Precise pointer analysis is desired since it is a core technique to find memory defects. There are several dimensions of pointer analysis precision, flow sensitivity, context sensitivity, field sensitivity and path sensitivity. For static analysis tools utilizing pointer analysis, considering all dimensions is difficult because the trade-off between precision and efficiency should be balanced. This paper presents TsmartGP, a static analysis tool for finding memory defects in C programs with a precise and efficient pointer analysis. The pointer analysis algorithm is flow, context, field, and quasi path sensitive. Control flow automatons are the key structures for our analysis to be flow sensitive. Function summaries are applied to get context information and elements of aggregate structures are handled to improve precision. Path conditions are used to filter unreachable paths. For efficiency, a multi-entry mechanism is proposed. Utilizing the pointer analysis algorithm, we implement a checker in TsmartGP to find uninitialized pointer errors in 13 real-world applications. Cppcheck and Clang Static Analyzer are chosen for comparison. The experimental results show that TsmartGP can find more errors while its accuracy rate is 100%, much higher than 3.1% for Cppcheck and 16.7% for Clang Static Analyzer. The demo video is available at https://youtu.be/IQlshemk6OA and TsmartGP can be downloaded from https://github.com/laoyaolandq/TsmartGP.

Yuexing Wang

Tsinghua University

Guang Chen

Tsinghua University

Min Zhou

Tsinghua University

Ming Gu

Tsinghua University

Jiaguang Sun

Tsinghua University

An enterprise system operates business by providing various services that are guided by set of certain business rules (BR) and constraints. These BR are usually written using plain Natural Language in operating procedures, terms and conditions, and other documents or in source code of legacy enterprise systems. For implementing the BR in a software system, expressing them as UML use-case specifications, or preparing for Merger & Acquisition (M&A) activity, analysts manually interpret the documents or try to identify constraints from the source code, leading to potential discrepancies and ambiguities. These issues in the software system can be resolved only after testing, which is a very tedious and expensive activity. To minimize such errors and efforts, we propose BuRRiTo framework consisting of automatic extraction of BR by mining documents and source code, ability to clean them of various anomalies like inconsistency, redundancies, conflicts, etc. and able to analyze the functional gaps present and performing semantic querying and searching.

Pavan Chittimalli

TCS Research

Kritika Anand

TCS Research

Shrishti Pradhan

TCS Research

Sayandeep Mitra

TCS Research

Chandan Prakash

TCS Research

India

Rohit Shere

TCS Research

Ravindra Naik

TCS Research, TRDDC, India

India

Programming is typically a difficult and repetitive task. Programmers encounter endless problems during programming, and they often need to write similar code over and over again. To prevent programmers from reinventing wheels thus increase their productivity, we propose a context-aware code-to-code recommendation tool named Lancer. With the support of a Library-Sensitive Language Model (LSLM) and the BERT model, Lancer is able to automatically analyze the intention of the incomplete code and recommend relevant and reusable code samples in real-time. code, and datasets can be found. A video demonstration of Lancer can be found at https://youtu.be/tO9nhqZY35g. Lancer is open source and the code is available at https://github.com/sfzhou5678/Lancer.

Shufan Zhou

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

Beijun Shen

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

Hao Zhong

Shanghai Jiao Tong University

China

We present TestCov, a tool for robust test-suite execution and test-coverage measurement on C programs. TestCov executes program tests in isolated containers to ensure system integrity and reliable resource control. The tool provides coverage statistics per test and for the whole test suite. TestCov uses the simple, XML-based exchange format for test-suite specifications that was established as standard by Test-Comp. TestCov has been successfully used in Test-Comp ’19 to execute almost 9 million tests on 1720 different programs. The source code of TestCov is released under the open-source license Apache 2.0 and available at https://gitlab.com/sosy-lab/software/test-suite-validator. A full artifact, including a demonstration video, is available at https://doi.org/10.5281/zenodo.3418726.

