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Articles 1321 - 1350 of 4266
Full-Text Articles in Entire DC Network
Just-In-Time Defect Identification And Localization: A Two-Phase Framework, Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, Shanping Li
Just-In-Time Defect Identification And Localization: A Two-Phase Framework, Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Defect localization aims to locate buggy program elements (e.g., buggy files, methods or lines of code) based on defect symptoms, e.g., bug reports or program spectrum. However, when we receive the defect symptoms, the defect has been exposed and negative impacts have been introduced. Thus, one challenging task is: whether we can locate buggy program prior to appearance of the defect symptom at an early time (e.g., when buggy program elements are being checked-in). We refer to this type of defect localization as “Just-In-Time (JIT) Defect localization”. Although many prior studies have proposed various JIT defect identification methods to identify …
Action-Centric Relation Transformer Network For Video Question Answering, Jipeng Zhang, Jie Shao, Rui Cao, Lianli Gao, Xing Xu, Heng Tao Shen
Action-Centric Relation Transformer Network For Video Question Answering, Jipeng Zhang, Jie Shao, Rui Cao, Lianli Gao, Xing Xu, Heng Tao Shen
Research Collection School Of Computing and Information Systems
Video question answering (VideoQA) has emerged as a popular research topic in recent years. Enormous efforts have been devoted to developing more effective fusion strategies and better intra-modal feature preparation. To explore these issues further, we identify two key problems. (1) Current works take almost no account of introducing action of interest in video representation. Additionally, there exists insufficient labeling data on where the action of interest is in many datasets. However, questions in VideoQA are usually action-centric. (2) Frame-to-frame relations, which can provide useful temporal attributes (e.g., state transition, action counting), lack relevant research. Based on these observations, we …
"More Than Deep Learning": Post-Processing For Api Sequence Recommendation, Chi Chen, Xin Peng, Bihuan Chen, Jun Sun, Zhenchang Xing, Xin Wang, Wenyun Zhao
"More Than Deep Learning": Post-Processing For Api Sequence Recommendation, Chi Chen, Xin Peng, Bihuan Chen, Jun Sun, Zhenchang Xing, Xin Wang, Wenyun Zhao
Research Collection School Of Computing and Information Systems
In the daily development process, developers often need assistance in finding a sequence of APIs to accomplish their development tasks. Existing deep learning models, which have recently been developed for recommending one single API, can be adapted by using encoder-decoder models together with beam search to generate API sequence recommendations. However, the generated API sequence recommendations heavily rely on the probabilities of API suggestions at each decoding step, which do not take into account other domain-specific factors (e.g., whether an API suggestion satisfies the program syntax and how diverse the API sequence recommendations are). Moreover, it is difficult for developers …
Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt
Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Novice programmers often struggle with problem solving due to the high cognitive loads they face. Furthermore, many introductory programming courses do not explicitly teach it, assuming that problem solving skills are acquired along the way. In this paper, we present 'PCDIT', a non-linear problem solving framework that provides scaffolding to guide novice programmers through the process of transforming a problem specification into an implemented and tested solution for an imperative programming language. A key distinction of PCDIT is its focus on developing concrete cases for the problem early without actually writing test code: students are instead encouraged to think about …
Authenticated Data Redaction With Accountability And Transparency, Jinhua Ma, Xinyi Huang, Yi Mu, Robert H. Deng
Authenticated Data Redaction With Accountability And Transparency, Jinhua Ma, Xinyi Huang, Yi Mu, Robert H. Deng
Research Collection School Of Computing and Information Systems
A common practice in data redaction is removing sensitive information prior to data publication or release. In data-driven applications, one must be convinced that the redacted data is still trustworthy. Meanwhile, the data redactor must be held accountable for (malicious) redaction, which could change/hide the meaning of the original data. Motivated by these concerns, we present a novel solution for authenticated data redaction based on a new Redactable Signature Scheme with Implicit Accountability (RSS - IA). In the event of a dispute, not only the original data signer but also the redactor can generate an evidence tag to unequivocally identify …
Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Research Collection School Of Computing and Information Systems
Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important …
On The Reproducibility And Replicability Of Deep Learning In Software Engineering, Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, Xiaohu Yang
On The Reproducibility And Replicability Of Deep Learning In Software Engineering, Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge.Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility—whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability—whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a …
Codematcher: Searching Code Based On Sequential Semantics Of Important Query Words, Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li
Codematcher: Searching Code Based On Sequential Semantics Of Important Query Words, Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that …
