Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations,
2020
Singapore Management University
Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations, Lili Quan, Qianyu Guo, Hongxu Chen, Xiaofei Xie, Xiaohong Li, Yang Liu, Jing Hu
Research Collection School Of Computing and Information Systems
The security assurance of SSL/TLS critically depends on the correct validation of X.509 certificates. Therefore, it is important to check whether a certificate is correctly validated by the SSL/TLS implementations. Although differential testing has been proven to be effective in finding semantic bugs, it still suffers from the following limitations: (1) The syntax of test cases cannot be correctly guaranteed. (2) Current test cases are not diverse enough to cover more implementation behaviours. This paper tackles these problems by introducing SADT, a novel syntax-aware differential testing framework for evaluating the certificate validation process in SSL/TLS implementations. We first propose a …
Generating Concept Based Api Element Comparison Using A Knowledge Graph,
2020
Singapore Management University
Generating Concept Based Api Element Comparison Using A Knowledge Graph, Yang Liu, Mingwei Liu, Xin Peng, Christoph Treude, Zhenchang Xing, Xiaoxin Zhang
Research Collection School Of Computing and Information Systems
Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary—a long list of recommendations only ranked by the model’s confidence. In this vision paper, we lay out our research agenda for re-imagining how recommender systems for software engineering communicate their insights to developers. When issuing recommendations, our aim is to recommend diverse rather than redundant solutions and present them in ways that highlight …
Adelaidecyc At Semeval-2020 Task 12: Ensemble Of Classifiers For Offensive Language Detection In Social Media,
2020
Singapore Management University
Adelaidecyc At Semeval-2020 Task 12: Ensemble Of Classifiers For Offensive Language Detection In Social Media, Mahen Herath, Thushari Atapattu, Hoang Anh Dung, Christoph Treude, Katrina Falkner
Research Collection School Of Computing and Information Systems
This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still …
Algorithms And Hardness Results For Computing Cores Of Markov Chains,
2020
Singapore Management University
Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Given a Markov chain M = (V,v0,δ), with state space V and a starting state v0, and a probability threshold ϵ, an ϵ-core is a subset C of states that is left with probability at most ϵ. More formally, C ⊆V is an ϵ-core, iff P reach(V\C) ≤ ϵ. Cores have been applied in a wide variety of verification problems over Markov chains, Markov decision processes, and probabilistic programs, as a means of discarding uninteresting and low-probability parts of a probabilistic system and instead being able to focus on the states that are likely to be encountered in a real-world …
Smartfuzz: An Automated Smart Fuzzing Approach For Testing Smartthings Apps,
2020
Singapore Management University
Smartfuzz: An Automated Smart Fuzzing Approach For Testing Smartthings Apps, Lwin Khin Shar, Nguyen Binh Duong Ta, Lingxiao Jiang, David Lo, Wei Minn, Kiah Yong Glenn Yeo, Eugene Kim
Research Collection School Of Computing and Information Systems
As IoT ecosystem has been fast-growing recently, there have been various security concerns of this new computing paradigm. Malicious IoT apps gaining access to IoT devices and capabilities to execute sensitive operations (sinks), e.g., controlling door locks and switches, may cause serious security and safety issues. Unlike traditional mobile/web apps, IoT apps highly interact with a wide variety of physical IoT devices and respond to environmental events, in addition to user inputs. It is therefore important to conduct comprehensive testing of IoT apps to identify possible anomalous behaviours. On the other hand, it is also important to optimize the number …
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization,
2020
Singapore Management University
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization, Anuradha Ravi, Archan Misra
Research Collection School Of Computing and Information Systems
We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types …
Refinements Of The Concept Of Privacy In Distributed Computing,
2020
Florida Institute of Technology
Refinements Of The Concept Of Privacy In Distributed Computing, Entisar Seedi Alshammry
Theses and Dissertations
In light of the tremendous development of technology in the modern world in which we live, privacy concerns are increasing, especially after the massive spread of distributed computing systems and the technologies that depend on it, whether in personal devices or public services. Hence, this research proposes refinements on the concept of privacy for enhancing the development of privacy-related strategies in distributed computing systems to address the elements of privacy. In particular, the study introduces the new concept of Privacy Appetite to describe and model the nature of the relationship between the intended disclosure of private information and gained value …