Dirk Beyer

LMU Munich

Germany

Thomas Lemberger

LMU Munich

Germany

We present Prema, a tool for Precise Requirement Editing, Modeling and Analysis. It can be used in various fields for describing precise requirements using formal notations and performing rigorous analysis. By parsing the requirements written in formal modeling language, Prema is able to get a model which aptly depicts the requirements. It also provides different rigorous verification and validation techniques to check whether the requirements meet users’ expectation and find potential errors. We show that our tool can provide a unified environment for writing and verifying requirements without using tools that are not well inter-related. For experimental demonstration, we use the requirements of the automatic train protection (ATP) system of CASCO signal co. LTD., the largest railway signal control system manufacturer of China. The code of the tool cannot be released here because the project is commercially confidential. However, a demonstration video of the tool is available at https://youtu.be/BX0yv8pRMWs.

Yihao Huang

East China Normal University

Jincao Feng

East China Normal University

Hanyue Zheng

East China Normal University

Jiayi Zhu

East China Normal University

Shang Wang

East China Normal University

Siyuan Jiang

Eastern Michigan University

Weikai Miao

Shanghai Key Lab for Trustworthy Computing, School of Computer Science and Software Engineering, East China Normal University

Geguang Pu

East China Normal University&Shanghai Trusted Industrial Control Platform Co., Ltd

Software engineering has seen much progress in recent past including introduction of new methodologies, new paradigms for software teams, and from smaller monolithic applications to complex, intricate, and distributed software applications. However, the way we represent, discuss, and collaborate on software applications throughout the software development life cycle is still primarily using the source code, textual representations, or charts on 2D computer screens - the confines of which have long limited how we visualize and comprehend software systems. In this paper, we present XRaSE, a novel prototype implementation that leverages augmented reality to visualize a software application as a virtually tangible entity. This immersive approach is aimed at making activities like application comprehension, architecture analysis, knowledge communication, and analysis of a software’s dynamic aspects, more intuitive, richer and collaborative. A video demonstration is available at https://youtu.be/_BYYnayA1FE.

Rohit Mehra

Accenture Labs, India

India

Vibhu Saujanya Sharma

Accenture Labs

Vikrant Kaulgud

Accenture Labs, India

India

Sanjay Podder

Accenture

India

The smart contract cannot be modified when it has been deployed on a blockchain. Therefore, it must be given thoroughly test before its being deployed. Mutation testing is considered as a practical test methodology to evaluate the adequacy of software testing. In this paper, we introduce MuSC, a mutation testing tool for Ethereum Smart Contract (ESC). It can generate numerous mutants at a fast speed and supports the automatic operations such as creating test nets, deploying and executing tests. Specially, MuSC implements a set of novel mutation operators w.r.t ESC programming language, Solidity. Therefore, it can expose the defects of smart contracts to a certain degree. The demonstration video of MuSC is available at https://youtu.be/3KBKXJPVjbQ, and the source code can be downloaded at https://github.com/belikout/MuSC-Tool-Demo-repo.

Zixin Li

Nanjing University

Haoran Wu

State Key Laboratory for Novel Software Technology, Nanjing University

Jiehui Xu

Nanjing University

Xingya Wang

State Key Laboratory for Novel Software Technology, Nanjing University

Lingming Zhang

The University of Texas at Dallas

United States

Zhenyu Chen

Nanjing University

China

Swarm-based verification methods split a verification problem into a large number of independent simpler tasks and so exploit the availability of large numbers of cores to speed up verification. Lazy-CSeq is a BMC-based bug-finding tool for C programs using POSIX threads that is based on sequentialization. Here we present the tool VeriSmart 2.0, which extends Lazy-CSeq with a swarm-based bug-finding method. The key idea of this approach is to constrain the interleavings such that context switches can only happen within selected tiles (more specifically, contiguous code segments within the individual threads). This under-approximates the program’s behaviors, with the number and size of tiles as additional parameters, which allows us to vary the complexity of the tasks. Overall, this significantly improves peak memory consumption and (wall-clock) analysis time.

