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Articles 2911 - 2940 of 8722
Full-Text Articles in Entire DC Network
Iquant: Interactive Quantitative Investment Using Sparse Regression Factors, Xuanwu Yue, Qiao Gu, Deyun Wang, Huamin Qu, Yong Wang
Iquant: Interactive Quantitative Investment Using Sparse Regression Factors, Xuanwu Yue, Qiao Gu, Deyun Wang, Huamin Qu, Yong Wang
Research Collection School Of Computing and Information Systems
The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of “good” factors …
Boosting Video Representation Learning With Multi-Faceted Integration, Zhaofan Qiu, Yao Ting, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei
Boosting Video Representation Learning With Multi-Faceted Integration, Zhaofan Qiu, Yao Ting, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei
Research Collection School Of Computing and Information Systems
Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the …
Hpress: A Hardware-Enhanced Proxy Re-Encryption Scheme Using Secure Enclave, Fan Zhang, Ziyuan Liang, Cong Zuo, Jun Shao, Jianting Ning, Jun Sun, Joseph K. Liu, Yibao Bao
Hpress: A Hardware-Enhanced Proxy Re-Encryption Scheme Using Secure Enclave, Fan Zhang, Ziyuan Liang, Cong Zuo, Jun Shao, Jianting Ning, Jun Sun, Joseph K. Liu, Yibao Bao
Research Collection School Of Computing and Information Systems
Proxy re-encryption (PRE) allows a proxy to transform one ciphertext to another under different encryption keys while keeping the underlying plaintext secret. Because of the ciphertext transformability of PRE, there are many potential private communicating applications of this feature. However, existing PRE schemes are not as full-fledged as expected. The lack of necessary features makes them hard to apply in real-world scenarios. So far, there does not exist a unidirectional multihop PRE scheme with constant decryption efficiency and constant ciphertext size without extensions. Impractical performance and weak scalability also hinder PRE from most real-world applications. In this work, we present …
Sql-Like Interpretable Interactive Video Search, Jiaxin Wu, Phuong Anh Nguyen, Zhixin Ma, Chong-Wah Ngo
Sql-Like Interpretable Interactive Video Search, Jiaxin Wu, Phuong Anh Nguyen, Zhixin Ma, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Concept-free search, which embeds text and video signals in a joint space for retrieval, appears to be a new state-of-the-art. However, this new search paradigm suffers from two limitations. First, the search result is unpredictable and not interpretable. Second, the embedded features are in high-dimensional space hindering real-time indexing and search. In this paper, we present a new implementation of the Vireo video search system (Vireo-VSS), which employs a dual-task model to index each video segment with an embedding feature in a low dimension and a concept list for retrieval. The concept list serves as a reference to interpret its …
Marrying Top-K With Skyline Queries: Relaxing The Preference Input While Producing Output Of Controllable Size, Kyriakos Mouratidis, Keming Li, Bo Tang
Marrying Top-K With Skyline Queries: Relaxing The Preference Input While Producing Output Of Controllable Size, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top-�� query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …
Efficient Conditional Gan Transfer With Knowledge Propagation Across Classes, Shahbazi. Mohamad, Zhiwu Huang, Huang, Danda Pani Paudel, Ajad Chhatkuli, Gool L. Van
Efficient Conditional Gan Transfer With Knowledge Propagation Across Classes, Shahbazi. Mohamad, Zhiwu Huang, Huang, Danda Pani Paudel, Ajad Chhatkuli, Gool L. Van
Research Collection School Of Computing and Information Systems
Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer …
Same File, Different Changes: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude
Same File, Different Changes: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude
Research Collection School Of Computing and Information Systems
Online collaboration platforms such as GitHub have provided software developers with the ability to easily reuse and share code between repositories. With clone-and-own and forking becoming prevalent, maintaining these shared files is important, especially for keeping the most up-to-date version of reused code. Different to related work, we propose the concept of meta-maintenance-i.e., tracking how the same files evolve in different repositories with the aim to provide useful maintenance opportunities to those files. We conduct an exploratory study by analyzing repositories from seven different programming languages to explore the potential of meta-maintenance. Our results indicate that a majority of active …
Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang
Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms …
An Empirical Study Of The Landscape Of Open Source Projects In Baidu, Alibaba, And Tencent, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
An Empirical Study Of The Landscape Of Open Source Projects In Baidu, Alibaba, And Tencent, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
Research Collection School Of Computing and Information Systems
Open source software has drawn more and more attention from researchers, developers and companies nowadays. Meanwhile, many Chinese technology companies are embracing open source and choosing to open source their projects. Nevertheless, most previous studies are concentrated on international companies such as Microsoft or Google, while the practical values of open source projects of Chinese technology companies remain unclear. To address this issue, we conduct a mixed-method study to investigate the landscape of projects open sourced by three large Chinese technology companies, namely Baidu, Alibaba, and Tencent (BAT). We study the categories and characteristics of open source projects, the developer's …
Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy
Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy
Research Collection School Of Computing and Information Systems
API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract …
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar
Research Collection School Of Computing and Information Systems
In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module's action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …
On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao
On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao
Research Collection School Of Computing and Information Systems
Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? …
Research Artifact: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude
Research Artifact: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude
Research Collection School Of Computing and Information Systems
This is a research artifact for the paper “Same File, Different Changes: The Potential of Meta-Maintenance on GitHub”. This artifact is a data repository including a list of studied 32,007 repositories on GitHub, a list of targeted 401,610,677 files, the results of the qualitative analysis for RQ2, RQ3, and RQ4, the results of the quantitative analysis for RQ5, and survey material for RQ6. The purpose of this artifact is enabling researchers to replicate our mixed-methods results of the paper, and to reuse the results of our exploratory study for further software engineering research. This research artifact is available at https://github.com/NAIST-SE/MetaMaintenancePotential …
The Shifting Sands Of Motivation: Revisiting What Drives Contributors In Open Source, Marco Gerosa, Igor Wiese, Bianca Trinkenreich, Georg Link, Gregorio Robles, Christoph Treude, Igor Steinmacher, Anita Sarma
The Shifting Sands Of Motivation: Revisiting What Drives Contributors In Open Source, Marco Gerosa, Igor Wiese, Bianca Trinkenreich, Georg Link, Gregorio Robles, Christoph Treude, Igor Steinmacher, Anita Sarma
Research Collection School Of Computing and Information Systems
Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field's understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals' personal growth …
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.
Tripdecoder: Study Travel Time Attributes And Route Preferences Of Metro Systems From Smart Card Data, Xiancai Tian, Baihua Zheng, Yazhe Wang, Hsao-Ting Huang, Chih-Cheng Hung
Tripdecoder: Study Travel Time Attributes And Route Preferences Of Metro Systems From Smart Card Data, Xiancai Tian, Baihua Zheng, Yazhe Wang, Hsao-Ting Huang, Chih-Cheng Hung
Research Collection School Of Computing and Information Systems
In this paper, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most …
Working With Smart Machines: Insights On The Future Of Work, Thomas H. Davenport, Steven M. Miller
Working With Smart Machines: Insights On The Future Of Work, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
In this article, we share our observations on how and why AI-based systems are being deployed. We look at how these systems have been integrated into existing and new work processes, especially the implications for the changing nature of work and how it will be conducted in future with AI-based smart machines. This will help companies that are in the earlier stages of considering, planning, or deploying these systems to know what to expect from recent developments in practice. We draw our analysis from 24 case studies that we have recently completed on AI system usage in actual operational settings.
