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Articles 3601 - 3630 of 8479
Full-Text Articles in Computer Sciences
Tighter Security Proofs For Post-Quantum Key Encapsulation Mechanism In The Multi-Challenge Setting, Zhengyu Zhang, Puwen Wei, Haiyang Xue
Tighter Security Proofs For Post-Quantum Key Encapsulation Mechanism In The Multi-Challenge Setting, Zhengyu Zhang, Puwen Wei, Haiyang Xue
Research Collection School Of Computing and Information Systems
Due to the threat posed by quantum computers, a series of works investigate the security of cryptographic schemes in the quantum-accessible random oracle model (QROM) where the adversary can query the random oracle in superposition. In this paper, we present tighter security proofs of a generic transformations for key encapsulation mechanism (KEM) in the QROM in the multi-challenge setting, where the reduction loss is independent of the number of challenge ciphertexts. In particular, we introduce the notion of multi-challenge OW-CPA (mOW-CPA) security, which captures the one-wayness of the underlying public key encryption (PKE) under chosen plaintext attack in the multi-challenge …
Multi-Label Thoracic Disease Image Classification With Cross-Attention Networks, Congbo Ma, Hu Wang, Steven C. H. Hoi
Multi-Label Thoracic Disease Image Classification With Cross-Attention Networks, Congbo Ma, Hu Wang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology image classification task is significantly more challenging as it is far more expensive to collect the training data where the labeled data is in nature multi-label; and more seriously samples from easy classes often dominate; training data is highly class-imbalanced problem exists in practice as well. To overcome these challenges, in this paper, we propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images, which …
Semi-Supervised Deep Quantization For Cross-Modal Search, Xin Wang, Wenwu Zhu, Chenghao Liu
Semi-Supervised Deep Quantization For Cross-Modal Search, Xin Wang, Wenwu Zhu, Chenghao Liu
Research Collection School Of Computing and Information Systems
The problem of cross-modal similarity search, which aims at making efficient and accurate queries across multiple domains, has become a significant and important research topic. Composite quantization, a compact coding solution superior to hashing techniques, has shown its effectiveness for similarity search. However, most existing works utilizing composite quantization to search multi-domain content only consider either pairwise similarity information or class label information across different domains, which fails to tackle the semi-supervised problem in composite quantization. In this paper, we address the semi-supervised quantization problem by considering: (i) pairwise similarity information (without class label information) across different domains, which captures …
Duplicate Pull Request Detection: When Time Matters, Qingye Wang, Bowen Xu, Xin Xia, Ting Wang, Shanping Li
Duplicate Pull Request Detection: When Time Matters, Qingye Wang, Bowen Xu, Xin Xia, Ting Wang, Shanping Li
Research Collection School Of Computing and Information Systems
In open source communities (e.g., GitHub), developers frequently submit pull requests to fix bugs or add new features during development process. Since the process of pull request is uncoordinated and distributed, it causes massive duplication. Usually, only the first pull request qualified by reviewers can be merged to the main branch of the repository, and the others are regarded as duplication by maintainers. Since the duplication largely aggravates workloads of project reviewers and maintainers, the evolutionary process of open source repositories is delayed. To identify the duplicate pull requests automatically, Ren et al. proposed a state-of-the-art approach that models a …
Comprehending Test Code: An Empirical Study, Chak Shun Yu, Christoph Treude, Maurício Aniche
Comprehending Test Code: An Empirical Study, Chak Shun Yu, Christoph Treude, Maurício Aniche
Research Collection School Of Computing and Information Systems
Developers spend a large portion of their time and effort on comprehending source code. While many studies have investigated how developers approach these comprehension tasks and what factors influence their success, less is known about how developers comprehend test code specifically, despite the undisputed importance of testing. In this paper, we report on the results of an empirical study with 44 developers to understand which factors influence developers when comprehending Java test code. We measured three dependent variables: the total time spent reading a test suite, the ability to identify the overall purpose of a test suite, and the ability …
Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe
Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe
Research Collection School Of Computing and Information Systems
During software maintenance, developers have different information needs (e.g., to understand what type of maintenance activity to perform, the impact of a maintenance activity and its effort). However, information to support developers may be distributed across various sources. Furthermore, information captured in formal architecture documentation may be outdated. In this paper, we put forward a late breaking idea and outline a solution to improve the productivity of developers by providing task-specific recommendations based on concrete information needs that arise during software maintenance.