Sources: http://www.cs.sun.ac.za/~bfischer/verismart.tgz

Bernd Fischer

Stellenbosch University

South Africa

Salvatore La Torre

Università degli Studi di Salerno

Italy

Gennaro Parlato

University of Molise

Deep neural networks~(DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks~(FNNs) and stateful recurrent neural networks~(RNNs). It not only enables to statically analyze the robustness of a DNN model against the input as a whole, but also allows to identify the vulnerable segments of a sequential input (e.g. audio input) by runtime analysis. It is worth noting that DeepMutation++ specially features the support of RNNs mutation testing. The tool demo video can be found on the project website https://sites.google.com/view/deepmutationpp.

Qiang Hu

Kyushu University, Japan

Lei Ma

Kyushu University

Japan

Xiaofei Xie

Nanyang Technological University

Bing Yu

Kyushu University, Japan

Yang Liu

Nanyang Technological University, Singapore

Singapore

Jianjun Zhao

Kyushu University

Japan

An effective way to maximize code coverage in software tests is through dynamic symbolic execution, a technique that uses constraint solving to systematically explore a program’s state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore’s flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore’s architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.

Mark Mossberg

Trail of Bits

Felipe Manzano

Trail of Bits

Eric Hennenfent

Trail of Bits

Alex Groce

Trail of Bits

Gustavo Grieco

Trail of Bits

Josselin Feist

Trail of Bits

Trent Brunson

Trail of Bits

Artem Dinaburg

Trail of Bits

Concurrency vulnerabilities are extremely harmful and can be frequently exploited to launch severe attacks. Due to the non-determinism of multithreaded executions, it is very difficult to detect them. Recently, data race detectors and techniques based on maximal casual model have been applied to detect concurrency vulnerabilities. However, the former are ineffective and the latter report many false negatives. In this paper, we present CONVUL, an effective tool for concurrency vulnerability detection. CONVUL is based on exchangeable events, and adopts novel algorithms to detect three major kinds of concurrency vulnerabilities. To illustrate the competitiveness of CONVUL, we performed a comparison with three widely-used data race detectors and one recent tool based on maximal casual model. In our experiments, CONVUL detected 9 of 10 known vulnerabilities and found 6 zero-day vulnerabilities on MySQL, while other tools only detected at most 3 out of these 16 vulnerabilities. Our tool and data are available at CONVUL web page: https://sites.google.com/site/convultool. A demonstration video is available at https://youtu.be/-26C6ULxtbk

Ruijie Meng

University of Chinese Academy of Sciences

Biyun Zhu

University of Chinese Academy of Sciences

Hao Yun

University of Chinese Academy of Sciences

Haicheng Li

University of Chinese Academy of Sciences

Yan Cai

Institute of Software, Chinese Academy of Sciences

China

Zijiang Yang

Western Michigan University

Model Driven Engineering (MDE) techniques raise the level of abstraction at which developers construct software. However, modern cyber-physical systems are becoming more prevalent and complex and hence software models that represent the structure and behavior of such systems still tend to be large and complex. These models may have numerous if not infinite possible behaviors, with complex communications between their components. Appropriate software testing techniques to generate test cases with high coverage rate to put these systems to test at the model-level (without the need to understand the underlying code generator or refer to the generated code) are therefore important. Concolic testing, a hybrid testing technique that benefits from both concrete and symbolic execution, gains a high execution coverage and is used extensively in the industry for program testing but not for software models. In this paper, we present a novel technique and its tool mCUTE1 , an open source 2 model-level concolic testing engine. We describe the implementation of our tool in the context of Papyrus-RT, an open source Model Driven Engineering (MDE) tool based on UML-RT, and report the results of validating our tool using a set of benchmark models.

Reza Ahmadi

Queen's University

Karim Jahed

Queen's University

Juergen Dingel

Queen's University, Kingston, Ontario

Tactics are the actions performed by self-adaptive systems that enable them to adapt to changes in their environments. For a self-adaptive cloud-based system, one tactic may include activating additional computing resources when response time thresholds are surpassed. In real-world environments, tactics will frequently experience tactic volatility. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics have static attributes. This limitation creates uncertainty in the decision-making process and may adversely impact the system’s ability to perform the most optimal action. Additionally, many self-adaptive processes do not properly anticipate or account for future occurrences and volatility in respect to the Service Level Agreement (SLA). This can limit the system’s ability to act proactively, especially when utilizing tactics that contain latency.

