Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (239)
- Programming Languages and Compilers (198)
- Artificial Intelligence and Robotics (142)
- Engineering (142)
- Computer Engineering (126)
-
- Information Security (109)
- OS and Networks (80)
- Graphics and Human Computer Interfaces (78)
- Social and Behavioral Sciences (76)
- Numerical Analysis and Scientific Computing (59)
- Theory and Algorithms (46)
- Business (43)
- Computer and Systems Architecture (39)
- Digital Communications and Networking (39)
- Medicine and Health Sciences (33)
- Communication (30)
- Education (28)
- Systems Architecture (21)
- Health Information Technology (20)
- Sociology (18)
- Finance and Financial Management (15)
- Gerontology (15)
- Public Affairs, Public Policy and Public Administration (15)
- Higher Education (13)
- Social Media (13)
- Transportation (12)
- Technology and Innovation (11)
- Keyword
-
- Deep learning (54)
- Software engineering (49)
- Empirical study (43)
- Machine learning (35)
- Software (30)
-
- Android (29)
- Model Check (29)
- Collaboration (26)
- Deep Learning (24)
- GitHub (22)
- Stack Overflow (22)
- Fuzzing (21)
- Security (21)
- Testing (20)
- Data mining (19)
- Large language models (18)
- Programming (18)
- Software Engineering (18)
- Code search (17)
- Computer bugs (17)
- Codes (16)
- Empirical Study (16)
- Information retrieval (16)
- Large Language Models (16)
- Large language model (16)
- Software testing (15)
- Large Language Model (14)
- Linear Temporal Logic (14)
- Software maintenance (14)
- Vulnerability detection (14)
- Publication Year
- Publication
- Publication Type
Articles 1441 - 1470 of 2211
Full-Text Articles in Software Engineering
A Learning-To-Rank Based Fault Localization Approach Using Likely Invariants, Tien-Duy B. Le, David Lo, Claire Le Goues, Lars Grunske
A Learning-To-Rank Based Fault Localization Approach Using Likely Invariants, Tien-Duy B. Le, David Lo, Claire Le Goues, Lars Grunske
Research Collection School Of Computing and Information Systems
Debugging is a costly process that consumes much of developer time and energy. To help reduce debugging effort, many studies have proposed various fault localization approaches. These approaches take as input a set of test cases (some failing, some passing) and produce a ranked list of program elements that are likely to be the root cause of the failures (i.e., failing test cases). In this work, we propose Savant, a new fault localization approach that employs a learning-to-rank strategy, using likely invariant diffs and suspiciousness scores as features, to rank methods based on their likelihood to be a root cause …
Satisfiability Modulo Heap-Based Programs, Quang Loc Le, Jun Sun, Wei-Ngan Chin
Satisfiability Modulo Heap-Based Programs, Quang Loc Le, Jun Sun, Wei-Ngan Chin
Research Collection School Of Computing and Information Systems
In this work, we present a semi-decision procedure for a fragment of separation logic with user-defined predicates and Presburger arithmetic. To check the satisfiability of a formula, our procedure iteratively unfolds the formula and examines the derived disjuncts. In each iteration, it searches for a proof of either satisfiability or unsatisfiability. Our procedure is further enhanced with automatically inferred invariants as well as detection of cyclic proof. We also identify a syntactically restricted fragment of the logic for which our procedure is terminating and thus complete. This decidable fragment is relatively expressive as it can capture a range of sophisticated …
Proxy Signature With Revocation, Shengmin Xu, Guomin Yang, Yi Mu, Shu Ma
Proxy Signature With Revocation, Shengmin Xu, Guomin Yang, Yi Mu, Shu Ma
Research Collection School Of Computing and Information Systems
Proxy signature is a useful cryptographic primitive that allows signing right delegation. In a proxy signature scheme, an original signer can delegate his/her signing right to a proxy signer (or a group of proxy signers) who can then sign documents on behalf of the original signer. In this paper, we investigate the problem of proxy signature with revocation. The revocation of delegated signing right is necessary for a proxy signature scheme when the proxy signer’s key is compromised and/or any misuse of the delegated right is noticed. Although a proxy signature scheme usually specifies a delegation time period, it may …
