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Articles 2551 - 2580 of 6848

Full-Text Articles in Physical Sciences and Mathematics

Efficient Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu Oct 2018

Efficient Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu

Research Collection School Of Computing and Information Systems

Traitor tracing scheme can be used to identify a decryption key is illegally used in public-key encryption. In CCS’13, Liu et al. proposed an attribute-based traitor tracing (ABTT) scheme with blackbox traceability which can trace decryption keys embedded in a decryption blackbox/device rather than tracing a well-formed decryption key. However, the existing ABTT schemes with blackbox traceability are based on composite order group and the size of the decryption key depends on the policies and the number of system users. In this paper, we revisit blackbox ABTT and introduce a new primitive called attribute-based set encryption (ABSE) based on key-policy …

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Hawkeye: Towards A Desired Directed Grey-Box Fuzzer, Hongxu Chen, Yinxing Xue, Yuekang Li, Bihuan Chen, Xiaofei Xie, Xiuheng Wu, Yang Liu Oct 2018

Hawkeye: Towards A Desired Directed Grey-Box Fuzzer, Hongxu Chen, Yinxing Xue, Yuekang Li, Bihuan Chen, Xiaofei Xie, Xiuheng Wu, Yang Liu

Research Collection School Of Computing and Information Systems

Grey-box fuzzing is a practically effective approach to test real-world programs. However, most existing grey-box fuzzers lack directedness, i.e. the capability of executing towards user-specified target sites in the program. To emphasize existing challenges in directed fuzzing, we propose Hawkeye to feature four desired properties of directed grey-box fuzzers. Owing to a novel static analysis on the program under test and the target sites, Hawkeye precisely collects the information such as the call graph, function and basic block level distances to the targets. During fuzzing, Hawkeye evaluates exercised seeds based on both static information and the execution traces to generate …

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Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua Oct 2018

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

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Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon Oct 2018

Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon

Research Collection School Of Computing and Information Systems

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code …

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Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw Oct 2018

Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, …

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March Of The Silent Bots, Paul Robert Griffin Oct 2018

March Of The Silent Bots, Paul Robert Griffin

MITB Thought Leadership Series

Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.

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Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low Oct 2018

Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low

MITB Thought Leadership Series

Disruptive innovation was identified as a phenomenon more than two decades ago by prominent Harvard scholar Clayton Christensen. So you may wonder why established industries are only now waking up to the prospect of digital transformation - the banking industry in particular.

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Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua Oct 2018

Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …

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Mixed-Reality For Object-Focused Remote Collaboration, Martin Feick, Anthony Tang, Scott Bateman Oct 2018

Mixed-Reality For Object-Focused Remote Collaboration, Martin Feick, Anthony Tang, Scott Bateman

Research Collection School Of Computing and Information Systems

In this paper we outline the design of a mixed-reality system to support object-focused remote collaboration. Here, being able to adjust collaborators' perspectives on the object as well as understand one another's perspective is essential to support effective collaboration over distance. We propose a low-cost mixed-reality system that allows users to: (1) quickly align and understand each other's perspective; (2) explore objects independently from one another, and (3) render gestures in the remote's workspace. In this work, we focus on the expert's role and we introduce an interaction technique allowing users to quickly manipulation 3D virtual objects in space.

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I4s: Capturing Shopper’S In-Store Interactions, Sougata Sen, Archan Misra, Vigneshwaran Subbaraju, Karan Grover, Meeralakshmi Radhakrishnan, Rajesh K. Balan, Youngki Lee Oct 2018

I4s: Capturing Shopper’S In-Store Interactions, Sougata Sen, Archan Misra, Vigneshwaran Subbaraju, Karan Grover, Meeralakshmi Radhakrishnan, Rajesh K. Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

In this paper, we present I4S, a system that identifies item interactions of customers in a retail store through sensor data fusion from smartwatches, smartphones and distributed BLE beacons. To identify these interactions, I4S builds a gesture-triggered pipeline that (a) detects the occurrence of “item picks”, and (b) performs fine-grained localization of such pickup gestures. By analyzing data collected from 31 shoppers visiting a mid-sized stationary store, we show that we can identify person-independent picking gestures with a precision of over 88%, and identify the rack from where the pick occurred with 91%+ precision (for popular racks).