Deeplight: Robust And Unobtrusive Real-Time Screen-Camera Communication For Real-World Displays, Vu Huy Tran, Gihan Jayatilaka, Ashwin Ashok, Archan Misra
Deeplight: Robust And Unobtrusive Real-Time Screen-Camera Communication For Real-World Displays, Vu Huy Tran, Gihan Jayatilaka, Ashwin Ashok, Archan Misra
Research Collection School Of Computing and Information Systems
The paper introduces a novel, holistic approach for robust Screen-Camera Communication (SCC), where video content on a screen is visually encoded in a human-imperceptible fashion and decoded by a camera capturing images of such screen content. We first show that state-of-the-art SCC techniques have two key limitations for in-the-wild deployment: (a) the decoding accuracy drops rapidly under even modest screen extraction errors from the captured images, and (b) they generate perceptible flickers on common refresh rate screens even with minimal modulation of pixel intensity. To overcome these challenges, we introduce DeepLight, a system that incorporates machine learning (ML) models in …
Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun
Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun
Research Collection School Of Computing and Information Systems
Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-based vulnerabilities, which casts a shadow on its applications. As smart contracts are unpatchable (due to the immutability of blockchain), it is essential that smart contracts are guaranteed to be free of vulnerabilities. Unfortunately, smart contract languages such as Solidity are Turing-complete, which implies that verifying them statically is infeasible. Thus, alternative approaches must be developed to provide the guarantee. In this work, we develop an approach …
Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi
Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi
Research Collection School Of Computing and Information Systems
Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks. As a result, they often only accommodate common features of those tasks but neglect each task's specific features. On the other hand, dynamic architecture methods can have a separate network for each task, but they are too expensive to train and not scalable in practice, especially in online settings. To address this problem, we propose a novel online continual learning method named ``Contextual Transformation Networks” (CTN) to efficiently model the \emph{task-specific features} while enjoying neglectable complexity overhead compared to …
Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua
Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint …
Low-Power Downlink For The Internet Of Things Using Ieee 802.11-Compliant Wake-Up Receivers, Johannes Blobel, Vu Huy Tran, Archan Misra, Falko Dressler
Low-Power Downlink For The Internet Of Things Using Ieee 802.11-Compliant Wake-Up Receivers, Johannes Blobel, Vu Huy Tran, Archan Misra, Falko Dressler
Research Collection School Of Computing and Information Systems
Ultra-low power communication is critical for supporting the next generation of battery-operated or energy harvesting battery-less Internet of Things (IoT) devices. Duty cycling protocols and wake-up receiver (WuRx) technologies, and their combinations, have been investigated as energy-efficient mechanisms to support selective, event-driven activation of devices. In this paper, we go one step further and show how WuRx can be used for an efficient and multi-purpose low power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also …
Androevolve: Automated Update For Android Deprecated-Api Usages, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Androevolve: Automated Update For Android Deprecated-Api Usages, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Research Collection School Of Computing and Information Systems
The Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of the Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the limitations of the state-of-the-art tool, CocciEvolve. AndroEvolve utilizes data flow analysis to solve the problem of out-of-method-boundary variables, and variable denormalization to remove the temporary variables introduced by CocciEvolve. We evaluated the accuracy of AndroEvolve using a dataset of …
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …
Characterization And Prediction Of Questions Without Accepted Answers On Stack Overflow, Mohamad Yazdaninia, David Lo, Ashkan Sami
Characterization And Prediction Of Questions Without Accepted Answers On Stack Overflow, Mohamad Yazdaninia, David Lo, Ashkan Sami
Research Collection School Of Computing and Information Systems
A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is …
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Research Collection School Of Computing and Information Systems
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by …
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Parachuri, Akshat Kumar
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Parachuri, Akshat Kumar
Research Collection School Of Computing and Information Systems
In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module’s action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …
A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding
A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding
Research Collection School Of Computing and Information Systems
Code instrumentation and hardware based event trapping are two primary approaches used in dynamic malware analysis systems. In this paper, we propose a new approach called Execution Flow Instrumentation (EFI) where the analyzer execution flow is interleaved with the target flow in user- and kernel-mode, at junctures flexibly chosen by the analyzer at runtime. We also propose OASIS as the system infrastructure to realize EFI with virtues of the current two approaches, however without their drawbacks. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as instrumentation code. We …
Automated Query Reformulation For Efficient Search Based On Query Logs From Stack Overflow, Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen
Automated Query Reformulation For Efficient Search Based On Query Logs From Stack Overflow, Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen
Research Collection School Of Computing and Information Systems
As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we …
How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude
How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude
Research Collection School Of Computing and Information Systems
Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated work-flows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of …