Tracy: A Business-Driven Technical Debt Prioritization Framework, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza
Tracy: A Business-Driven Technical Debt Prioritization Framework, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza
Research Collection School Of Computing and Information Systems
Technical debt is a pervasive problem in software development. Software development teams have to prioritize debt items and determine whether they should address debt or develop new features at any point in time. This paper presents "Tracy", a framework for the prioritization of technical debt using a business-driven approach built on top of business processes. The current stage of the proposed framework is at the beginning of the third phase of Design Science Research, which is usually divided into the phases of exploration, engineering, and evaluation. The exploration and engineering phases involved the participation of 49 professionals from 12 different …
Solargest: Ubiquitous And Battery-Free Gesture Recognition Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Solargest: Ubiquitous And Battery-Free Gesture Recognition Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Research Collection School Of Computing and Information Systems
We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for …
Weakly-Supervised Deep Anomaly Detection With Pairwise Relation Learning, Guansong Pang, Anton Van Den Hengel, Chuanhua Shen
Weakly-Supervised Deep Anomaly Detection With Pairwise Relation Learning, Guansong Pang, Anton Van Den Hengel, Chuanhua Shen
Research Collection School Of Computing and Information Systems
This paper studies a rarely explored but critical anomaly detection problem: weakly-supervised anomaly detection with limited labeled anomalies and a large unlabeled data set. This problem is very important because it (i) enables anomalyinformed modeling which helps identify anomalies of interests and address the notorious high false positives in unsupervised anomaly detection, and (ii) eliminates the reliance on large-scale and complete labeled anomaly data in fullysupervised settings. However, the problem is especially challenging since we have only limited labeled data for a single class, and moreover, the seen anomalies often cannot cover all types of anomalies (i.e., unseen anomalies). We …
Deep Hashing By Discriminating Hard Examples, Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, Edwin Hancock
Deep Hashing By Discriminating Hard Examples, Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, Edwin Hancock
Research Collection School Of Computing and Information Systems
This paper tackles a rarely explored but critical problem within learning to hash, i.e., to learn hash codes that effectively discriminate hard similar and dissimilar examples, to empower large-scale image retrieval. Hard similar examples refer to image pairs from the same semantic class that demonstrate some shared appearance but have different fine-grained appearance. Hard dissimilar examples are image pairs that come from different semantic classes but exhibit similar appearance. These hard examples generally have a small distance due to the shared appearance. Therefore, effective encoding of the hard examples can well discriminate the relevant images within a small Hamming distance, …
Nonuniform Timeslicing Of Dynamic Graphs Based On Visual Complexity, Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, Huamin Qu
Nonuniform Timeslicing Of Dynamic Graphs Based On Visual Complexity, Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, Huamin Qu
Research Collection School Of Computing and Information Systems
Uniform timeslicing of dynamic graphs has been used due to its convenience and uniformity across the time dimension. However, uniform timeslicing does not take the data set into account, which can generate cluttered timeslices with edge bursts and empty timeslices with few interactions. The graph mining filed has explored nonuniform timeslicing methods specifically designed to preserve graph features for mining tasks. In this paper, we propose a nonuni-form timeslicing approach for dynamic graph visualization. Our goal is to create timeslices of equal visual complexity. To this end, we adapt histogram equalization to create timeslices with a similar number of events, …
End-To-End Deep Reinforcement Learning For Multi-Agent Collaborative Exploration, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan
End-To-End Deep Reinforcement Learning For Multi-Agent Collaborative Exploration, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture. The model combines convolutional neural network to process multi-channel visual inputs, curriculum-based learning, and PPO algorithm for motivation based reinforcement learning. Evaluations show that the proposed method …
Multi-Agent Collaborative Exploration Through Graph-Based Deep Reinforcement Learning, Tianze Luo, Budhitama Subagdja, Ah-Hwee Tan, Ah-Hwee Tan
Multi-Agent Collaborative Exploration Through Graph-Based Deep Reinforcement Learning, Tianze Luo, Budhitama Subagdja, Ah-Hwee Tan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach …
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Research Collection School Of Computing and Information Systems
Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified …
Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak
Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.
