Pinpointing the location where a given unit of functionality—or feature—was implemented is a demanding and time-consuming task, yet prevalent in most software maintenance or evolution efforts. To that extent, we present PANGOLIN, an Eclipse plugin that helps developers identifying features among the source code. It borrows Spectrum-based Fault Localization techniques from the software diagnosis research field by framing feature localization as a diagnostic problem. PANGOLIN prompts users to label system executions based on feature involvement, and subsequently presents its spectrum-based feature localization analysis to users with the aid of a color-coded, hierarchic, and navigable visualization which was shown to be effective at conveying diagnostic information to users. Our evaluation shows that PANGOLIN accurately pinpoints feature implementations and is resilient to misclassifications by users.

Bruno Miguel Sotto-Mayor de Castro Machado

IST, University of Lisbon

Alexandre Perez

Palo Alto Research Center

Rui Abreu

Instituto Superior Técnico, U. Lisboa & INESC-ID

Portugal

Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework — DeepStellar — to pave the way for effective security and quality analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We demonstrated that, with DeepStellar, users are able to design efficient test generation tools, and develop effective adversarial sample detectors. We evaluated the developed applications on three real RNN models, including speech recognition and image classification.
DeepStellar outperforms existing approaches three hundred times in generating defect-triggering tests and achieves 97% accuracy in detecting adversarial attacks. A video demonstration which shows the main features of DeepStellar is available at https://sites.google.com/view/deepstellar/home/tool-demo.

Xiaoning Du

Nanyang Technological University

Xiaofei Xie

Nanyang Technological University

Yi Li

Nanyang Technological University

Singapore

Lei Ma

Kyushu University

Japan

Yang Liu

Nanyang Technological University, Singapore

Singapore

Jianjun Zhao

Kyushu University

Japan

Misuse of APIs happens frequently due to misunderstanding of API semantics and lack of documentation. An important category of API-related defects is the error handling defects, which may result in security and reliability flaws. These defects can be detected with the help of static program analysis, provided that error specifications are known. The error specification of an API function indicates how the function can fail. Writing error specifications manually is time-consuming and tedious. Therefore, automatic inferring the error specification from API usage code is preferred. In this paper, we present Ares, a tool for automatically inferring error specifications for C code through static analysis. we employ multiple heuristics to identify error handling blocks and infer error specifications by analyzing the corresponding condition logic. Ares is evaluated on 19 real world projects, and the results reveal that Ares outperforms the state-of-the-art tool APEx by 37% in precision. Ares can also identify more error specifications than APEx. Moreover, the specifications inferred from Ares help find dozens of API-related bugs in well-known projects such as OpenSSL, among them 10 bugs are confirmed by developers. Video: https://youtu.be/nf1QnFAmu8Q. Repository: https://github.com/lc3412/Ares.

Li Chi

Tsinghua University

Zuxing Gu

School of Software, Tsinghua University

China

Min Zhou

Tsinghua University

Ming Gu

Tsinghua University

Hongyu Zhang

The University of Newcastle

Australia

Modern software often exists in many different, yet similar versions and/or variants, usually derived from a common code base (e.g., via clone-and-own). In the context of product- line engineering, family-based-analysis has shown very promising potential for improving efficiency in applying quality-assurance techniques to variant-rich software, as compared to a variant- by-variant approach. Unfortunately, these strategies rely on an product-line representation superimposing all program variants in a syntactically well-formed, semantically sound and variant- preserving manner, which is manually hard to obtain in practice. We demonstrate the SiMPOSE methodology for automatically generating superimpositions of N given program versions and/or variants facilitating family-based analysis of variant-rich soft- ware. SiMPOSE is based on a novel N-way model-merging tech- nique operating at the level of control-flow automata (CFA) rep- resentations of C programs, CFAs constitute a unified program abstraction utilized by many recent software-analysis tools. We illustrate different merging strategies supported by SiMPOSE, namely variant-by-variant, N-way merging, incremental 2-way merging, and partition-based N/2-way merging, and demonstrate how SiMPOSE can be used to systematically compare their impact on efficiency and effectiveness of family-based unit-test generation. The SiMPOSE tool, the demonstration of its usage as well as related artifacts and documentation can be found at http://pi.informatik.uni-siegen.de/projects/variance/simpose.