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction,
2020
University of Missouri-St. Louis
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger
Theses
Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …
Actor Concurrency Bugs: A Comprehensive Study On Symptoms, Root Causes, Api Usages, And Differences,
2020
Oakland University
Actor Concurrency Bugs: A Comprehensive Study On Symptoms, Root Causes, Api Usages, And Differences, Mehdi Bagherzadeh, Nicholas Fireman, Anas Shawesh, Raffi Khatchadourian
Publications and Research
Actor concurrency is becoming increasingly important in the development of real-world software systems. Although actor concurrency may be less susceptible to some multithreaded concurrency bugs, such as low-level data races and deadlocks, it comes with its own bugs that may be different. However, the fundamental characteristics of actor concurrency bugs, including their symptoms, root causes, API usages, examples, and differences when they come from different sources are still largely unknown. Actor software development can significantly benefit from a comprehensive qualitative and quantitative understanding of these characteristics, which is the focus of this work, to foster better API documentations, development practices, …
Reducing Estimation Bias Via Triplet-Average Deep Deterministic Policy Gradient,
2020
Beijing Institute of Technology
Reducing Estimation Bias Via Triplet-Average Deep Deterministic Policy Gradient, Dongming Wu, Xingping Dong, Jianbing Shen, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The overestimation caused by function approximation is a well-known property in Q-learning algorithms, especially in single-critic models, which leads to poor performance in practical tasks. However, the opposite property, underestimation, which often occurs in Q-learning methods with double critics, has been largely left untouched. In this article, we investigate the underestimation phenomenon in the recent twin delay deep deterministic actor-critic algorithm and theoretically demonstrate its existence. We also observe that this underestimation bias does indeed hurt performance in various experiments. Considering the opposite properties of single-critic and double-critic methods, we propose a novel triplet-average deep deterministic policy gradient algorithm that …
Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts,
2020
Singapore Management University
Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts, Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Minh Quang Tran
Research Collection School Of Computing and Information Systems
Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and …
Api Method Recommendation Via Explicit Matching Of Functionality Verb Phrases,
2020
Singapore Management University
Api Method Recommendation Via Explicit Matching Of Functionality Verb Phrases, Wenkai Xie, Xin Peng, Mingwei Liu, Christoph Treude, Zhenchang Xing, Xiaoxin Zhang, Wenyun Zhao
Research Collection School Of Computing and Information Systems
Due to the lexical gap between functionality descriptions and user queries, documentation-based API retrieval often produces poor results. Verb phrases and their phrase patterns are essential in both describing API functionalities and interpreting user queries. Thus we hypothesize that API retrieval can be facilitated by explicitly recognizing and matching between the fine-grained structures of functionality descriptions and user queries. To verify this hypothesis, we conducted a large-scale empirical study on the functionality descriptions of 14,733 JDK and Android API methods. We identified 356 different functionality verbs from the descriptions, which were grouped into 87 functionality categories, and we extracted 523 …
A Theory Of The Engagement In Open Source Projects Via Summer Of Code Programs,
2020
Singapore Management University
A Theory Of The Engagement In Open Source Projects Via Summer Of Code Programs, Jefferson Silva, Igor Wiese, Daniel M. German, Christoph Treude, Marco A. Gerosa, Igor Steinmacher
Research Collection School Of Computing and Information Systems
Summer of code programs connect students to open source software (OSS) projects, typically during the summer break from school. Analyzing consolidated summer of code programs can reveal how college students, who these programs usually target, can be motivated to participate in OSS, and what onboarding strategies OSS communities adopt to receive these students. In this paper, we study the well-established Google Summer of Code (GSoC) and devise an integrated engagement theory grounded in multiple data sources to explain motivation and onboarding in this context. Our analysis shows that OSS communities employ several strategies for planning and executing student participation, socially …
Beyond Accuracy: Assessing Software Documentation Quality,
2020
