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Articles 691 - 720 of 2211

Full-Text Articles in Software Engineering

Biasrv: Uncovering Biased Sentiment Predictions At Runtime, Zhou Yang, Muhammad Hilmi Asyrofi, David Lo Aug 2021

Biasrv: Uncovering Biased Sentiment Predictions At Runtime, Zhou Yang, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the community still lacks tools that can monitor and uncover biased predictions at runtime. This paper fills this gap by proposing BiasRV, the first tool to raise an alarm when a deployed SA system makes a biased prediction on a given input text. To implement this feature, BiasRV dynamically extracts a template from an input text and from the template generates gender-discriminatory mutants (semanticallyequivalent texts …

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On The Generalizability Of Neural Program Models With Respect To Semantic-Preserving Program Transformations, Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang Jul 2021

On The Generalizability Of Neural Program Models With Respect To Semantic-Preserving Program Transformations, Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Context: With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Objective: Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect …

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Deep Transfer Bug Localization, Xuan Huo, Ferdian Thung, Ming Li, David Lo, Shu-Ting Shi Jul 2021

Deep Transfer Bug Localization, Xuan Huo, Ferdian Thung, Ming Li, David Lo, Shu-Ting Shi

Research Collection School Of Computing and Information Systems

Many projects often receive more bug reports than what they can handle. To help debug and close bug reports, a number of bug localization techniques have been proposed. These techniques analyze a bug report and return a ranked list of potentially buggy source code files. Recent development on bug localization has resulted in the construction of effective supervised approaches that use historical data of manually localized bugs to boost performance. Unfortunately, as highlighted by Zimmermann et al., sufficient bug data is often unavailable for many projects and companies. This raises the need for cross-project bug localization -- the use of …

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A Differentially Private Task Planning Framework For Spatial Crowdsourcing, Qian Tao, Yongxin Tong, Shuyuan Li, Yuxiang Zeng, Zimu Zhou, Ke Xu Jul 2021

A Differentially Private Task Planning Framework For Spatial Crowdsourcing, Qian Tao, Yongxin Tong, Shuyuan Li, Yuxiang Zeng, Zimu Zhou, Ke Xu

Research Collection School Of Computing and Information Systems

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue …

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Self-Supervised Contrastive Learning For Code Retrieval And Summarization Via Semantic-Preserving Transformations, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang Jul 2021

Self-Supervised Contrastive Learning For Code Retrieval And Summarization Via Semantic-Preserving Transformations, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of code which can be applied to code retrieval tasks that do not have labeled data; (2) it can be used in a fine-tuning process for tasks that might still require label data such as code summarization. The key innovation is that we train the source code model by asking it to recognize similar …

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Rnnrepair: Automatic Rnn Repair Via Model-Based Analysis, Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing Jul 2021

Rnnrepair: Automatic Rnn Repair Via Model-Based Analysis, Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing

Research Collection School Of Computing and Information Systems

Deep neural networks are vulnerable to adversarial attacks. Due to their black-box nature, it is rather challenging to interpret and properly repair these incorrect behaviors. This paper focuses on interpreting and repairing the incorrect behaviors of Recurrent Neural Networks (RNNs). We propose a lightweight model-based approach (RNNRepair) to help understand and repair incorrect behaviors of an RNN. Specifically, we build an influence model to characterize the stateful and statistical behaviors of an RNN over all the training data and to perform the influence analysis for the errors. Compared with the existing techniques on influence function, our method can efficiently estimate …

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Efficient White-Box Fairness Testing Through Gradient Search, Lingfeng Zhang, Yueling Zhang, Min Zhang Jul 2021

Efficient White-Box Fairness Testing Through Gradient Search, Lingfeng Zhang, Yueling Zhang, Min Zhang

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory …

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Bias Field Poses A Threat To Dnn-Based X-Ray Recognition, Bingyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin Jul 2021

Bias Field Poses A Threat To Dnn-Based X-Ray Recognition, Bingyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin

Research Collection School Of Computing and Information Systems

Chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. Many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, posing a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the adversarial attack and propose a brand new attack, i.e., adversarial bias field attack where …

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Stealing Deep Reinforcement Learning Models For Fun And Profit, Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu Jul 2021

Stealing Deep Reinforcement Learning Models For Fun And Profit, Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu

Research Collection School Of Computing and Information Systems

This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only from its interaction with the environment. Model extraction attacks against supervised Deep Learning models have been widely studied. However, those techniques cannot be applied to the reinforcement learning scenario due to DRL models' high complexity, stochasticity and limited observable information. We propose a novel methodology to overcome the above challenges. The key insight of our approach is that the process of DRL model extraction is equivalent to imitation learning, a well-established solution to learn …