Dennis Reuling

Software Engineering Group, University of Siegen

Germany

Udo Kelter

Software Engineering Group, University of Siegen

Sebastian Ruland

TU Darmstadt, Real-time Systems Lab

Malte Lochau

TU Darmstadt

Verification of programs continues to be a challenge and no single technique succeeds on all programs. In this paper we present VeriAbs, a reachability C program verifier that incorporates a portfolio of techniques implemented as four strategies, where a strategy is a set of techniques applied in a specific sequence. It selects a strategy based on the kind of loops in the program. We analysed the effectiveness of implemented strategies on the 3831 verification tasks from the ReachSafety category of the 8th International Competition on Software Verification (SV-COMP) 2019 and found that although simple classic techniques - explicit state model checking and bounded model checking, succeed on a majority of the programs, a wide range of further techniques are required to analyse the rest. A screencast of the tool is available at https://youtu.be/YHxB2V4U0gQ.

Mohammad Afzal

Tata Cosultancy Services

A Asia

Tata Cosultancy Services

Avriti Chauhan

Tata Cosultancy Services

Bharti Chimdyalwar

Tata Consultancy Services

Priyanka Darke

Tata Consultancy Services

Advaita Datar

Tata Consultancy Services Ltd

Shrawan Kumar

Tata Cosultancy Services

R Venkatesh

Tata Research Development and Design Centre

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support multiple built-in configurations which are easy to extend. We evaluated DeepHunter on two popular datasets and the results demonstrate the effectiveness of DeepHunter in achieving coverage increase and detecting real defects. A video demonstration which showcases the main features of DeepHunter can be found at https://youtu.be/s5DfLErcgrc.

Xiaofei Xie

Nanyang Technological University

Hongxu Chen

Nanyang Technological University

Yi Li

Nanyang Technological University

Singapore

Lei Ma

Kyushu University

Japan

Yang Liu

Nanyang Technological University, Singapore

Singapore

Jianjun Zhao

Kyushu University

Japan

Smart pointers are widely used to prevent memory errors in modern C++ code. However, improper usage of smart pointers may also lead to common memory errors, which makes the code not as safe as expected. To avoid smart pointer errors as early as possible, we present a coding style checker to detect possible bad smart pointer usages during compile time, and notify programmers about bug-prone behaviors. The evaluation indicates that the currently available state-of-the-art static code checkers can only detect 25 out of 116 manually inserted errors, while our tool can detect all these errors. And we also found 521 bugs among 8 open source projects with only 4 false positives.

Xutong Ma

Institute of Software, Chinese Academy of Sciences

Jiwei Yan

Institute of Software, Chinese Academy of Sciences

Yaqi Li

Institute of Software, Chinese Academy of Sciences

Jun Yan

Institute of Software, Chinese Academy of Sciences

Jian Zhang

Institute of Software, Chinese Academy of Sciences

The fragmentation issue spreads over multiple mobile platforms such as Android, iOS, mobile web and even WeChat, which hinders test scripts from running across platforms. To reduce the cost of adapting scripts for various platforms, some existing tools apply conventional computer vision techniques to replay the same script on multiple platforms automatically. However, because these solutions can hardly identify dynamic or similar widgets, it becomes difficult for engineers to apply them in practice. In this paper, we present an image-driven tool, namely LIRAT, to record and replay test scripts cross platforms, solving the problem of test script cross-platform replay for the first time. LIRAT records screenshots and layouts of the widgets, and leverages image understanding techniques to locate them in the replay process. Based on accurate widget localization, LIRAT supports replaying test scripts across devices and platforms. We employed LIRAT to replay 25 scripts from 5 application across 8 Android devices and 2 iOS devices. The results show that LIRAT can replay 88% scripts on Android platforms and 60% on iOS platforms.