Automatic Generation Of Non-Intrusive Updates For Third-Party Libraries In Android Applications, Yue Duan, Lian Gao, Jie Hu, Heng Yin
Automatic Generation Of Non-Intrusive Updates For Third-Party Libraries In Android Applications, Yue Duan, Lian Gao, Jie Hu, Heng Yin
Research Collection School Of Computing and Information Systems
Third-Party libraries, which are ubiquitous in Android apps,have exposed great security threats to end users as they rarelyget timely updates from the app developers, leaving manysecurity vulnerabilities unpatched. This issue is due to thefact that manually updating libraries can be technically nontrivialand time-consuming for app developers. In this paper,we propose a technique that performs automatic generationof non-intrusive updates for third-party libraries in Androidapps. Given an Android app with an outdated library and anewer version of the library, we automatically update the oldlibrary in a way that is guaranteed to be fully backward compatibleand imposes zero impact to the library’s interactionswith …
Be Sensitive And Collaborative: Analyzing Impact Of Coverage Metrics In Greybox Fuzzing, Jinghan Wang, Yue Duan, Wei Song, Heng Yin, Chengyu Song
Be Sensitive And Collaborative: Analyzing Impact Of Coverage Metrics In Greybox Fuzzing, Jinghan Wang, Yue Duan, Wei Song, Heng Yin, Chengyu Song
Research Collection School Of Computing and Information Systems
Coverage-guided greybox fuzzing has become one of the most common techniques for finding software bugs. Coverage metric, which decides how a fuzzer selects new seeds, is an essential parameter of fuzzing and can significantly affect the results. While there are many existing works on the effectiveness of different coverage metrics on software testing, little is known about how different coverage metrics could actually affect the fuzzing results in practice. More importantly, it is unclear whether there exists one coverage metric that is superior to all the other metrics. In this paper, we report the first systematic study on the impact …
Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra
Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra
Research Collection School Of Computing and Information Systems
We explore the use of personal ‘earable’ devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.
Puncturable Proxy Re-Encryption Supporting To Group Messaging Service, Tran Viet Xuan Phuong, Willy Susilo, Jongkil Kim, Guomin Yang, Dongxi Liu
Puncturable Proxy Re-Encryption Supporting To Group Messaging Service, Tran Viet Xuan Phuong, Willy Susilo, Jongkil Kim, Guomin Yang, Dongxi Liu
Research Collection School Of Computing and Information Systems
This work envisions a new encryption primitive for many-to-many paradigms such as group messaging systems. Previously, puncturable encryption (PE) was introduced to provide forward security for asynchronous messaging services. However, existing PE schemes were proposed only for one-to-one communication, and causes a significant overhead for a group messaging system. In fact, the group communication over PE can only be achieved by encrypting a message multiple times for each receiver by the sender’s device, which is usually suitable to restricted resources such as mobile phones or sensor devices. Our new suggested scheme enables to re-encrypt ciphertexts of puncturable encryption by a …
A Lattice-Based Linkable Ring Signature Supporting Stealth Addresses, Zhen Liu, Khoa Nguyen, Guomin Yang, Huaxiong Wang, Duncan S. Wong
A Lattice-Based Linkable Ring Signature Supporting Stealth Addresses, Zhen Liu, Khoa Nguyen, Guomin Yang, Huaxiong Wang, Duncan S. Wong
Research Collection School Of Computing and Information Systems
First proposed in CryptoNote, a collection of popular privacy-centric cryptocurrencies have employed Linkable Ring Signature and a corresponding Key Derivation Mechanism (KeyDerM) for keeping the payer and payee of a transaction anonymous and unlinkable. The KeyDerM is used for generating a fresh signing key and the corresponding public key, referred to as a stealth address, for the transaction payee. The stealth address will then be used in the linkable ring signature next time when the payee spends the coin. However, in all existing works, including Monero, the privacy model only considers the two cryptographic primitives separately. In addition, to be …
Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li
Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li
Research Collection School Of Computing and Information Systems
Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift …
Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, David W. Rosen, Sai-Kit Yeung
Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, David W. Rosen, Sai-Kit Yeung