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Attack As Defense: Characterizing Adversarial Examples Using Robustness, Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun Jul 2021

Attack As Defense: Characterizing Adversarial Examples Using Robustness, Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to adversarial attacks. Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks. In this work, we propose a novel characterization to distinguish adversarial examples from benign ones based on the observation that adversarial examples are significantly less robust than benign ones. As existing robustness measurement does not scale to large networks, we propose a novel defense framework, named attack …

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Emotioncues: Emotion-Oriented Visual Summarization Of Classroom Videos, Haipeng Zeng, Xinhuan Shu, Yanbang Wang, Yong Wang, Liguo Zhang, Ting-Chuen Pong, Huamin Qu Jul 2021

Emotioncues: Emotion-Oriented Visual Summarization Of Classroom Videos, Haipeng Zeng, Xinhuan Shu, Yanbang Wang, Yong Wang, Liguo Zhang, Ting-Chuen Pong, Huamin Qu

Research Collection School Of Computing and Information Systems

Analyzing students' emotions from classroom videos can help both teachers and parents quickly know the engagement of students in class. The availability of high-definition cameras creates opportunities to record class scenes. However, watching videos is time-consuming, and it is challenging to gain a quick overview of the emotion distribution and find abnormal emotions. In this paper, we propose EmotionCues, a visual analytics system to easily analyze classroom videos from the perspective of emotion summary and detailed analysis, which integrates emotion recognition algorithms with visualizations. It consists of three coordinated views: a summary view depicting the overall emotions and their dynamic …

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Vibransee: Enabling Simultaneous Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra Jul 2021

Vibransee: Enabling Simultaneous Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Driven by the ubiquitous proliferation of low-cost LED luminaires, visible light communication (VLC) has been established as a high-speed communications technology based on the high-frequency modulation of an optical source. In parallel, Visible Light Sensing (VLS) has recently demonstrated how vision-based at-a-distance sensing of mechanical vibrations (e.g., of factory equipment) can be performed using high frequency optical strobing. However, to date, exemplars of VLC and VLS have been explored in isolation, without consideration of their mutual dependencies. In this work, we explore whether and how high-throughput VLC and high-coverage VLS can be simultaneously supported. We first demonstrate the existence of …

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Riding Through The Silver Tsunami: A Data Driven Approach To Improve Senior Citizens’ Engagement With Community Senior Activity Centres, Joshua Jie Feng Lam, Hwee-Pink Tan Jun 2021

Riding Through The Silver Tsunami: A Data Driven Approach To Improve Senior Citizens’ Engagement With Community Senior Activity Centres, Joshua Jie Feng Lam, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In Singapore, 1 in 4 persons will be elderly by 2030 In preparation for the Silver Tsunami, the Singapore government and community care providers have collaborations to promote active, independent living amongst elders Current implementation of data driven population health is focused on well being indices using data collected from the general population There is no literature on the use of data analytics in assessing elder

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Secure Repackage-Proofing Framework For Android Apps Using Collatz Conjecture, Haoyu Ma, Shijia Li, Debin Gao, Chunfu Jia Jun 2021

Secure Repackage-Proofing Framework For Android Apps Using Collatz Conjecture, Haoyu Ma, Shijia Li, Debin Gao, Chunfu Jia

Research Collection School Of Computing and Information Systems

App repackaging has been raising serious concerns about the health of the Android ecosystem, and repackage-proofing is an important mitigation against threat of such attacks. However, existing app repackage-proofing schemes were only evaluated against trivial adversaries simulated using analyzers for other purposes (e.g., disclosing privacy leakage vulnerabilities), hence were shown “effective” mainly because their key programming features were not even supported by those toolkits. Furthermore, existing works have also neglected dynamic adversaries capable of manipulating victim apps at runtime, making them vulnerable against such stronger opponents. In this paper, we propose a novel repackage-proofing framework, which deploys distributed detection and …

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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 Jun 2021

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 …

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Sql-Like Interpretable Interactive Video Search, Jiaxin Wu, Phuong Anh Nguyen, Zhixin Ma, Chong-Wah Ngo Jun 2021

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 …

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Sequence-To-Sequence Learning For Automated Software Artifact Generation, Zhongxin Liu, Xin Xia, David Lo Jun 2021

Sequence-To-Sequence Learning For Automated Software Artifact Generation, Zhongxin Liu, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

