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Articles 961 - 990 of 2211
Full-Text Articles in Software Engineering
Tracy: A Business-Driven Technical Debt Prioritization Framework, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza
Tracy: A Business-Driven Technical Debt Prioritization Framework, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza
Research Collection School Of Computing and Information Systems
Technical debt is a pervasive problem in software development. Software development teams have to prioritize debt items and determine whether they should address debt or develop new features at any point in time. This paper presents "Tracy", a framework for the prioritization of technical debt using a business-driven approach built on top of business processes. The current stage of the proposed framework is at the beginning of the third phase of Design Science Research, which is usually divided into the phases of exploration, engineering, and evaluation. The exploration and engineering phases involved the participation of 49 professionals from 12 different …
Nonuniform Timeslicing Of Dynamic Graphs Based On Visual Complexity, Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, Huamin Qu
Nonuniform Timeslicing Of Dynamic Graphs Based On Visual Complexity, Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, Huamin Qu
Research Collection School Of Computing and Information Systems
Uniform timeslicing of dynamic graphs has been used due to its convenience and uniformity across the time dimension. However, uniform timeslicing does not take the data set into account, which can generate cluttered timeslices with edge bursts and empty timeslices with few interactions. The graph mining filed has explored nonuniform timeslicing methods specifically designed to preserve graph features for mining tasks. In this paper, we propose a nonuni-form timeslicing approach for dynamic graph visualization. Our goal is to create timeslices of equal visual complexity. To this end, we adapt histogram equalization to create timeslices with a similar number of events, …
Sieve: Helping Developers Sift Wheat From Chaff Via Cross-Platform Analysis, Agus Sulistya, Gede A. A. P. Prana, David Lo, Christoph Treude
Sieve: Helping Developers Sift Wheat From Chaff Via Cross-Platform Analysis, Agus Sulistya, Gede A. A. P. Prana, David Lo, Christoph Treude
Research Collection School Of Computing and Information Systems
Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing cross-platform analysis. Our approach is based on transfer representation learning and word embedding, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related content. We first build a word …
New Challenges In Display-Saturated Environments, Mateusz Andrzej Mikusz, Tsu Wei Kenny Choo, Rajesh Krishna Balan, Nigel Davies, Youngki Lee
New Challenges In Display-Saturated Environments, Mateusz Andrzej Mikusz, Tsu Wei Kenny Choo, Rajesh Krishna Balan, Nigel Davies, Youngki Lee
Research Collection School Of Computing and Information Systems
We live in a world in which our physical spaces are becoming increasingly enriched with computing technology. Pervasive displays have been at the forefront of this progression and are now commonplace. In this paper, we focus on the natural end-point of this trend and consider the case when displays become truly ubiquitous and saturate our physical environments. We use as motivation a state-of-the-art display deployment in which mobile users navigating the space are simultaneously exposed to many hundreds of displays within their field of view and we highlight a number of new research challenges.
Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe
Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe
Research Collection School Of Computing and Information Systems
During software maintenance, developers have different information needs (e.g., to understand what type of maintenance activity to perform, the impact of a maintenance activity and its effort). However, information to support developers may be distributed across various sources. Furthermore, information captured in formal architecture documentation may be outdated. In this paper, we put forward a late breaking idea and outline a solution to improve the productivity of developers by providing task-specific recommendations based on concrete information needs that arise during software maintenance.
Parametric Timed Model Checking For Guaranteeing Timed Opacity, Étienne André, Jun Sun
Parametric Timed Model Checking For Guaranteeing Timed Opacity, Étienne André, Jun Sun
Research Collection School Of Computing and Information Systems
Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage is the ability for an attacker to deduce internal information depending on the system execution time. We address the following problem: given a timed system, synthesize the execution times for which one cannot deduce whether the system performed some secret behavior. We solve this problem in the setting of timed automata (TAs). We first provide a general solution, and then extend the problem to parametric TAs, by synthesizing internal timings making the TA secure. We study decidability, devise algorithms, and show that our …
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Research Collection School Of Computing and Information Systems
Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified …
Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh
Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh
Dissertations and Theses Collection (Open Access)
Over the past few years, deep learning has emerged as state-of-the-art solutions for many challenging computer vision tasks such as face recognition, object detection, etc. Despite of its outstanding performance, deep neural networks (DNNs) are computational intensive, which prevent them to be widely adopted on billions of mobile and embedded devices with scarce resources. To address that limitation, we
focus on building systems and optimization algorithms to accelerate those models, making them more computational-efficient.
First, this thesis explores the computational capabilities of different existing processors (or co-processors) on modern mobile devices. It recognizes that by leveraging the mobile Graphics Processing …
Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng
Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng
Research Collection School Of Computing and Information Systems
Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in …
A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
Research Collection School Of Computing and Information Systems
Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which …
Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra
Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra
Research Collection School Of Computing and Information Systems
We explore the use of personal ‘earable’ devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.
Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude
Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude
Research Collection School Of Computing and Information Systems
Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the …
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
Research Collection School Of Computing and Information Systems
No abstract provided.
Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele
Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Low-cost sensors, installed on mobile vehicles, provide a cost-effective way for fine-grained urban air pollution monitoring. However, frequent calibration is crucial for lowcost sensors to consistently deliver accurate measurements. Multi-hop calibration is a common practice to calibrate mobile sensor deployments, but is prone to severe error accumulation over hops. Prior research mitigates error accumulation by designing special calibration models, which only apply to linear models. In this paper, we propose an orthogonal approach by selecting reliable measurements for calibration at each hop. We analyze the impact of different data-induced uncertainties on calibration errors and devise a scheme to estimate these …
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS projects.