To address the limitation of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the time required to execute tactics and their associated costs. TVA also utilizes Autoregressive Integrated Moving Average (ARIMA) to perform time series forecasting allowing the system to proactively maintain requirements.

Jeffrey Palmerino

Rochester Institute of Technology

Qi Yu

Rochester Institute of Technology

Travis Desell

University of North Dakota

United States

Daniel Krutz

Rochester Institute of Technology

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding ‘test suites’ of CPS network attacks, without requiring any expertise in the system’s control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of actuators so as to drive the CPS into different unsafe physical states. We demonstrate the efficacy of smart fuzzing by implementing it for two real-world CPS testbeds—a water purification plant and a water distribution system—finding attacks that drive them into 27 different unsafe states involving water flow, pressure, and tank levels, including six that were not covered by an established attack benchmark. Finally, we use our approach to test the effectiveness of an invariant-based defence system for the water treatment plant, finding two attacks that were not detected by its physical invariant checks, highlighting a potential weakness that could be exploited in certain conditions.

Yuqi Chen

Singapore University of Technology and Design, Singapore

Chris Poskitt

Singapore University of Technology and Design

Singapore

Jun Sun

Singapore Management University, Singapore

Singapore

Sridhar Adepu

Singapore University of Technology and Design, Singapore

Singapore

Fan Zhang

Zhejiang University, Zhejiang Lab, and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China

Modern software systems, such as Cyber-Physical Systems (CPSs), often require interacting with environment or human. As environment and human are unpredictable or non-determinate, uncertainty is inevitable in such systems. In this paper, we proposed UncerTest to test such systems (i.e., CPSs in our context) with an explicit consideration of uncertainty, through test design, test generation, test optimization and test reporting. UncerTest is a model-based and search-based approach that rely on belief models — a test ready model with explicit subjective uncertainties. In UncerTest, we extended an uncertainty-wise test modeling framework (UncerTum) for designing and enabling of introduction of indeterminacy sources in the environment during test execution. To obtain tests, we first proposed an uncertainty-wise test generation with two strategies, designed for different coverage criteria of models, for enabling an automated test generations with uncertainties. In addition, we developed an uncertainty-wise test minimization to optimize the generated tests based on uncertainty related property using multi-objective search. We conducted an extensive empirical study with two phases to evaluate UncerTest with five use cases of two industrial CPS case studies. In the first phase, eight commonly used multi-objective search algorithms were selected for studying the best algorithm for each of the four minimization strategies. Next, the test cases obtained with UncerTest strategies (i.e., two generation strategies combined with four minimization strategies with the best algorithm) were executed on the two real CPSs for studying the performance of each UncerTest strategies. With the best strategy, we managed to observe 51% more uncertainties due to unknown indeterminate behaviors of the physical environments of the CPSs as compared to the other test strategies. In addition, with the same strategy, we managed to observe 118% more unknown uncertainties that are not specified in the test ready models.

Link to Publication: https://www.sciencedirect.com/science/article/pii/S0164121219300561

Man Zhang

Kristiania University

Shaukat Ali

Simula Research Lab

Tao Yue

Nanjing University of Aeronautics and Astronautics & Simula Research Laboratory

China

This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the last 25 years. These venues were divided, nearly evenly, between conferences and journals. An important aspect of this analysis is that it is fully automated and repeatable. To achieve that automation, we used topic modeling (with LDA) to mine 10 topics that represent much of the structure of contemporary SE. The 10 topics presented here should not be “set in stone” as the only topics worthy of study in SE. Rather our goal is to report that (a) text mining methods can detect large scale trends within our community; (b) those topic change with time; so (c) it is important to have automatic agents that can update our understanding of our community whenever new data arrives.

Link to Publication: https://ieeexplore.ieee.org/abstract/document/8465996

George Mathew

Department of Computer Science, North Carolina State University

Amritanshu Agrawal

Wayfair

United States

Tim Menzies

North Carolina State University

United States