Shengcheng Yu

Nanjing University, China

China

Chunrong Fang

Nanjing University

Yang Feng

University of California, Irvine

United States

Wenyuan Zhao

Nanjing University

Zhenyu Chen

Nanjing University

China

Workflow underlies most process automation software, such as those for product lines, business processes, and scientific computing. However, current Cloud Computing based workflow systems cannot support real-time applications due to network latency, which limits their application in many IoT systems such as smart healthcare and smart traffic. Fog Computing extends the Cloud by providing virtualized computing resources close to the End Devices so that the response time of accessing computing resources can be reduced significantly. However, how to most effectively manage heterogeneous resources and different computing tasks in the Fog is a big challenge. In this paper, we introduce “FogWorkflowSim” an efficient and extensible toolkit for automatically evaluating resource and task management strategies in Fog Computing with simulated user-defined workflow applications. Specifically, FogWorkflowSim is able to: 1) automatically set up a simulated Fog Computing environment for workflow applications; 2) automatically execute user submitted workflow applications; 3) automatically evaluate and compare the performance of different computation offloading and task scheduling strategies with three basic performance metrics, including time, energy and cost. FogWorkflowSim can serve as an effective experimental platform for researchers in Fog based workflow systems as well as practitioners interested in adopting Fog Computing and workflow systems for their new software projects.

Xiao Liu

School of Information Technology, Deakin University

Australia

Lingmin Fan

School of Computer Science and Technology, Anhui University

Jia Xu

School of Computer Science and Technology, Anhui University

Xuejun Li

School of Computer Science and Technology, Anhui University

Lina Gong

School of Computer Science and Technology, Anhui University

John Grundy

Monash University

Australia

Yun Yang

Swinburne University of Technology

Spreadsheets are widely used but subject to various defects. In this paper, we present SGUARD to effectively detect spreadsheet defects. SGUARD learns spreadsheet features to cluster cells with similar computational semantics, and then refine these clusters to recognize anomalous cells as defects. SGUARD well balances the trade-off between the precision (87.8%) and recall rate (71.9%) in the defect detection, and achieves an F-measure of 0.79, exceeding existing spreadsheet defect detection techniques. We introduce the SGUARD implementation and its usage by a video presentation (https://youtu.be/gNPmMvQVf5Q), and provide its download repository for public access (https://github.com/sheetguard/sguard).

Da Li

State Key Lab. for Novel Software Tech. and Dept. of Comp. Sci. and Tech., Nanjing University, Nanjing, China

Huiyan Wang

State Key Lab. for Novel Software Tech. and Dept. of Comp. Sci. and Tech., Nanjing University, Nanjing, China

Chang Xu

Nanjing University

China

Ruiqing Zhang

Search Tech. Center Asia, Microsoft, Suzhou, China

Shing-Chi Cheung

Department of Computer Science and Engineering, The Hong Kong University of Science and Technology

China

Xiaoxing Ma

State Key Lab. for Novel Software Tech. and Dept. of Comp. Sci. and Tech., Nanjing University

Floating-point arithmetic is widely used in applications from several fields including scientific computing, machine learning, graphics, and finance. Many of these applications are rapidly adopting the use of GPUs to speedup computations. GPUs, however, have limited support to detect floating-point exceptions, making it difficult to software developers to detect unexpected exceptional cases in such applications. We present FPChecker, the first tool to automatically detect floating-point exceptions in GPU applications. FPChecker uses the clang/LLVM compiler to instrument GPU kernels and to detect floating-point exceptions in GPUs at runtime. Once an exception is detected, it reports to the programmer the code location of the exception as well as other useful information. The programmer can then use this information to avoid the exception, e.g., by modifying the application algorithm or changing its input. We present the design of FPChecker, an evaluation of the overhead of the tool, and a real-world case scenario on which the tool is used to identify a hidden exception. The slowdown of FPChecker is moderate ($1.5\times$ on average) and the code is publicly available as open source.

