crashing functional bugs (in short as "non-crashing bugs" in this work) like inadvertent function failures, silent user data lost and incorrect display information are prevalent, even in popular, well-tested apps. These non-crashing functional bugs are usually caused by program logic errors and manifest themselves on the graphic user interfaces (GUIs). In practice, such bugs pose significant challenges in effectively detecting them because (1) current practices heavily rely on expensive, small-scale manual validation (the lack of automation); and (2) modern fully automated testing has been limited to crash bugs (the lack of test oracles). This paper fills this gap by introducing independent view fuzzing, a novel, fully automated approach for detecting non-crashing functional bugs in Android apps. Inspired by metamorphic testing, our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs for further manual confirmation. We have realized our approach as an automated, end-to-end functional fuzzing tool, Genie. Given an app, (1) Genie automatically detects non-crashing bugs without requiring human-provided tests and oracles (thus fully automated); and (2) the detected non-crashing bugs are diverse (thus general and not limited to specific functional properties), which set Genie apart from prior work.

Pengfei Gao, Fu Song, Taolue Chen et al.

JavaScript has become one of the most widely used languages for Web development. However, it is challenging to ensure the correctness and reliability of Web applications written in JavaScript, due to their dynamic and event-driven features. A variety of dynamic analysis techniques for JavaScript Web applications have been proposed, but they are limited in either coverage or scalability. In this paper, we propose a model-based automated approach to achieve high code coverage in a reasonable amount of time via testing with longer event sequences. We implement our approach as the tool LJS, and perform extensive experiments on 21 publicly available benchmarks (18,559 lines of code in total). On average, LJS achieves 86.4\% line coverage in 10 minutes, which is 5.4\% higher than that of JSDep, a breadth-first search based automated testing tool enriched with partial order reduction. In particular, on large applications, the coverage of LJS is 11-18\% higher than that of JSDep. Our empirical finding supports that longer test sequences can achieve higher code coverage in JavsScript testing.

Sen Chen, Lingling Fan, Chunyang Chen et al.

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code.

Sen Chen, Lingling Fan, Guozhu Meng et al.

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named AUSERA, which leverages static program analysis techniques and sensitive keyword identification. By leveraging AUSERA, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. To date, we highlight that 21 banks have confirmed the weaknesses we reported. We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.

Chengyu Zhang, Ting Su, Yichen Yan et al.

Dataflow coverage, one of the white-box testing criteria, focuses on the relations between variable definitions and their uses.Several empirical studies have proved data-flow testing is more effective than control-flow testing. However, data-flow testing still cannot find its adoption in practice, due to the lack of effective tool support. To this end, we propose a guided symbolic execution approach to efficiently search for program paths to satisfy data-flow coverage criteria. We implemented this approach on KLEE and evaluated with 30 program subjects which are constructed by the subjects used in previous data-flow testing literature, SIR, SV-COMP benchmarks. Moreover, we are planning to integrate the data-flow testing technique into the new proposed symbolic execution engine, SmartUnit, which is a cloud-based unit testing service for industrial software, supporting coverage-based testing. It has successfully helped several well-known corporations and institutions in China to adopt coverage-based testing in practice, totally tested more than one million lines of real code from industry.

Guozhu Meng, Yinxing Xue, Jing Kai Siow et al.

Data driven research on Android has gained a great momentum these years. The abundance of data facilitates knowledge learning, however, also increases the difficulty of data preprocessing. Therefore, it is non-trivial to prepare a demanding and accurate set of data for research. In this work, we put forward AndroVault, a framework for the Android research composing of data collection, knowledge representation and knowledge extraction. It has started with a long-running web crawler for data collection (both apps and description) since 2013, which guarantees the timeliness of data; With static analysis and dynamic analysis of the collected data, we compute a variety of attributes to characterize Android apps. After that, we employ a knowledge graph to connect all these apps by computing their correlation in terms of attributes; Last, we leverage multiple technologies such as logical inference, machine learning, and correlation analysis to extract facts (more accurate and demanding, either high level or not, data) that are beneficial for a specific research problem. With the produced data of high quality, we have successfully conducted many research works including malware detection, code generation, and Android testing. We would like to release our data to the research community in an authenticated manner, and encourage them to conduct productive research.