Just-In-Time Defect Prediction On Javascript Projects: A Replication Study, Chao Ni, Xin Xia, David Lo, Xiaohu Yang, Ahmed E. Hassan
Just-In-Time Defect Prediction On Javascript Projects: A Replication Study, Chao Ni, Xin Xia, David Lo, Xiaohu Yang, Ahmed E. Hassan
Research Collection School Of Computing and Information Systems
Change-level defect prediction is widely referred to as just-in-time (JIT) defect prediction since it identifies a defect-inducing change at the check-in time, and researchers have proposed many approaches based on the language-independent change-level features. These approaches can be divided into two types: supervised approaches and unsupervised approaches, and their effectiveness has been verified on Java or C++ projects. However, whether the language-independent change-level features can effectively identify the defects of JavaScript projects is still unknown. Additionally, many researches have confirmed that supervised approaches outperform unsupervised approaches on Java or C++ projects when considering inspection effort. However, whether supervised JIT defect …
Correlating Automated And Human Evaluation Of Code Documentation Generation Quality, Xing Hu, Qiuyuan Chen, Haoye Wang, Xin Xia, David Lo, Thomas Zimmermann
Correlating Automated And Human Evaluation Of Code Documentation Generation Quality, Xing Hu, Qiuyuan Chen, Haoye Wang, Xin Xia, David Lo, Thomas Zimmermann
Research Collection School Of Computing and Information Systems
Automatic code documentation generation has been a crucial task in the field of software engineering. It not only relieves developers from writing code documentation but also helps them to understand programs better. Specifically, deep-learning-based techniques that leverage large-scale source code corpora have been widely used in code documentation generation. These works tend to use automatic metrics (such as BLEU, METEOR, ROUGE, CIDEr, and SPICE) to evaluate different models. These metrics compare generated documentation to reference texts by measuring the overlapping words. Unfortunately, there is no evidence demonstrating the correlation between these metrics and human judgment. We conduct experiments on two …
Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng
Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware …
Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren
Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren
Research Collection School Of Computing and Information Systems
In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, …
Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu
Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu
Research Collection School Of Computing and Information Systems
Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good …
An Exploratory Study On The Repeatedly Shared External Links On Stack Overflow, Jiakun Liu, Haoxiang Zhang, Xin Xia, David Lo, Ying Zou, Ahmed E. Hassan, Shanping Li
An Exploratory Study On The Repeatedly Shared External Links On Stack Overflow, Jiakun Liu, Haoxiang Zhang, Xin Xia, David Lo, Ying Zou, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources …
Orchestration Or Automation: Authentication Flaw Detection In Android Apps, Siqi Ma, Juanru Li, Surya Nepal, Diethelm Ostry, David Lo, Sanjay K. Jha, Robert H. Deng, Elisa Bertino
Orchestration Or Automation: Authentication Flaw Detection In Android Apps, Siqi Ma, Juanru Li, Surya Nepal, Diethelm Ostry, David Lo, Sanjay K. Jha, Robert H. Deng, Elisa Bertino
Research Collection School Of Computing and Information Systems
Passwords are pervasively used to authenticate users' identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/TLS-based PAP and timestamp-based PAP. Previously, we proposed an automated tool, GLACIATE, to detect authentication flaws. We were curious whether orchestration (i.e., involving manual-effort) works better than automation. To answer this question, we propose an orchestrated approach, AUTHEXPLOIT and compare its effectiveness GLACIATE. We study requirements for correct implementation of PAP …
Accessibility In Software Practice: A Practitioner's Perspective, Tingting Bi, Xin Xia, David Lo, John C. Grundy, Thomas Zimmermann, Denae Ford
Accessibility In Software Practice: A Practitioner's Perspective, Tingting Bi, Xin Xia, David Lo, John C. Grundy, Thomas Zimmermann, Denae Ford
Research Collection School Of Computing and Information Systems
Being able to access software in daily life is vital for everyone, and thus accessibility is a fundamental challenge for software development. However, given the number of accessibility issues reported by many users, e.g., in app reviews, it is not clear if accessibility is widely integrated into current software projects and how software projects address accessibility issues. In this article, we report a study of the critical challenges and benefits of incorporating accessibility into software development and design. We applied a mixed qualitative and quantitative approach for gathering data from 15 interviews and 365 survey respondents from 26 countries across …
Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski
Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski
Research Collection School Of Computing and Information Systems
Many instructors in computing and HCI disciplines use hands-on activities for teaching and training new skills. Beyond simply teaching hands-on skills like sketching and programming, instructors also use these activities so students can acquire tacit skills. Yet, current video-conferencing technologies may not effectively support hands-on activities in online teaching contexts. To develop an understanding of the inadequacies of current video-conferencing technologies for hands-on activities, we conducted 15 interviews with university-level instructors who had quickly pivoted their use of hands-on activities to an online context during the early part of the COVID-19 pandemic. Based on our analysis, we uncovered four pedagogical …