Ignacio Laguna

Lawrence Livermore National Laboratory

This paper presents PMExec, a tool that supports the execution of partial UML-RT models. To this end, the tool implements the following steps: static analysis, automatic refinement, and input-driven execution. The static analysis respects the execution semantics of UML-RT models is used to detect problematic model elements, i.e., elements that cause problems during execution due to the partiality. Then, the models are refined automatically using model transformation techniques, which mostly add decision points where missing information can be supplied. Third, the refined models are executed, and when the execution reaches the decision points, input required to continue the execution is obtained either interactively or from a script that captures how to deal with partial elements. We have evaluated PMExec using several use-cases that show that the static analysis, refinement, and application of user input can be carried out with reasonable performance, and that the overhead of approach, due to the refinement step, is manageable. (https://youtu.be/BRKsselcMnc)

Mojtaba Bagherzadeh

Queen's University

Canada

Karim Jahed

Queen's University

Nafiseh Kahani

Queen's University

Juergen Dingel

Queen's University, Kingston, Ontario

Program debugging is a time-consuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrum-based fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored.

In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores.

Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, fine-grained data collection significantly outperforms coarse-grained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches.

According to our empirical study, we propose a new fault localization approach, PREDFL (Predicate-based Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a state-of-the-art fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of state-of-the-art fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top-1, which reveals that PREDFL complements existing techniques.

Jiajun Jiang

Peking University

China

Ran Wang

Peking University

Yingfei Xiong

Peking University

China

Xiangping Chen

Sun Yat-sen University

Lu Zhang

Peking University

China

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which cannot be handled by existing tools for diagnosing intra-process (thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Tarannum Shaila Zaman

University of Kentucky

Xue Han

University of Kentucky

Tingting Yu

University of Kentucky

United States

Localization of the root cases for unreproducible builds is an important yet challenging task during software maintenance. The major challenges lie in limited runtime traces from build processes and high diversity of build environments. To address these challenges, in this paper, we propose RepTrace, a framework that identifies the root causes for unreproducible builds based on collected system call traces of the executed build commands. Our framework leverages system call tracing’s uniform interfaces for monitoring executed build commands in diverse build environments. From the collected system call traces, causality analysis included in our framework builds a dependency graph starting from an inconsistent build artifact (across two builds) via two types of dependencies: read/write dependencies among processes and parent/child process dependencies, and searches the graph to find the processes that result in the inconsistencies. To handle massive noisy dependencies and uncertain parent/child dependencies, RepTrace includes two novel techniques: (1) using difference analysis on multiple builds to reduce the search space of read/write dependencies, and (2) computing similarity of the runtime values to filter out noisy parent/child process dependencies. The evaluation results of RepTrace over a set of real-world software packages show that \tool effectively finds not only the root cause commands responsible for the unreproducible builds, but also the files to patch for addressing the unreproducible issues. Among its Top-10 identified commands and files, RepTrace achieves high accuracy of 90.00% and 90.56% in identifying the root causes, respectively.

Zhilei Ren

Dalian University of Technology

Changlin Liu

Case Western Reserve University

Xusheng Xiao

Case Western Reserve University

United States

He Jiang

School of Software, Dalian University of Technology

China

Tao Xie

Peking University

China

PatchNet demonstrates a promising prospect of using machine learning to classify Linux kernel patches. It identifies bug-fixing patches, which should be back ported to long-term stable versions, from all patches in the mainline Linux kernel. This tool may greatly lighten the burden and reduce the omission of picking patches for maintainers. However, there are still some obstacles for engineering applications. We present PTracer, a Linux kernel patch trace bot based on an improved PatchNet. PTracer continuously monitors new patches in the git repository of the mainline Linux kernel, filters out unconcerned ones, classifies the rest as bug-fixing or non bug-fixing patches, and report bug-fixing patches to kernel experts of commercial operating systems. As a part of PTracer, kernel experts’ feedback information is collected and used to retrain the neural network periodically for improving performance. We use the patches in February 2019 of the mainline Linux kernel to perform the test. As a result, PTracer recommended 151 patches to CGEL kernel experts out of 5,142, 102 of which were accepted. Our PTracer is the first patch trace bot successfully applied to a commercial operating system and has the advantage of improving software quality and saving labor cost.

Yang Wen

ZTE Corporation

China

Jicheng Cao

ZTE Corporation

Shengyu Cheng

ZTE Corporation