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Passively Testing Routing Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee-Xian Tan, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Smart systems are today increasingly developed with the number of wireless sensor devices that drastically increases. They are implemented within several contexts through our environment. Thus, sensed data transported in ubiquitous systems are important and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed to wireless sensor networks (WSN). However, one stage that is often neglected before their deployment, is the conformance testing process, a crucial and challenging step. Active testing techniques commonly used in wired networks are not suitable to WSN and passive approaches are needed. While some works …
Cross-Modal Self-Taught Hashing For Large-Scale Image Retrieval, Liang Xie, Lei Zhu, Peng Pan, Yansheng Lu
Cross-Modal Self-Taught Hashing For Large-Scale Image Retrieval, Liang Xie, Lei Zhu, Peng Pan, Yansheng Lu
Research Collection School Of Computing and Information Systems
Cross-modal hashing integrates the advantages of traditional cross-modal retrieval and hashing, it can solve large-scale cross-modal retrieval effectively and efficiently. However, existing cross-modal hashing methods rely on either labeled training data, or lack semantic analysis. In this paper, we propose Cross-Modal Self-Taught Hashing (CMSTH) for large-scale cross-modal and unimodal image retrieval. CMSTH can effectively capture the semantic correlation from unlabeled training data. Its learning process contains three steps: first we propose Hierarchical Multi-Modal Topic Learning (HMMTL) to detect multi-modal topics with semantic information. Then we use Robust Matrix Factorization (RMF) to transfer the multi-modal topics to hash codes which are …
Stpp: Spatial-Temporal Phase Profiling Based Method For Relative Rfid Tag Localization, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Stpp: Spatial-Temporal Phase Profiling Based Method For Relative Rfid Tag Localization, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using radio frequency identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called spatial-temporal phase profiling (STPP) to RFID-based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating …
Practitioners' Expectations On Automated Fault Localization, Pavneet Singh Kochhar, Xin Xia, David Lo, Shanping Li
Practitioners' Expectations On Automated Fault Localization, Pavneet Singh Kochhar, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Software engineering practitioners often spend significant amount of time and effort to debug. To help practitioners perform this crucial task, hundreds of papers have proposed various fault localization techniques. Fault localization helps practitioners to find the location of a defect given its symptoms (e.g., program failures). These localization techniques have pinpointed the locations of bugs of various systems of diverse sizes, with varying degrees of success, and for various usage scenarios. Unfortunately, it is unclear whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by surveying 386 practitioners from more than 30 countries …
An Interference-Free Programming Model For Network Objects, Mischael Schill, Christopher M. Poskitt, Bertrand Meyer
An Interference-Free Programming Model For Network Objects, Mischael Schill, Christopher M. Poskitt, Bertrand Meyer
Research Collection School Of Computing and Information Systems
Network objects are a simple and natural abstraction for distributed object-oriented programming. Languages that support network objects, however, often leave synchronization to the user, along with its associated pitfalls, such as data races and the possibility of failure. In this paper, we present D-Scoop, a distributed programming model that allows for interference-free and transaction-like reasoning on (potentially multiple) network objects, with synchronization handled automatically, and network failures managed by a compensation mechanism. We achieve this by leveraging the runtime semantics of a multi-threaded object-oriented concurrency model, directly generalizing it with a message-based protocol for efficiently coordinating remote objects. We present …
Deepsense: A Gpu-Based Deep Convolutional Neural Network Framework On Commodity Mobile Devices, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee
Deepsense: A Gpu-Based Deep Convolutional Neural Network Framework On Commodity Mobile Devices, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Recently, a branch of machine learning algorithms called deep learning gained huge attention to boost up accuracy of a variety of sensing applications. However, execution of deep learning algorithm such as convolutional neural network on mobile processor is non-trivial due to intensive computational requirements. In this paper, we present our early design of DeepSense - a mobile GPU-based deep convolutional neural network (CNN) framework. For its design, we first explored the differences between server-class and mobile-class GPUs, and studied effectiveness of various optimization strategies such as branch divergence elimination and memory vectorization. Our results show that DeepSense is able to …
Fusing Wifi And Video Sensing For Accurate Group Detection In Indoor Spaces, Kasthuri Jayarajah, Zaman Lantra, Archan Misra
Fusing Wifi And Video Sensing For Accurate Group Detection In Indoor Spaces, Kasthuri Jayarajah, Zaman Lantra, Archan Misra
Research Collection School Of Computing and Information Systems
Understanding one's group context in indoor spaces is useful for many reasons - e.g., at a shopping mall, knowing a customer's group context can help in offering context-specific incentives, or estimating taxi demand for customers exiting the mall. Group detection and monitoring using WiFi-based indoor location traces fails when users are invisible (either because they don't carry smartphones, or because their WiFi is turned OFF) or when location tracking is inaccurate. In this paper, we propose a multi-modal group detection system that fuses two independent modes: video and WiFi, for detecting groups with low latency and high accuracy. We present …
How Long Will This Live? Discovering The Lifespans Of Software Engineering Ideas, Subhajit Datta, Santonu Sarkar, A. S. M Sajeev
How Long Will This Live? Discovering The Lifespans Of Software Engineering Ideas, Subhajit Datta, Santonu Sarkar, A. S. M Sajeev
Research Collection School Of Computing and Information Systems
We all want to be associated with long lasting ideas; as originators, or at least, expositors. For a tyro researcher or a seasoned veteran, knowing how long an idea will remain interesting in the community is critical in choosing and pursuing research threads. In the physical sciences, the notion of half-life is often evoked to quantify decaying intensity. In this paper, we study a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. In the absence of any consistent and well-accepted …
Demo: Drumming Application Using Commodity Wearable Devices, Bharat Dwivedi, Archan Misra, Youngki Lee
Demo: Drumming Application Using Commodity Wearable Devices, Bharat Dwivedi, Archan Misra, Youngki Lee
Research Collection School Of Computing and Information Systems
We aim to develop a drumming application in which individual can play drums using multiple wearable and mobile devices. Our vision is to tap out different rythms in the air using smart watches as a virtual drum stick and smart phone would act as a drum kit. Same user interface can be visualized in smart glasses. Here, our prime target is to use multiple commodity wearable devices (non-commodity i.e. Myo arm band) and smart phones for recognizing new (or same type of here) types of multi limb gestural context and building an adaptive application interface and allow such gesture recognition …
Demo: Gpu-Based Image Recognition And Object Detection On Commodity Mobile Devices, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Demo: Gpu-Based Image Recognition And Object Detection On Commodity Mobile Devices, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this demo, we show that it is feasible to execute CNN for vision sensing tasks directly on mobile devices by leveraging integrated GPU. We propose our design of DeepSense framework based on OpenCL to execute deep learning algorithms in energy-efficient and fast manner.
Demo: Multi-Device Gestural Interfaces, Tran Huy Vu, Youngki Lee, Archan Misra
Demo: Multi-Device Gestural Interfaces, Tran Huy Vu, Youngki Lee, Archan Misra
Research Collection School Of Computing and Information Systems
Varieties of wearable devices such as smart watches, Virtual/Augmented Reality devices (AR/VR) are much more affordable with interesting capabilities. In our vision, a person may use more than one devices at a time, and they form an eco-system of wearable devices. Therefore, we aim to build a system where an application expands its input and output among different devices, and adapts its input/output stream for different contexts.