During the development and maintenance of a software system, developers produce many digital artifacts besides source code, e.g., requirement documents, code comments, change history, bug reports, etc. Such artifacts are valuable for developers to understand and maintain the software system. However, creating software artifacts can be burdensome and developers sometimes neglect to write and maintain important artifacts. This problem can be alleviated by software artifact generation tools, which can assist developers in creating software artifacts and automatically generate artifacts to replace existing empty ones. The focus of this chapter is automated software artifact generation (hereon, SAG) using seq2seq learning. This …

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A Large Scale Study Of Long-Time Contributor Prediction For Github Projects, Lingfeng Bao, Xin Xia, David Lo, Gail C. Murphy Jun 2021

A Large Scale Study Of Long-Time Contributor Prediction For Github Projects, Lingfeng Bao, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

The continuous contributions made by long time contributors (LTCs) are a key factor enabling open source software (OSS) projects to be successful and survival. We study Github as it has a large number of OSS projects and millions of contributors, which enables the study of the transition from newcomers to LTCs. In this paper, we investigate whether we can effectively predict newcomers in OSS projects to be LTCs based on their activity data that is collected from Github. We collect Github data from GHTorrent, a mirror of Github data. We select the most popular 917 projects, which contain 75,046 contributors. …

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Incorrectness Logic For Graph Programs, Christopher M. Poskitt Jun 2021

Incorrectness Logic For Graph Programs, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for a nondeterministic graph programming language, and show how it can be used to reason deductively about program incorrectness, whether defined by the presence of forbidden graph structure or by finitely failing executions. We prove this 'incorrectness logic' to be sound and complete, and speculate on some possible future applications …

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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 May 2021

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 …

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Oidpr: Optimized Insulin Dosage Via Privacy‐Preserving Reinforcement Learning, Zuobin Ying, Yun Zhang, Shuanglong Cao, Shengmin Xu, Maode Ma May 2021

Oidpr: Optimized Insulin Dosage Via Privacy‐Preserving Reinforcement Learning, Zuobin Ying, Yun Zhang, Shuanglong Cao, Shengmin Xu, Maode Ma

Research Collection School Of Computing and Information Systems

The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving …

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Infercode: Self-Supervised Learning Of Code Representations By Predicting Subtrees, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang May 2021

Infercode: Self-Supervised Learning Of Code Representations By Predicting Subtrees, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Learning code representations has found many uses in software engineering, such as code classification, code search, code comment generation, and bug prediction. Although representations of code in tokens, syntax trees, dependency graphs, paths in trees, or the combinations of their variants have been proposed, existing learning techniques have a major limitation that these models are often trained on datasets labeled for specific downstream tasks, and the code representations may not be suitable for other tasks. Even though some techniques generate representations from unlabeled code, they are far from satisfactory when applied to downstream tasks. To overcome the limitation, this paper …

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Deeplight: Robust And Unobtrusive Real-Time Screen-Camera Communication For Real-World Displays, Vu Huy Tran, Gihan Jayatilaka, Ashwin Ashok, Archan Misra May 2021

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 …

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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 May 2021

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 …

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Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng May 2021

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 …

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Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun May 2021

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 …

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Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang May 2021

Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang

Research Collection School Of Computing and Information Systems

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions …

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Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang May 2021

Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang

Research Collection School Of Computing and Information Systems

For immigrant families, instant messaging family groups are a common platform for sharing and discussing health-related information. Immigrants often maintain contact with their family abroad and trust information in shared IM family groups more than the information from local authorities and sources. In this study, we aimed to understand health-related information behaviors of immigrant families in their IM family groups. Based on the interviews with 6 participants from immigrant families to Canada, we found that immigrant families’ discourse on IM platforms is motivated by love and care for other family members. The families used local and international sources of information, …

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Business-Driven Technical Debt Prioritization: An Industrial Case Study, Rodrigo Rebouças De Almeida, Rafael Do Nascimento Ribeiro, Christoph Treude, ‪Uirá Kulesza May 2021

Business-Driven Technical Debt Prioritization: An Industrial Case Study, Rodrigo Rebouças De Almeida, Rafael Do Nascimento Ribeiro, Christoph Treude, ‪Uirá Kulesza

Research Collection School Of Computing and Information Systems

Incorporating the business perspective into prioritizing technical debt is essential to contribute to decision making in industry. In this paper, we evolve and evaluate a businessdriven approach for technical debt prioritization. The approach was evaluated during a five-months industrial case study with business and technical stakeholders’ active participation. The results show that the approach contributed to aligning business criteria between the business and technical stakeholders. We also observed a downward trend in the amount of technical debt that affects high-value business assets. Moreover, we identified eight business factors that affect the decision making related to the prioritization of technical debt. …

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Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude May 2021

Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude

Research Collection School Of Computing and Information Systems

Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, …

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