A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng
A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng
Research Collection School Of Computing and Information Systems
Aiming for minimizing passengers’ waiting time and vehicles’ vacancy rate, online car-hailing service systems with fog computing has been deployed in various scenarios. In this paper, we focus on addressing the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) private, finegrained and bilateral order matching between passengers and drivers; (2) authenticity verification of passengers’ orders in the form of ciphertext, and (3) temporal assurance of passengers’ ciphertext orders. To the best of our knowledge, no previous system has been designed to meet all three requirements. Existing …
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Cross-modal recipe retrieval has recently been explored for food recognition and understanding. Text-rich recipe provides not only visual content information (e.g., ingredients, dish presentation) but also procedure of food preparation (cutting and cooking styles). The paired data is leveraged to train deep models to retrieve recipes for food images. Most recipes on the Web include sample pictures as the references. The paired multimedia data is not noise-free, due to errors such as pairing of images containing partially prepared dishes with recipes. The content of recipes and food images are not always consistent due to free-style writing and preparation of food …
A Blockchain-Based Self-Tallying Voting Protocol In Decentralized Iot, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani
A Blockchain-Based Self-Tallying Voting Protocol In Decentralized Iot, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani
Research Collection School Of Computing and Information Systems
The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused …
Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang
Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application …
Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu
Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu
Research Collection School Of Computing and Information Systems
User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Research Collection School Of Computing and Information Systems
The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and …
Lessons Learnt Conducting Capture The Flag Cybersecurity Competition During Covid-19, Kee Hock Tan, Eng Lieh Ouh
Lessons Learnt Conducting Capture The Flag Cybersecurity Competition During Covid-19, Kee Hock Tan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
This innovative practice full paper describes our experiences conducting cybersecurity capture the flag (CTF) competition for cybersecurity enthusiast participants (inclusive of both tertiary students and working professionals) local and abroad during the COVID-19 pandemic. Learning and appreciation of cybersecurity concepts for our participants with little to no technical background can be challenging. Gamification methods such as capture the flag competition style is a popular form of cybersecurity education to help participants overcome this challenge and identify talents. Participants get to apply theoretical concepts in a controlled environment, solve hands-on tasks in an informal, game-like setting and gain hands-on active learning …
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan
Research Collection School Of Computing and Information Systems
It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various …
Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Research Collection School Of Computing and Information Systems
The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-based data gathering for every layer printed. Current crystal plasticity finite element (CPFE) modelling is capable of predicting the associated strength based on a microstructural image and material data but is computationally expensive. This work utilizes a large database of input–output samples from CPFE modelling to develop a trained deep neural network (DNN) model which instantly estimates the …
Delta Debugging Microservice Systems With Parallel Optimization, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Wenha Li, Dan Ding
Delta Debugging Microservice Systems With Parallel Optimization, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Wenha Li, Dan Ding
Research Collection School Of Computing and Information Systems
Microservice systems are complicated due to their runtime environments and service communications. Debugging a failure involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations). Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas during delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines …
Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude
Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude
Research Collection School Of Computing and Information Systems
Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To understand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and …
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Research Collection School Of Computing and Information Systems
Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware …
Linear Algebra For Computer Science, M. Thulasidas
Linear Algebra For Computer Science, M. Thulasidas
Research Collection School Of Computing and Information Systems
This textbook introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science. The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.