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Sufat: An Analytics Tool For Gaining Insights From Student Feedback Comments, Siddhant Pyasi, Swapna Gottipati, Venky Shankararaman Oct 2018

Sufat: An Analytics Tool For Gaining Insights From Student Feedback Comments, Siddhant Pyasi, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Teacher evaluation is a vital element inimproving student learning outcomes. Course and instructor feedback given bystudents, provides insights that can help improve student learning outcomes andteaching quality. Teaching and course evaluation systems help to collectquantitative and qualitative feedback from students. Since manually analysingthe qualitative feedback is painstaking and a tedious process, usually, onlythe quantitative feedback is often used for evaluating the course and theinstructor. However, useful knowledge is hidden in the qualitative comments, inthe form of sentiments and suggestions that can provide valuable insights tohelp plan improvements in the course content and delivery. In order toefficiently gather, analyse and provide …

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Exploiting The Interdependency Of Land Use And Mobility For Urban Planning, Kasthuri Jayarajah, Andrew Tan, Archan Misra Oct 2018

Exploiting The Interdependency Of Land Use And Mobility For Urban Planning, Kasthuri Jayarajah, Andrew Tan, Archan Misra

Research Collection School Of Computing and Information Systems

Urban planners and economists alike have strong interest in understanding the inter-dependency of land use and people flow. The two-pronged problem entails systematic modeling and understanding of how land use impacts crowd flow to an area and in turn, how the influx of people to an area (or lack thereof) can influence the viability of business entities in that area. With cities becoming increasingly sensor-rich, for example, digitized payments for public transportation and constant trajectory tracking of buses and taxis, understanding and modelling crowd flows at the city scale, as well as, at finer granularity such as at the neighborhood …

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Inferring Trip Occupancies In The Rise Of Ride-Hailing Services, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Tuan-Anh Hoang Oct 2018

Inferring Trip Occupancies In The Rise Of Ride-Hailing Services, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Tuan-Anh Hoang

Research Collection School Of Computing and Information Systems

The knowledge of all occupied and unoccupied trips made by self-employed drivers are essential for optimized vehicle dispatch by ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However, the occupancy status of vehicles is not always known to the service operators due to adoption of multiple ride-hailing apps. In this paper, we propose a novel framework, Learning to INfer Trips (LINT), to infer occupancy of car trips by exploring characteristics of observed occupied trips. Two main research steps, stop point classification and structural segmentation, are included in LINT. In the stop point classification step, we represent a vehicle trajectory …

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Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy Oct 2018

Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention …

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Revisiting Supervised And Unsupervised Models For Effort-Aware Just-In-Time Defect Prediction, Qiao Huang, Xin Xia, David Lo Oct 2018

Revisiting Supervised And Unsupervised Models For Effort-Aware Just-In-Time Defect Prediction, Qiao Huang, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software changes with limited code inspection cost. Traditionally, supervised models have been used; however, they require sufficient labelled training data, which is difficult to obtain, especially for new projects. Recently, Yang et al. proposed an unsupervised model (i.e., LT) and applied it to projects with rich historical bug data. Interestingly, they reported that, under the same inspection cost (i.e., 20 percent of the total lines of code modified by all changes), it could find about 12% - 27% more defective changes than a state-of-the-art supervised model (i.e., EALR) when using …

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Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo Oct 2018

Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo

Research Collection School Of Computing and Information Systems

README files play an essential role in shaping a developer’s first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the ‘What’ and …

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Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe Oct 2018

Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe

Research Collection School Of Computing and Information Systems

Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for …

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Measuring Program Comprehension: A Large-Scale Field Study With Professionals, Xin Xia, Lingfeng Bao, David Lo, Zhengchang Xing, Ahmed E. Hassan, Shanping Li Oct 2018