Locating Vulnerabilities In Binaries Via Memory Layout Recovering, Haijun Wang, Xiaofei Xie, Shang-Wei Lin, Yun Lin, Yuekang Li, Shengchao Qin, Yang Liu, Ting Liu
Locating Vulnerabilities In Binaries Via Memory Layout Recovering, Haijun Wang, Xiaofei Xie, Shang-Wei Lin, Yun Lin, Yuekang Li, Shengchao Qin, Yang Liu, Ting Liu
Research Collection School Of Computing and Information Systems
Locating vulnerabilities is an important task for security auditing, exploit writing, and code hardening. However, it is challenging to locate vulnerabilities in binary code, because most program semantics (e.g., boundaries of an array) is missing after compilation. Without program semantics, it is difficult to determine whether a memory access exceeds its valid boundaries in binary code. In this work, we propose an approach to locate vulnerabilities based on memory layout recovery. First, we collect a set of passed executions and one failed execution. Then, for passed and failed executions, we restore their program semantics by recovering fine-grained memory layouts based …
Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu
Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu
Research Collection School Of Computing and Information Systems
Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Research Collection School Of Computing and Information Systems
The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A highprecision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g., quantization, compression) before deployment to a target device (e.g., mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This …
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Research Collection School Of Computing and Information Systems
In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties inprocessing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships andFile similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects …
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …
Multiagent Decision Making And Learning In Urban Environments, Akshat Kumar
Multiagent Decision Making And Learning In Urban Environments, Akshat Kumar
Research Collection School Of Computing and Information Systems
Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents—from self-driving cars, ships to aerial drones—that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges—ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in largescale urban …
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
The advancements in software technology and data science are enabling Industry 4.0, aka the Fourth Industrial Revolution or the Industrial Internet of Things (IIoT). While the first three industrial revolutions have brought about immense change, the impact of Industry 4.0 will be much wider and far greater, especially with regard to the easily overlooked ethical and moral aspects. Widening wealth gaps between countries and among classes of people within countries, a potential growing unemployment rate, data privacy and accessibility issues, and the treatment of intelligent agents (e.g., military robots) present new and complex ethical and moral dilemmas. In this article, …
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two …
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Research Collection School Of Computing and Information Systems
No abstract provided.
Answerbot: An Answer Summary Generation Tool Based On Stack Overflow, Liang Cai, Haoye Wang, Bowen Xu, Qiao Huang, Xin Xia, David Lo, Zhenchang Xing
Answerbot: An Answer Summary Generation Tool Based On Stack Overflow, Liang Cai, Haoye Wang, Bowen Xu, Qiao Huang, Xin Xia, David Lo, Zhenchang Xing
Research Collection School Of Computing and Information Systems
The prevalence of questions and answers on domainspecific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details …
Sar: Learning Cross-Language Api Mappings With Little Knowledge, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Sar: Learning Cross-Language Api Mappings With Little Knowledge, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments. To minimize the need of parallel corpora, this paper aims at an automated approach that can map …
Practical And Effective Sandboxing For Linux Containers, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai
Practical And Effective Sandboxing For Linux Containers, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai
Research Collection School Of Computing and Information Systems
A container is a group of processes isolated from other groups via distinct kernel namespaces and resource allocation quota. Attacks against containers often leverage kernel exploits through the system call interface. In this paper, we present an approach that mines sandboxes and enables fine-grained sandbox enforcement for containers. We first explore the behavior of a container by running test cases and monitor the accessed system calls including types and arguments during testing. We then characterize the types and arguments of system call invocations and translate them into sandbox rules for the container. The mined sandbox restricts the container’s access to …
Network-Clustered Multi-Modal Bug Localization, Thong Hoang, Richard J. Oentaryo, Tien-Duy B. Le, David Lo
Network-Clustered Multi-Modal Bug Localization, Thong Hoang, Richard J. Oentaryo, Tien-Duy B. Le, David Lo
Research Collection School Of Computing and Information Systems
Developers often spend much effort and resources to debug a program. To help the developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been devised. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). While both techniques ultimately generate a ranked list of program elements that likely contain a bug, they only consider one source of information—either bug reports or program spectra— which is not optimal. In light of this deficiency, this paper presents a new approach dubbed Network-clustered …
Resource Constrained Deep Reinforcement Learning, Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar
Resource Constrained Deep Reinforcement Learning, Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking stations to reduce lost demand in shared mobility systems. Such problems are challenging owing to the demand uncertainty, combinatorial action spaces and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions). Existing systems typically employ myopic and …
The Impact Of Changes Mislabeled By Szz On Just-In-Time Defect Prediction, Yuanrui Fan, Xin Xia, Daniel A. Costa, David Lo, Ahmed E. Hassan, Shanping Li
The Impact Of Changes Mislabeled By Szz On Just-In-Time Defect Prediction, Yuanrui Fan, Xin Xia, Daniel A. Costa, David Lo, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
Just-in-Time (JIT) defect prediction—a technique which aims to predict bugs at change level—has been paid more attention. JIT defect prediction leverages the SZZ approach to identify bug-introducing changes. Recently, researchers found that the performance of SZZ (including its variants) is impacted by a large amount of noise. SZZ may considerably mislabel changes that are used to train a JIT defect prediction model, and thus impact the prediction accuracy. In this paper, we investigate the impact of the mislabeled changes by different SZZ variants on the performance and interpretation of JIT defect prediction models. We analyze four SZZ variants (i.e., B-SZZ, …