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

Context Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeling technique is Latent Dirichlet allocation. When running on different datasets, LDA suffers from “order effects”, i.e., different topics are generated if the order of training data is shuffled. Such order effects introduce a systematic error for any study. This error can relate to misleading results; specifically, inaccurate topic descriptions and a reduction in the efficacy of text mining classification results.

Objective To provide a method in which distributions generated by LDA are more stable and can be used for further analysis.

Method We use LDADE, a search-based software engineering tool which uses Differential Evolution (DE) to tune the LDA’s parameters. LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of Software Engineering (SE) papers, and software defect reports from NASA. Results were collected across different implementations of LDA (Python+Scikit-Learn, Scala+Spark) across Linux platform and for different kinds of LDAs (VEM, Gibbs sampling). Results were scored via topic stability and text mining classification accuracy.

Results In all treatments: (i) standard LDA exhibits very large topic instability; (ii) LDADE’s tunings dramatically reduce cluster instability; (iii) LDADE also leads to improved performances for supervised as well as unsupervised learning.

Conclusion Due to topic instability, using standard LDA with its “off-the-shelf” settings should now be depreciated. Also, in future, we should require SE papers that use LDA to test and (if needed) mitigate LDA topic instability. Finally, LDADE is a candidate technology for effectively and efficiently reducing that instability.

Link to Publication: https://www.sciencedirect.com/science/article/pii/S0950584917300861

Amritanshu Agrawal

Wayfair

United States

Wei Fu

Department of Computer Science, North Carolina State University

United States

Tim Menzies

North Carolina State University

United States

Systems-of-systems are formed by the composition of independently created software components. These components are designed to satisfy their individual requirements, rather than the global requirements of the systems-of-systems. We refer to components that cannot be adapted to meet both individual and global requirements as defiant components. In this paper, we propose a cautious adaptation approach which supports changing the behaviour of such defiant components under exceptional conditions to satisfy global requirements, while continuing to guarantee the satisfaction of the components’ individual requirements. The approach represents both normal and exceptional conditions as scenarios; models the behaviour of exceptional conditions as wrappers implemented using an aspect-oriented technique; and deals with both single and multiple instances of defiant components with different precedence order at runtime. We evaluated an implementation of the approach using drones and boats for an organ delivery application conceived by our industrial partners, in which we assess how the proposed approach helps achieve the system-of-systems’ global requirements while accommodating increased complexity of hybrid aspects such as multiplicity, precedence ordering, openness, and heterogeneity.

Paulo Maia

State University of Ceará

Brazil

Lucas Vieira

State University of Ceará

Brazil

Matheus Chagas

State University of Ceará

Yijun Yu

The Open University, UK

United Kingdom

Andrea Zisman

The Open University

Bashar Nuseibeh

The Open University (UK) & Lero (Ireland)

United Kingdom

Ensure the correctness of safety critical systems play a key role in the worldwide software engineer. Over the past yeas we have been helping CASCO Signal Ltd which is the Chinese biggest high speed railway company to develop high speed railway safety critical software. We have also contributed specific methods for develop better safety critical software, including a search-based model-driven software development approach which uses VIATRA Solver and UML diagram refinement method to construct Sysml model and use SAT solver to check the model. This talk aims at sharing the challenge of developing high speed railway safety critical system, what we learn from develop a safety critical software with a Chinese high speed railway company, and we use ZC subsystem as a case study to show the systematic model-driven safety critical software development method.

Zhiwei Wu

East China Normal University

Jing Liu

East China Normal University

Xiang Chen

CASCO Signal Ltd.

Architecture degradation has a strong negative impact on software quality and can result in significant losses. Severe software degradation does not happen overnight. Software evolves continuously, through numerous issues, fixing bugs and adding new features, and architecture flaws emerge quietly and largely unnoticed until they grow in scope and significance when the system becomes difficult to maintain. Developers are largely unaware of these flaws or the accumulating debt as they are focused on their immediate tasks of address individual issues. As a consequence, the cumulative impacts of their activities, as they affect the architecture, go unnoticed. To detect these problems early and prevent them from accumulating into severe ones, we propose to monitor software evolution by tracking the interactions among files revised to address issues. In particular, we propose and show how we can automatically detect active hotspot, to reveal architecture problems. We have empirically studied hundreds of hotspots along the evolution timelines of 21 open source projects, and showed that there exist just a few dominating active hotspots per project at any given time. Moreover, these dominating active hotspots persist over long time periods, and thus deserve special attention. Compared with state-of-the-art design and code smell detection tools we report that by using active hotspots it is possible to detect signs of software degradation both earlier and more precisely.

Qiong Feng

Drexel University

Yuanfang Cai

Drexel University

United States

Rick Kazman

University of Hawai‘i at Mānoa

Di Cui

Xi'an Jiaotong University

Ting Liu

Xi'an Jiaotong University

China

Hongzhou Fang

Drexel University

More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users’ preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)—which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.

Ilias Gerostathopoulos

Technical University of Munich

Germany

Stefan Kugele

Technical University of Munich

Christoph Segler

BMW Group Research, New Technologies, Innovations

Germany

Tomas Bures

Charles University, Czech Republic

Czech Republic

Alois Knoll

Technical University of Munich