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

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