Singapore Management University
Beyond Accuracy: Assessing Software Documentation Quality, Christoph Treude, Justin Middleton, Thushari Atapattu
Research Collection School Of Computing and Information Systems
Good software documentation encourages good software engineering, but the meaning of “good” documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments—for example, reference documentation and README files excel in quality whereas blog articles have more problems—and we describe our vision for reasoning about software …
Perceptions, Expectations, And Challenges In Defect Prediction,
2020
Zhejiang University
Perceptions, Expectations, And Challenges In Defect Prediction, Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review, followed by a validation survey. We received 395 responses from practitioners. Some of our key findings include: 1) Over 90% of respondents are willing to adopt defect prediction techniques. 2) There …
Enhancing Developer Interactions With Programming Screencasts Through Accurate Code Extraction,
2020
Zhejiang University
Enhancing Developer Interactions With Programming Screencasts Through Accurate Code Extraction, Lingfeng Bao, Shengyi Pan, Zhenchang Xing, Xin Xia, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Programming screencasts have become a pervasive resource on the Internet, which is favoured by many developers for learning new programming skills. For developers, the source code in screencasts is valuable and important. However, the streaming nature of screencasts limits the choice that they have for interacting with the code. Many studies apply the Optical Character Recognition (OCR) technique to convert screen images into text, which can be easily searched and indexed. However, we observe that the noise in the screen images significantly affects the quality of OCRed code.In this paper, we develop a tool named psc2code, which has two components, …
Semi-Analytical Model For Design And Analysis Of On-Orbit Servicing Architecture,
2020
Singapore Management University
Semi-Analytical Model For Design And Analysis Of On-Orbit Servicing Architecture, Koki Ho, Hai Wang, Paul A. De Trempe, Tristan Sarton Du Jonchay, Kento Tomita
Research Collection School Of Computing and Information Systems
Robotic on-orbit servicing (OOS) is expected to be a key technology and concept for future sustainable space exploration. This paper develops a novel semi-analytical model for OOS system analysis, responding to the growing needs and ongoing trend of robotic OOS. An OOS infrastructure system is considered whose goal is to provide responsive services to the random failures of a set of customer modular satellites distributed in space (e.g., at the geosynchronous orbit). The considered OOS architecture comprises a servicer that travels and provides module-replacement services to the customer satellites, an on-orbit depot to store the spares, and a series of …
Machine Learning Integrated Design For Additive Manufacturing,
2020
Singapore Management University
Machine Learning Integrated Design For Additive Manufacturing, Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen
Research Collection School Of Computing and Information Systems
For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and …
Selecting Third-Party Libraries: The Practitioners' Perspective,
2020
Singapore Management University
Selecting Third-Party Libraries: The Practitioners' Perspective, Enrique Larios Vargas, Maurício Aniche, Christoph Treude, Magiel Bruntink, Georgios Gousios
Research Collection School Of Computing and Information Systems
The selection of third-party libraries is an essential element of virtually any software development project. However, deciding which libraries to choose is a challenging practical problem. Selecting the wrong library can severely impact a software project in terms of cost, time, and development effort, with the severity of the impact depending on the role of the library in the software architecture, among others. Despite the importance of following a careful library selection process, in practice, the selection of third-party libraries is still conducted in an ad-hoc manner, where dozens of factors play an influential role in the decision. In this …
Deep-Learning-Based App Sensitive Behavior Surveillance For Android Powered Cyber-Physical Systems,
2020
Singapore Management University
Deep-Learning-Based App Sensitive Behavior Surveillance For Android Powered Cyber-Physical Systems, Haoyu Ma, Jianwen Tian, Kefan Qiu, David Lo, Debin Gao, Daoyuan Wu, Chunfu Jia, Thar Baker
Research Collection School Of Computing and Information Systems
Android as an operating system is now increasingly being adopted in industrial information systems, especially with Cyber-Physical Systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicous applications targeting the platform. The existence of such malware threats therefore call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and IoT devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android …