Livelabs: Building In-Situ Mobile Sensing And Behavioural Experimentation Testbeds, Kasthuri Jayarajah, Rajesh Krishna Balan, Meera Radhakrishnan, Archan Misra, Youngki Lee
Livelabs: Building In-Situ Mobile Sensing And Behavioural Experimentation Testbeds, Kasthuri Jayarajah, Rajesh Krishna Balan, Meera Radhakrishnan, Archan Misra, Youngki Lee
Research Collection School Of Computing and Information Systems
In this paper, we present LiveLabs, a first-of-its-kind testbed that isdeployed across a university campus, convention centre, and resortisland and collects real-time attributes such as location, group contextetc., from hundreds of opt-in participants. These venues, data,and participants are then made available for running rich humancentricbehavioural experiments that could test new mobile sensinginfrastructure, applications, analytics, or more social-sciencetype hypotheses that influence and then observe actual user behaviour.We share case studies of how researchers from aroundthe world have and are using LiveLabs, and our experiences andlessons learned from building, maintaining, and expanding LiveLabsover the last three years.
Jasper: Sensing Gamers' Emotions Using Physiological Sensors, Sinh Huynh, Youngki Lee, Taiwoo Park, Rajesh Krishna Balan
Jasper: Sensing Gamers' Emotions Using Physiological Sensors, Sinh Huynh, Youngki Lee, Taiwoo Park, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
This paper aims to develop a system that evaluates the emotional experience of gamers based on physiological changes. A within-subject experiment with 22 participants has been designed to investigate the effects of difficulty level and social playing mode on player emotions and to examine the correlation between each emotion and the physiological changes. We demonstrate the feasibility of using commodity wearable physiological sensing devices to recognize mobile gamer's emotion. Specifically, our system performs 3-level excitement classification at an accuracy of 77.38% and binary classification of happiness state at an accuracy of 73.21%. These classification results show the potential of using …
Small Scale Deployment Of Seat Occupancy Detectors, Nguyen Huy Hoang Huy, Gihan Hettiarachchi, Youngki Lee, Rajesh Krishna Balan
Small Scale Deployment Of Seat Occupancy Detectors, Nguyen Huy Hoang Huy, Gihan Hettiarachchi, Youngki Lee, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In this paper, we present the results of a small-scale field deployment of our capacitance-based seat occupancy detector. We deployed our sensors to 36 seats in our university library and measured the performance of our system over a period of 8 weeks. As part of this deployment, we had to tackle numerous real-world deployment issues such as hardware failure, variations in signal quality, and interference caused by multiple objects in near proximity. We present our overall system design, along with the modifications we made to tackle various real-world problems. Finally, we present the results of our deployment which showed that …
Poster: A Device-Free Evaluation System For Gymnastics Using Passive Rfid Tags, Binbin Xie, Jie Xiong, Dingyi Fang, Xiaojiang Chen, Anwen Wang, Zhanyong Tang
Poster: A Device-Free Evaluation System For Gymnastics Using Passive Rfid Tags, Binbin Xie, Jie Xiong, Dingyi Fang, Xiaojiang Chen, Anwen Wang, Zhanyong Tang
Research Collection School Of Computing and Information Systems
No abstract provided.
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Research Collection School Of Computing and Information Systems
A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …
Demo: Wearable Application To Manage Problem Behavior In Children With Neurodevelopmental Disorders, Camellia Zakaria, Richard C. Davis
Demo: Wearable Application To Manage Problem Behavior In Children With Neurodevelopmental Disorders, Camellia Zakaria, Richard C. Davis
Research Collection School Of Computing and Information Systems
Managing problem behaviors in children with neurodevelopmental disorders can be challenging. Such behaviors may discourage social participation and learning. Many of these behaviors warrant intervention, however, are challenging for caregivers to constantly supervise. Previous work focused on developing recognition systems for stereotypical and aggressive behaviors. Researchers also developed visualization interface for caregivers to better understand their child’s needs. Our goal however, is to design an independent behavior management application to help children manage problem behaviors with minimal supervision.We conducted a field study at a school for children with special needs in Singapore, and interviewed ten teachers. This study helped us …
Demo: Ta$Ker: Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Thivya Kandappu, Randy Tandriansyah, Archan Misra
Demo: Ta$Ker: Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Thivya Kandappu, Randy Tandriansyah, Archan Misra
Research Collection School Of Computing and Information Systems
We design and develop TA$Ker, a real-world mobile crowd- sourcing platform to empirically study the worker responses to various task recommendation and selection strategies.