Measuring Program Comprehension: A Large-Scale Field Study With Professionals, Xin Xia, Lingfeng Bao, David Lo, Zhengchang Xing, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

During software development and maintenance, developers spend a considerable amount of time on program comprehension activities. Previous studies show that program comprehension takes up as much as half of a developer's time. However, most of these studies are performed in a controlled setting, or with a small number of participants, and investigate the program comprehension activities only within the IDEs. However, developers' program comprehension activities go well beyond their IDE interactions. In this paper, we extend our ActivitySpace framework to collect and analyze Human-Computer Interaction (HCI) data across many applications (not just the IDEs). We follow Minelli et al.'s approach …

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Initializing Trust In Smart Devices Via Presence Attestation, Xuhua Ding, Gene Tsudik Oct 2018

Initializing Trust In Smart Devices Via Presence Attestation, Xuhua Ding, Gene Tsudik

Research Collection School Of Computing and Information Systems

Many personal computing and more specialized (e.g., high-end IoT) devices are now equipped with sophisticated processors that only a few years ago were present only on high-end desktops and servers. Such processors often include an important hardware security component in the form of a DRTM (Dynamic Root of Trust for Measurement) which initiates trust and resists software (and even some physical) attacks. However, despite substantial prior research on trust establishment with secure hardware, DRTM security was always considered without any involvement of the human user, who represents a vital missing link. This prompts an important challenge: how can a user …

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Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao Oct 2018

Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao

Research Collection School Of Computing and Information Systems

Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of5 moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt6 to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split7 into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a8 much larger memory space at the server side. In this paper, we present SIMkNN, a scalable and in-memory …

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Unearthing The X-Streams: Visualizing Water Contamination, Akangsha Bandalkul, Angad Srivastava, Kishan Bharadwaj Shridhar, Jason Guan Jie Ong, Yanrong Zhang Oct 2018

Unearthing The X-Streams: Visualizing Water Contamination, Akangsha Bandalkul, Angad Srivastava, Kishan Bharadwaj Shridhar, Jason Guan Jie Ong, Yanrong Zhang

Research Collection School Of Computing and Information Systems

The datasets released for VAST 2018 Mini Challenge 2 pertain to sensor readings capturing chemical concentrations and physical properties from water bodies in the Boonsong Lekagul wildlife preserve. This challenge is in continuation to the VAST 2017 Challenge, where the company Kasios was identified as the culprit in dumping the chemical - Methylosmoline. In the absence of actual chemical measurements in the soil, challenge participants need to visualize chemical contamination based on the proximal water bodies to identify trends of interest. A horizon plot developed helps to narrow down the complete list of 106 chemicals provided to only 7, …

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Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan Oct 2018

Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This paper shares our insights on exploring theexperiential learning model and risk management process todesign an undergraduate software architecture course. The keychallenge for undergraduate students to appreciate softwarearchitecture design is usually their limited experience in thesoftware industry. In software architecture, the high-level designprinciples are heuristics lacking the absoluteness of firstprinciples which for inexperienced undergraduate students, thisis a frustrating divergence from what they used to value. From aneducator's perspective, teaching software architecture requirescontending with the problem of how to express this level ofabstraction practically and also make the learning realistic. Inthis paper, we propose a model adapting the concepts ofexperiential learning …

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Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan Oct 2018

Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

Software architectures present high-level views ofsystems, enabling developers to abstract away the unnecessarydetails and focus on the overall big picture. Designing a softwarearchitecture is an essential skill in software engineering and adultlearners are seeking this skill to further progress in their career.With the technology revolution and advancements in this rapidlychanging world, the proportion of adult learners attendingcourses for continuing education are increasing. Their learningobjectives are no longer to obtain good grades but the practicalskills to enable them to perform better in their work and advancein their career. Teaching software architecture to upskill theseadult learners requires contending with the problem of …

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Investigating Multimodal Affect Sensing In An Affective Tutoring System Using Unobtrusive Sensors, Hua Leong Fwa, Lindsay Marshall Oct 2018

