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Articles 601 - 630 of 2211
Full-Text Articles in Software Engineering
Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Context: In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. Objective: Towards tackling the challenge, we conduct a study on analyzing user concerns about in-app advertisement. Method: Specifically, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues …
Active Learning Of Discriminative Subgraph Patterns For Api Misuse Detection, Hong Jin Kang, David Lo
Active Learning Of Discriminative Subgraph Patterns For Api Misuse Detection, Hong Jin Kang, David Lo
Research Collection School Of Computing and Information Systems
A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs). API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives. One limitation of prior work is the inability to reliably identify correct patterns of usage. Many approaches confuse a usage pattern’s frequency for correctness. Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses. We …
Post2vec: Learning Distributed Representations Of Stack Overflow Posts, Bowen Xu, Thong Hoang, Abhishek Sharma, Chengran Yang, Xin Xia, David Lo
Post2vec: Learning Distributed Representations Of Stack Overflow Posts, Bowen Xu, Thong Hoang, Abhishek Sharma, Chengran Yang, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
Past studies have proposed solutions that analyze Stack Overflow content to help users find desired information or aid various downstream software engineering tasks. A common step performed by those solutions is to extract suitable representations of posts; typically, in the form of meaningful vectors. These vectors are then used for different tasks, for example, tag recommendation, relatedness prediction, post classification, and API recommendation. Intuitively, the quality of the vector representations of posts determines the effectiveness of the solutions in performing the respective tasks. In this work, to aid existing studies that analyze Stack Overflow posts, we propose a specialized deep …
Broken External Links On Stack Overflow, Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang, Ying Zou, Ahmed E. Hassan, Shanping Li
Broken External Links On Stack Overflow, Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang, Ying Zou, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2 percent of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of …
Defectchecker: Automated Smart Contract Defect Detection By Analyzing Evm Bytecode, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Defectchecker: Automated Smart Contract Defect Detection By Analyzing Evm Bytecode, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Research Collection School Of Computing and Information Systems
Smart contracts are Turing-complete programs running on the blockchain. They are immutable and cannot be modified, even when bugs are detected. Therefore, ensuring smart contracts are bug-free and well-designed before deploying them to the blockchain is extremely important. 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. Detecting and removing contract defects can avoid potential bugs and make programs more robust. Our previous work defined 20 contract defects for smart contracts and divided them into five impact levels. According to …
Simultaneous Material Identification And Target Imaging With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang
Simultaneous Material Identification And Target Imaging With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang
Research Collection School Of Computing and Information Systems
Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the …
Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan
Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan
Research Collection School Of Computing and Information Systems
The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. …
Emerging App Issue Identification Via Online Joint Sentiment-Topic Tracing, Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Emerging App Issue Identification Via Online Joint Sentiment-Topic Tracing, Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Millions of mobile apps are available in app stores, such as Apple’s App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging …
A Deep Dive Into The Impact Of Covid-19 On Software Development, Paulo Anselmo Da Mota Silveira Neto, Umme Ayda Mannan, Eduardo Santana De Almeida, Nachiappan Nagappan, David Lo, Pavneet Singh Kochhar, Cuiyun Gao, Iftekhar Ahmed
A Deep Dive Into The Impact Of Covid-19 On Software Development, Paulo Anselmo Da Mota Silveira Neto, Umme Ayda Mannan, Eduardo Santana De Almeida, Nachiappan Nagappan, David Lo, Pavneet Singh Kochhar, Cuiyun Gao, Iftekhar Ahmed
Research Collection School Of Computing and Information Systems
The COVID-19 pandemic is considered as the most crucial global health calamity of the century. It has impacted different business sectors around the world and software development is not an exception. This study investigates the impact of COVID-19 on software projects and software development professionals. We conducted a mining software repository study based on 100 GitHub projects developed in Java using ten different metrics. Next, we surveyed 279 software development professionals for better understanding the impact of COVID-19 on daily activities and wellbeing. We identified 12 observations related to productivity, code quality, and wellbeing. Our findings highlight that the impact …
Modeling Functional Similarity In Source Code With Graph-Based Siamese Networks, Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
Modeling Functional Similarity In Source Code With Graph-Based Siamese Networks, Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare
Research Collection School Of Computing and Information Systems
Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) …
Enjoy Your Observability: An Industrial Survey Of Microservice Tracing And Analysis, Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, Xuanzhe Liu
Enjoy Your Observability: An Industrial Survey Of Microservice Tracing And Analysis, Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, Xuanzhe Liu
Research Collection School Of Computing and Information Systems
Microservice systems are often deployed in complex cloud-based environments and may involve a large number of service instances being dynamically created and destroyed. It is thus essential to ensure observability to understand these microservice systems’ behaviors and troubleshoot their problems. As an important means to achieve the observability, distributed tracing and analysis is known to be challenging. While many companies have started implementing distributed tracing and analysis for microservice systems, it is not clear whether existing approaches fulfill the required observability. In this article, we present our industrial survey on microservice tracing and analysis through interviewing developers and operation engineers …
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 …
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 …
"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 …
Mind The Gap: Reimagining An Interactive Programming Course For The Synchronous Hybrid Classroom, Christopher M. Poskitt, Kyong Jin Shim, Yi Meng Lau, Hong Seng Ong
Mind The Gap: Reimagining An Interactive Programming Course For The Synchronous Hybrid Classroom, Christopher M. Poskitt, Kyong Jin Shim, Yi Meng Lau, Hong Seng Ong
Research Collection School Of Computing and Information Systems
COVID-19 has significantly affected universities, forcing many courses to be delivered entirely online. As countries bring the pandemic under control, a potential way to safely resume some face-to-face teaching is the synchronous hybrid classroom, in which physically and remotely attending students are taught simultaneously. This comes with challenges, however, including the risk that remotely attending students perceive a ‘gap’ between their engagement and that of their physical peers. In this experience report, we describe how an interactive programming course was adapted to hybrid delivery in a way that mitigated this risk. Our solution centred on the use of a professional …
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 …
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 …
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 …
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 …
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 …
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 …
A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo
A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
In 2006, Geoffrey Hinton proposed the concept of training "Deep Neural Networks (DNNs)" and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in …
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 …
Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto
Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of open-source scientific software which implements bleeding-edge science in its code. However, aligning the link between GitHub repositories and academic papers can prove difficult, and the current practice of establishing and maintaining such links remains unknown. This paper investigates the role of academic paper references contained in these repositories. We conduct a large-scale study of 20 thousand GitHub repositories that make references to academic papers. We use a mixed-methods approach to identify public access, traceability and evolutionary aspects of the links. Although referencing a paper is …
Contextual Documentation Referencing On Stack Overflow, Sebastian Baltes, Christoph Treude, Martin P. Robillard
Contextual Documentation Referencing On Stack Overflow, Sebastian Baltes, Christoph Treude, Martin P. Robillard
Research Collection School Of Computing and Information Systems
Software engineering is knowledge-intensive and requires software developers to continually search for knowledge, often on community question answering platforms such as Stack Overflow. Such information sharing platforms do not exist in isolation, and part of the evidence that they exist in a broader software documentation ecosystem is the common presence of hyperlinks to other documentation resources found in forum posts. With the goal of helping to improve the information diffusion between Stack Overflow and other documentation resources, we conducted a study to answer the question of how and why documentation is referenced in Stack Overflow threads. We sampled and classified …
An Empirical Study Of Developers' Discussions About Security Challenges Of Different Programming Languages, Roland Croft, Yongzheng Xie, Mansooreh Zahedi, Muhammad Ali Babar, Christoph Treude
An Empirical Study Of Developers' Discussions About Security Challenges Of Different Programming Languages, Roland Croft, Yongzheng Xie, Mansooreh Zahedi, Muhammad Ali Babar, Christoph Treude
Research Collection School Of Computing and Information Systems
In collaborative software development projects, work items are used as a mechanism to coordinate tasks and track shared development work. In this paper, we explore how “tagging,” a lightweight social computing mechanism, is used to communicate matters of concern in the management of development tasks. We present the results from two empirical studies over 36 and 12 months, respectively, on how tagging has been adopted and what role it plays in the development processes of several professional development projects with more than 1,000 developers in total. Our research shows that the tagging mechanism was eagerly adopted by the teams, and …
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 …