Cace: Exploiting Behavioral Interactions For Improved Activity Recognition In Multi-Inhabitant Smart Homes, Mohammad Arif Ul Alam, Nirmalya Roy, Archan Misra, Joseph Taylor
Cace: Exploiting Behavioral Interactions For Improved Activity Recognition In Multi-Inhabitant Smart Homes, Mohammad Arif Ul Alam, Nirmalya Roy, Archan Misra, Joseph Taylor
Research Collection School Of Computing and Information Systems
We propose CACE (Constraints And Correlations mining Engine) which investigates the challenges of improving the recognition of complex daily activities in multi-inhabitant smart homes, by better exploiting the spatiotemporal relationships across the activities of different individuals. We first propose and develop a loosely-coupled Hierarchical Dynamic Bayesian Network (HDBN), which both (a) captures the hierarchical inference of complex (macro-activity) contexts from lower-layer microactivity context (postural and improved oral gestural context), and (b) embeds the various types of behavioral correlations and constraints (at both micro-and macro-activity contexts) across the individuals. While this model is rich in terms of accuracy, it is computationally …
Demo: Smartwatch Based Shopping Gesture Recognition, Meeralakshmi Radhakrishnan, Sharanya Eswaran, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan
Demo: Smartwatch Based Shopping Gesture Recognition, Meeralakshmi Radhakrishnan, Sharanya Eswaran, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In the current retail segment, the retail store owners are keen to understand the browsing behavior and purchase pattern of the shoppers inside the physical stores. Profiling the behavior of the shopper is key to success for any marketing strategies that can optimize or personalize shopping-related services in real-time. We envision that exploiting the knowledge of real-time behavior of shopper’s in-store activities enables novel applications such as: (a) targeted advertising or recommendations: based on longer term shopper profiles, (b) proactive retail help to assist the shoppers who are confused in choosing between two items, (c) smart reminders that can remind …
Value-Inspired Elderly Care Service Design For Aging-In-Place, Na Liu, Sandeep Purao, Hwee-Pink Tan
Value-Inspired Elderly Care Service Design For Aging-In-Place, Na Liu, Sandeep Purao, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Most current projects aimed at in-home monitoring for the elderly appear to focus on demonstrating technical feasibility and ensuring safety. In doing so, they often overlook the complexity of the interactions between the elderly and the caregivers. This study explores this complexity by adopting a value-inspired design perspective. Following an action design method, we describe the (re)design of the system and service protocol for an elderly-home monitoring effort. The work requires that we leverage the capabilities (of the technological infrastructure system as well as the service providers) to reconcile the values held by the participants (the elderly and their caregivers). …
Indoor Location Error-Detection Via Crowdsourced Multi-Dimensional Mobile Data, Savina Singla, Archan Misra
Indoor Location Error-Detection Via Crowdsourced Multi-Dimensional Mobile Data, Savina Singla, Archan Misra
Research Collection School Of Computing and Information Systems
We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify …
Poster: Sonicnect: Accurate Hands-Free Gesture Input System With Smart Acoustic Sensing, Maotian Chang, Ping Li, Panlong Yang, Jie Xiong, Chang Tian
Poster: Sonicnect: Accurate Hands-Free Gesture Input System With Smart Acoustic Sensing, Maotian Chang, Ping Li, Panlong Yang, Jie Xiong, Chang Tian
Research Collection School Of Computing and Information Systems
This work presents Sonicnect, an acoustic sensing system with smartphone that enables accurate hands-free gesture input. Sonicnect leverages the embedded microphone in the smartphone to capture the subtle audio signals generated with fingers touching on the table. It supports 9 commonly used gestures (click, flip, scroll and zoom, etc) with above 92% recognition accuracy, and the minimum gesture movement could be 2cm. Distinguishable features are then extracted by exploiting spatio-temporal and frequency properties of the subtle audio signals. We conduct extensive real environment experiments to evaluate its performance. The results validate the effectiveness and robustness of Sonicnect.
