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Multi-Factor Duplicate Question Detection in Stack Overflow

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Journal of Computer Science & Technology 2015年第5期30卷 981-997页

作者：张芸 david lo 夏鑫孙建伶 College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore Singapore

Stack Overflow is a popular on-line question and answer site for software developers to share their experience and expertise. Among the numerous questions posted in Stack Overflow, two or more of them may express the same point and thus are duplicates of one another. Duplicate questions make Stack Overflow site maintenance harder, waste resources that could have been used to answer other questions, and cause developers to unnecessarily wait for answers that are already available. To reduce the problem of duplicate questions, Stack Overflow allows questions to be manually marked as duplicates of others. Since there are thousands of questions submitted to Stack Overflow every day, manually identifying duplicate questions is a difficult work. Thus, there is a need for an automated approach that can help in detecting these duplicate questions. To address the above-mentioned need, in this paper, we propose an automated approach named DuPPREDICTOR that takes a new question as input and detects potential duplicates of this question by considering multiple factors. DuPPREDICTOR extracts the title and description of a question and also tags that are attached to the question. These pieces of information （title, description, and a few tags） are mandatory information that a user needs to input when posting a question. DuPPREDICTOR then computes the latent topics of each question by using a topic model. Next, for each pair of questions, it computes four similarity scores by comparing their titles, descriptions, latent topics, and tags. These four similarity scores are finally combined together to result in a new similarity score that comprehensively considers the multiple factors. To examine the benefit of DuPPREDICTOR, we perform an experiment on a Stack Overflow dataset which contains a total of more than two million questions. The result shows that DuPPREDICTOR can achieve a recali-rate@20 score of 63.8%. We compare our approach with the standard search engine of Stack Overfl

关键词： software information site duplicate question Stack Overflow DupPredictor

Combined classifier for cross-project defect prediction： an extended empirical study

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Frontiers of Computer Science 2018年第2期12卷 280-296页

作者： Yun ZHANG david lo Xin XIA Jianling SUN College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore 641674 Singapore

To facilitate developers in effective allocation of their testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on the past history of classes, methods, or certain other code elements. These techniques are effective provided that a sufficient amount of data is available to train a prediction model. However, sufficient training data are rarely available for new software projects. To resolve this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, was proposed and is regarded as a new challenge in the area of defect prediction. Thus far, only a few cross-project defect prediction techniques have been proposed. To advance the state of the art, in this study, we investigated seven composite algorithms that integrate multiple machine learning classifiers to improve cross-project defect prediction. To evaluate the performance of the composite algorithms, we performed experiments on 10 open-source software systems from the PROMISE repository, which contain a total of 5,305 instances labeled as defective or clean. We compared the composite algorithms with the combined defect predictor where logistic regression is used as the meta classification algorithm （CODEPlogistic）, which is the most recent cross-project defect prediction algorithm in terms of two standard evaluation metrics： cost effectiveness and F-measure. Our experimental results show that several algorithms outperform CODEPlogistic：Maximum voting shows the best performance in terms of F-measure and its average F-measure is superior to that of CODEPlogistic by 36.88%. Bootstrap aggregation （Bagging J48） shows the best performance in terms of cost effectiveness and its average cost effectiveness is superior to that of CODEPlogistic by 15.34%.

关键词： defect prediction cross-project classifier combination

Automated Android application permission recommendation

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Science China(Information Sciences) 2017年第9期60卷 132-148页

作者： Lingfeng BAO david lo Xin XIA Shanping LI College of Computer Science and Technology Zhejiang University School of Information Systems Singapore Management University Department of Computer Science University of British Columbia

The number of Android applications has increased rapidly as Android is becoming the dominant platform in the smartphone market. Security and privacy are key factors for an Android application to be successful. Android provides a permission mechanism to ensure security and privacy. This permission mechanism requires that developers declare the sensitive resources required by their applications. On installation or during runtime, users are required to agree with the permission request. However, in practice, there are numerous popular permission misuses, despite Android introducing official documents stating how to use these permissions properly. Some data mining techniques(e.g., association rule mining) have been proposed to help better recommend permissions required by an API. In this paper, based on popular techniques used to build recommendation systems, we propose two novel approaches to improve the effectiveness of the prior work. The first approach utilizes a collaborative filtering technique, which is inspired by the intuition that apps that have similar features — inferred from their APIs — usually share similar permissions. The second approach recommends permissions based on a text mining technique that uses a naive Bayes multinomial classification algorithm to build a prediction model by analyzing descriptions of apps. To evaluate these two approaches, we use 936 Android apps from F-Droid, which is a repository of free and open source Android applications. We find that our proposed approaches yield a significant improvement in terms of precision, recall, F1-score, and MAP of the top-k results over the baseline approach.

