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

Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness via a user-study and its potential applications via a case study using real-world logs.

Lingfeng Bao

Institute of Information Engineering, Chinese Academy of Sciences

Nimrod Busany

Tel Aviv University

Israel

David Lo

Singapore Management University

Singapore

Shahar Maoz

Tel Aviv University

Israel

Execution logs record detailed runtime information of software systems and are used as the main data source for many tasks around software engineering. As modern software systems are evolving into large scale and complex structures, logs have become one type of fast-growing big data in industry. In particular, such logs often need to be stored for a long time in practice (e.g., a year), in order to analyze recurrent problems or track security issues. However, archiving logs consumes a large amount of storage space and computing resources, which in turn incurs high operational cost. Data compression is essential to reduce the cost of log storage. Traditional compression tools (e.g., gzip) work well for general texts, but are not tailed for execution logs. In this paper, we propose a novel and effective log compression method, namely logzip. Logzip is capable of extracting hidden structures from logs via fast iterative clustering and further generating coherent intermediate representations that can enable more effective compression. We evaluate logzip on five large log datasets of different types, with a total of 63.6 GB in size. The results show that, on average, logzip can save about half of the storage space over traditional compression tools. Meanwhile, the design of logzip is highly parallel and only incurs negligible overhead. In addition, we share the industrial experience of applying logzip in a global company.

Jinyang Liu

Sun Yat-Sen University

Jieming Zhu

Huawei Noah's Ark Lab

China

Shilin He

Chinese University of Hong Kong

China

Pinjia He

ETH Zurich

Switzerland

Zibin Zheng

Sun Yat-Sen University

Michael Lyu

The Chinese University of Hong Kong

Domain models are the most important asset in widely accepted software development approaches, like Domain-Driven Design (DDD), yet those models are still implicitly represented in programs. Model-Driven Engineering (MDE) regards those models as first-order citizens that are amenable to automated analysis and processing, facilitating quality assurance while increasing productivity in software development processes. Although this connection is not new, very few approaches facilitate adoption of MDE tooling without compromising existing value, their data. Moreover, switching to MDE tooling usually involves re-engineering core parts of an application, hindering backward compatibility and, thereby, continuous integration, while requiring an up-front investment in training in specialized modeling frameworks. In those approaches that overcome the previous problem, there is no clear indication - from a quantitative point of view - of the extent to which adopting state-of-the-art MDE practices and tooling is feasible or advantageous.

In this work, we advocate a code-first approach to modeling through an approach for applying MDE techniques and tools to existing object-oriented software applications that fully preserves the semantics of the original application, which need not be modified. Our approach consists both of a semi-automated method for specifying explicit view models out of existing object-oriented applications and of a conservative extension mechanism that enables the use of such view models at run time, where view model queries are resolved on demand and view model updates are propagated incrementally to the original application. This mechanism enables an iterative, flexible application of MDE tooling to software applications, where metamodels and models do not exist explicitly. An evaluation of this extension mechanism, implemented for Java applications and for view models atop the Eclipse Modeling Framework (EMF), has been conducted with an industry benchmark for decision support systems, analyzing performance and scalability of the synchronization mechanism. Backward propagation of large updates over very large views is instant.

Artur Boronat

University of Leicester

Many works infer finite-state models from execution logs. Large models are more accurate but also more difficult to present and understand. Small models are easier to present and understand but are less accurate. In this work we investigate the tradeoff between model size and accuracy in the context of the classic k-Tails model inference algorithm. First, we define mk-Tails, a generalization of k-Tails from one to many parameters, which enables fine-grained control over the tradeoff. Second, we extend mk-Tails with a reduction based on past-equivalence, which effectively reduces the size of the model without decreasing its accuracy. We implemented our work and evaluated its performance and effectiveness on models and generated logs from the literature.

Nimrod Busany

Tel Aviv University

Israel

Shahar Maoz

Tel Aviv University

Israel

Yehonatan Yulazari

Tel Aviv University

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