Investigating Multimodal Affect Sensing In An Affective Tutoring System Using Unobtrusive Sensors, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Affect inextricably plays a critical role in the learning process. In this study, we investigate the multimodal fusion of facial, keystrokes, mouse clicks, head posture and contextual features for the detection of student’s frustration in an Affective Tutoring System. The results (AUC=0.64) demonstrated empirically that a multimodal approach offers higher accuracy and better robustness as compared to a unimodal approach. In addition, the inclusion of keystrokes and mouse clicks makes up for the detection gap where video based sensing modes (facial and head postures) are not available. The findings in this paper will dovetail to our end research objective of …

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Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen Oct 2018

Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen

Research Collection School Of Computing and Information Systems

Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, …

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Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua Oct 2018

Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …

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A Lightweight Cloud Sharing Phr System With Access Policy Updating, Zuobin Ying, Wenjie Jang, Shuanlong Cao, Ximeng Liu, Jie Cui Oct 2018

A Lightweight Cloud Sharing Phr System With Access Policy Updating, Zuobin Ying, Wenjie Jang, Shuanlong Cao, Ximeng Liu, Jie Cui

Research Collection School Of Computing and Information Systems

The rapid development of smart wearable devices makes personal health management feasible, which also stimulates the evolution of personal health records (PHRs). However, PHRs face many security challenges ever since it has been created. Besides, the complicated policy adjusting operation makes the PHRs stored in the cloud not so easy to use. In this paper, we propose a lightweight PHRs system on the basis of attribute-based encryption with policy updating. To update an outsourced ciphertext PHRs in the cloud, PHRs owners only need to generate an updating key, then upload it to the cloud server instead of retrieving the entire …

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Visforum: A Visual Analysis System For Exploring User Groups In Online Forums, Siwei Fu, Yong Wang, Yi Yang, Qingqing Bi, Fangzhou Guo, Huamin Qu Oct 2018

Visforum: A Visual Analysis System For Exploring User Groups In Online Forums, Siwei Fu, Yong Wang, Yi Yang, Qingqing Bi, Fangzhou Guo, Huamin Qu

Research Collection School Of Computing and Information Systems

User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user …

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Pfix: Fixing Concurrency Bugs Based On Memory Access Patterns, Huarui Lin, Zan Wang, Shuang Liu, Jun Sun, Dongdi Zhang, Guangning Wei Sep 2018

Pfix: Fixing Concurrency Bugs Based On Memory Access Patterns, Huarui Lin, Zan Wang, Shuang Liu, Jun Sun, Dongdi Zhang, Guangning Wei

Research Collection School Of Computing and Information Systems

Concurrency bugs of a multi-threaded program may only manifest with certain scheduling, i.e., they are heisenbugs which are observed only from time to time if we execute the same program with the same input multiple times. They are notoriously hard to fix. In this work, we propose an approach to automatically fix concurrency bugs. Compared to previous approaches, our key idea is to systematically fix concurrency bugs by inferring locking policies from failure inducing memory-access patterns. That is, we automatically identify memory-access patterns which are correlated with the manifestation of the bug, and then conjecture what is the intended locking …

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Break The Dead End Of Dynamic Slicing: Localizing Data And Control Omission Bug, Yun Lin, Jun Sun, Lyly Tran, Guangdong Bai, Haijun Wang, Jin Song Dong Sep 2018

Break The Dead End Of Dynamic Slicing: Localizing Data And Control Omission Bug, Yun Lin, Jun Sun, Lyly Tran, Guangdong Bai, Haijun Wang, Jin Song Dong

Research Collection School Of Computing and Information Systems

Dynamic slicing is a common way of identifying the root cause when a program fault is revealed. With the dynamic slicing technique, the programmers can follow data and control flow along the program execution trace to the root cause. However, the technique usually fails to work on omission bugs, i.e., the faults which are caused by missing executing some code. In many cases, dynamic slicing over-skips the root cause when an omission bug happens, leading the debugging process to a dead end. In this work, we conduct an empirical study on the omission bugs in the Defects4J bug repository. Our …

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