关键词： Android permission recommendation association rule collaborative filtering text mining

High-Impact Bug Report Identification with Imbalanced Learning Strategies

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Journal of Computer Science & Technology 2017年第1期32卷 181-198页

作者： Xin-Li Yang david lo Xin Xia Qiao Huang Jian-Ling Sun College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore Singapore

In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique）＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling）＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.

关键词： high-impact bug imbalanced learning bug report identification

What Security Questions Do Developers Ask？ A Large-Scale Study of Stack Overflow Posts

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Journal of Computer Science & Technology 2016年第5期31卷 910-924页

作者： Xin-Li Yang david lo Xin Xia Zhi-Yuan Wan Jian-Ling Sun College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore Singapore

Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people＇s attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Diriehlet Allocation （LDA） tuned using Genetic Algorithm （GA）, to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners.

关键词： security Stack Overflow empirical study topic model

A path planning algorithm for a crop monitoring fixed-wing unmanned aerial system

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Science China(Information Sciences) 2024年第8期67卷 5-23页

作者： longhao QIAN Yi lok lo Hugh Hong-tao LIU Institute of Aerospace Studies University of Toronto Department of Mechanical Engineering The University of Hong Kong

With the growing demand for automation in agriculture, industries increasingly rely on drones to perform crop monitoring and surveillance. In this regard, fixed-wing unmanned aerial systems(UASs)are viable platforms for scanning a large crop field, given their payload capacity and range. To achieve maximum coverage without landing for battery replacement, an algorithm for producing a minimal required energy survey path is essential. Hence, an energy-aware coverage path planning algorithm is proposed *** constraints for a fixed-wing UAS to fly at low altitudes while achieving full coverage of the crop field are first analyzed. Then, the full path is decomposed into straight-line and U-turn primitives. Finally, an algorithm to calculate a combination of straight-line segments and U-turns is proposed to obtain the path with minimum required energy consumption. The genetic algorithm is used to efficiently determine the order of the straight-line paths to traverse. Case studies show that the proposed algorithm can produce planning results for a convex-polygon-shaped crop field.

关键词： aerial survey energy minimization fixed-wing UAS genetic algorithm path planning

Tau in the pancreas:understanding the link between type 2 diabetes mellitus and Alzheimer's disease

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Signal Transduction and Targeted Therapy 2024年第1期9卷 6-7页

作者： Wenlu Li Steffen Tiedt Eng H.lo Neuroprotection Research Laboratories Departments of Radiology and NeurologyMassachusetts General HospitalHarvard Medical SchoolCharlestownMA02129USA Consortium International pour la Recherche Circadienne sur l’AVC(CIRCA) BostonUSA Institute for Stroke and Dementia Research LMU University HospitalLMU MunichMunichGermany

A recent study published in Molecular Psychiatry proposes a cellular mechanism that may contribute to decreased insulin production in individuals with type 2 diabetes mellitus(T2DM).1 Using a combination of cell culture experiments,animal models and studies in T2DM patients,the authors show that an up-regulation of tau inβ-islet cells can inhibit pancreatic insulin secretion through changes in microtubule assembly.

关键词： T2DM Alzheimer tau

On locating Malicious Code in Piggybacked Android Apps

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Journal of Computer Science & Technology 2017年第6期32卷 1108-1124页

作者： Li Li Daoyuan Li Tegawende F. Bissyande Jacques Klein Haipeng Cai david lo Yves Le Traon Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg 2721 Luxembourg School of Electrical Engineering and Computer Science Washington State University Washington WA 99163 U.S.A. School of Information Systems Singapore Management University Singapore 178902 Singapore

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently.

关键词： Android piggybacked app malicious code HookRanker

Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

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Computers, Materials & Continua 2024年第6期79卷 4533-4549页

作者： Chen Chen Zhongwei Xu Meng Mei Kai Huang Siu Ming lo School of Electronic and Information Engineering Tongji UniversityShanghai201804China Department of Architecture and Civil Engineering City University of Hong KongHong KongChina School of Computer Engineering Jimei UniversityXiamen361021China

Railway switch machine is essential for maintaining the safety and punctuality of train operations.A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed *** existing methods,this approach takes into account the spatial information of the time series monitoring data,aligning with the domain expertise of on-site manual ***,a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient ***,one-dimensional signal data is preprocessed and transformed into two-dimensional ***,the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDE-COMP/PARAFAC(CP)decomposition ***,the tensor learning-based model is built using the extracted fusion feature *** developed fault diagnosis scheme is valid with the field three-phase current *** experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach,particularly in terms of recall,precision,and F1-score.

关键词： Railway switch machine tensor machine fault diagnosis