Research Collection School Of Computing and Information Systems
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the …
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Research Collection School Of Computing and Information Systems
Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …
Self-Refining Deep Symmetry Enhanced Network For Rain Removal, Hong Liu, Hanrong Ye, Xia Li, Wei Shi, Mengyuan Liu, Qianru Sun
Self-Refining Deep Symmetry Enhanced Network For Rain Removal, Hong Liu, Hanrong Ye, Xia Li, Wei Shi, Mengyuan Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Rain removal aims to remove the rain streaks on rain images. Traditional methods based on convolutional neural network (CNN) have achieved impressive results. However, these methods are under-performed when dealing with tilted rain streaks, because CNN is not equivariant to object rotations. To tackle this problem, we propose the Deep Symmetry Enhanced Network (DSEN) that explicitly extracts and learns from rotation-equivariant features from rain images. In addition, we design a self-refining strategy to remove rain streaks in a coarse-to-fine manner. The key idea is to reuse DSEN with an information link which passes the gradient flow to the finer stage. …
Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang
Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang
Research Collection School Of Computing and Information Systems
We investigate the problem of object relationship classification of visual scenes. For a relationship object1-predicate-object2 that captures the object interaction, its representation is composed by the combination of object1 and object2 features. As a result, relationship classification models usually bias to the frequent objects, leading to poor generalization to rare or unseen objects. Inspired by the data augmentation methods, we propose a novel Semantic Transform Generative Adversarial Network (ST-GAN) that synthesizes relationship features for rare objects, conditioned on the features from random instances of the objects. Specifically, ST-GAN essentially offers a semantic transform function from cheap object features to expensive …
Finding Flaws From Password Authentication Code In Android Apps, Siqi Ma, Elisa Bertino, Surya Nepal, Jianru Li, Ostry Diethelm, Robert H. Deng, Sanjay Jha
Finding Flaws From Password Authentication Code In Android Apps, Siqi Ma, Elisa Bertino, Surya Nepal, Jianru Li, Ostry Diethelm, Robert H. Deng, Sanjay Jha
Research Collection School Of Computing and Information Systems
Password authentication is widely used to validate users’ identities because it is convenient to use, easy for users to remember, and simple to implement. The password authentication protocol transmits passwords in plaintext, which makes the authentication vulnerable to eavesdropping and replay attacks, and several protocols have been proposed to protect against this. However, we find that secure password authentication protocols are often implemented incorrectly in Android applications (apps). To detect the implementation flaws in password authentication code, we propose GLACIATE, a fully automated tool combining machine learning and program analysis. Instead of creating detection templates/rules manually, GLACIATE automatically and accurately …
Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng
Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng
Research Collection School Of Computing and Information Systems
Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in …
Directional Co-Clustering, Aghiles Salah, Mohamed Nadif
Directional Co-Clustering, Aghiles Salah, Mohamed Nadif
Research Collection School Of Computing and Information Systems
Co-clustering addresses the problem of simultaneous clustering of both dimensions of a data matrix. When dealing with high dimensional sparse data, co-clustering turns out to be more beneficial than one-sided clustering even if one is interested in clustering along one dimension only. Aside from being high dimensional and sparse, some datasets, such as document-term matrices, exhibit directional characteristics, and the L2 normalization of such data, so that it lies on the surface of a unit hypersphere, is useful. Popular co-clustering assumptions such as Gaussian or Multinomial are inadequate for this type of data. In this paper, we extend the scope …
A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
Research Collection School Of Computing and Information Systems
Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which …
Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude
Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude
Research Collection School Of Computing and Information Systems
Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the …