Seeking Independent Management Of Problem Behavior: A Proof-Of-Concept Study With Children And Their Teachers, Camellia Zakaria, Richard C. Davis, Zachary Walker
Seeking Independent Management Of Problem Behavior: A Proof-Of-Concept Study With Children And Their Teachers, Camellia Zakaria, Richard C. Davis, Zachary Walker
Research Collection School Of Computing and Information Systems
Problem behaviors are particularly common in children with neurodevelopmental disorders like Autism and Down syndrome. These behaviors sometimes discourage social inclusion, inhibit learning development, and cause severe injuries, but caregivers are often unable to attend to their children immediately when the behaviors occur. Recent research shows that problem behavior can be automatically detected with wearable devices, but it is still not clear how to reduce caregivers' burdens and facilitate academic, social, and functional development of children with problem behaviors. We conducted a field study at a school with 21 children who exhibit problem behaviors and found that they needed frequent …
Poster: Android Whole-System Control Flow Analysis For Accurate Application Behavior Modeling, Huu Hoang Nguyen
Poster: Android Whole-System Control Flow Analysis For Accurate Application Behavior Modeling, Huu Hoang Nguyen
Research Collection School Of Computing and Information Systems
Android, the modern operating system for smartphones, together with its millions of apps, has become an important part of human life. There are many challenges to analyzing them. It is important to model the mobile systems in order to analyze the behaviors of apps accurately. These apps are built on top of interactions with Android systems. We aim to automatically build abstract models of the mobile systems and thus automate the analysis of mobile applications and detect potential issues (e.g., leaking private data, causing unexpected crashes, etc.). The expected results will be the accuracy models of actual various versions of …
Condensing Class Diagrams With Minimal Manual Labeling Cost, Xinli Yang, David Lo, Xin Xia, Jianling Sun
Condensing Class Diagrams With Minimal Manual Labeling Cost, Xinli Yang, David Lo, Xin Xia, Jianling Sun
Research Collection School Of Computing and Information Systems
Traditionally, to better understand the design of a project, developers can reconstruct a class diagram from source code using a reverse engineering technique. However, the raw diagram is often perplexing because there are too many classes in it. Condensing the reverse engineered class diagram into a compact class diagram which contains only the important classes would enhance the understandability of the corresponding project. A number of recent works have proposed several supervised machine learning solutions that can be used for condensing reverse engineered class diagrams given a set of classes that are manually labeled as important or not. However, a …
Automated Identification Of High Impact Bug Reports Leveraging Imbalanced Learning Strategies, Xinli Yang, David Lo, Qiao Huang, Xin Xia, Jianling Sun
Automated Identification Of High Impact Bug Reports Leveraging Imbalanced Learning Strategies, Xinli Yang, David Lo, Qiao Huang, Xin Xia, Jianling Sun
Research Collection School Of Computing and Information Systems
In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resource, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high impact bugs are used to refer to the bugs which appear in unexpected time or locations and bring more unexpected effects, or break pre-existing functionalities and destroy the user experience. Unfortunately, identifying high impact bugs from the thousands of bug reports in a bug tracking system is not an easy feat. Thus, an …