Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (239)
- Programming Languages and Compilers (198)
- Artificial Intelligence and Robotics (142)
- Engineering (142)
- Computer Engineering (126)
-
- Information Security (109)
- OS and Networks (80)
- Graphics and Human Computer Interfaces (78)
- Social and Behavioral Sciences (76)
- Numerical Analysis and Scientific Computing (59)
- Theory and Algorithms (46)
- Business (43)
- Computer and Systems Architecture (39)
- Digital Communications and Networking (39)
- Medicine and Health Sciences (33)
- Communication (30)
- Education (28)
- Systems Architecture (21)
- Health Information Technology (20)
- Sociology (18)
- Finance and Financial Management (15)
- Gerontology (15)
- Public Affairs, Public Policy and Public Administration (15)
- Higher Education (13)
- Social Media (13)
- Transportation (12)
- Technology and Innovation (11)
- Keyword
-
- Deep learning (54)
- Software engineering (49)
- Empirical study (43)
- Machine learning (35)
- Software (30)
-
- Android (29)
- Model Check (29)
- Collaboration (26)
- Deep Learning (24)
- GitHub (22)
- Stack Overflow (22)
- Fuzzing (21)
- Security (21)
- Testing (20)
- Data mining (19)
- Large language models (18)
- Programming (18)
- Software Engineering (18)
- Code search (17)
- Computer bugs (17)
- Codes (16)
- Empirical Study (16)
- Information retrieval (16)
- Large Language Models (16)
- Large language model (16)
- Software testing (15)
- Large Language Model (14)
- Linear Temporal Logic (14)
- Software maintenance (14)
- Vulnerability detection (14)
- Publication Year
- Publication
- Publication Type
Articles 301 - 330 of 2211
Full-Text Articles in Software Engineering
Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude
Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude
Research Collection School Of Computing and Information Systems
Deep learning techniques applied to program analysis tasks such as code classification, summarization, and bug detection have seen widespread interest. Traditional approaches, however, treat programming source code as natural language text, which may neglect significant structural or semantic details. Additionally, most current methods of representing source code focus solely on the code, without considering beneficial additional context. This paper explores the integration of static analysis and additional context such as bug reports and design patterns into source code representations for deep learning models. We use the Abstract Syntax Tree-based Neural Network (ASTNN) method and augment it with additional context information …
Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo
Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo
Research Collection School Of Computing and Information Systems
The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users' interactions with the app.However, rarely used features may not be recorded during the data collection, and users' preferences may change slightly over time. To address these …
Assessing Ai Detectors In Identifying Ai-Generated Code: Implications For Education, Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong, Yung Xin Shin, Yeong Shian Poon, Zhou Yang, Chun Yong Chong, David Lo, Mei Kuan Lim
Assessing Ai Detectors In Identifying Ai-Generated Code: Implications For Education, Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong, Yung Xin Shin, Yeong Shian Poon, Zhou Yang, Chun Yong Chong, David Lo, Mei Kuan Lim
Research Collection School Of Computing and Information Systems
Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct.In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were …
Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo
Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of …
Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo
Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo
Research Collection School Of Computing and Information Systems
Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerful models, there remain several scenarios where developers cannot take full advantage of them, stemming from factors such as restricted or unreliable internet access, institutional privacy policies that prohibit external transmission of code to third-party vendors, and more. Therefore, developing a compact, efficient, and yet energy-saving model for deployment on developers' devices becomes essential.To this aim, we propose Avatar, a novel approach that crafts a deployable model from a large language model of code by optimizing …
Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu
Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu
Research Collection School Of Computing and Information Systems
Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue.In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to …
Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang
Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results.In this paper, we propose a new patch presence test framework named Ppt4J …
Exploiting Library Vulnerability Via Migration-Based Automated Test Generation, Zirui Chen, Xing Hu, Xin Xia, Yi Gao, Tongtong Xu, David Lo, Xiaohu Yang
Exploiting Library Vulnerability Via Migration-Based Automated Test Generation, Zirui Chen, Xing Hu, Xin Xia, Yi Gao, Tongtong Xu, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclosed, project maintainers need to determine whether their projects are affected by the vulnerability, which requires developers to invest substantial effort in assessment. However, existing tools face a series of issues: static analysis tools produce false alarms, dynamic analysis tools require existing tests and test generation tools have low success rates when facing complex vulnerabilities.Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability …
Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang
Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Test migration, which enables the reuse of test cases crafted with knowledge and creativity by testers across various platforms and programming languages, has exhibited effectiveness in mobile app testing. However, unit test migration at the source code level has not garnered adequate attention and exploration. In this paper, we propose a novel cross-language and cross-platform test migration methodology, named MUT, which consists of four modules: code mapping, test case filtering, test case translation, and test case adaptation. MUT initially calculates code mappings to establish associations between source and target projects, and identifies suitable unit tests for migration from the source …
Streamlining Java Programming: Uncovering Well-Formed Idioms With Idiomine, Yanming Yang, Xing Hu, Xin Xia, David Lo, Xiaohu Yang
Streamlining Java Programming: Uncovering Well-Formed Idioms With Idiomine, Yanming Yang, Xing Hu, Xin Xia, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Code idioms are commonly used patterns, techniques, or practices that aid in solving particular problems or specific tasks across multiple software projects. They can improve code quality, performance, and maintainability, and also promote program standardization and reuse across projects. However, identifying code idioms is significantly challenging, as existing studies have still suffered from three main limitations. First, it is difficult to recognize idioms that span non-contiguous code lines. Second, identifying idioms with intricate data flow and code structures can be challenging. Moreover, they only extract dataset-specific idioms, so common idioms or well-established code/design patterns that are rarely found in datasets …
Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
Research Collection School Of Computing and Information Systems
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, …
Towards Speedy Permission-Based Debloating For Android Apps, Thung Ferdian, Jiakun Liu, Pattarakrit Rattanukul, Shahar Maoz, Eran Toch, Debin Gao, David Lo
Towards Speedy Permission-Based Debloating For Android Apps, Thung Ferdian, Jiakun Liu, Pattarakrit Rattanukul, Shahar Maoz, Eran Toch, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Android apps typically include many functionalities that not all users require. These result in software bloat that increases possible attack surface and app size. Common functionalities that users may not require are related to permissions that they intend to disallow in the first place. As these permissions are disallowed, their related code would never be executed and therefore can be safely removed. Existing work has proposed a solution to debloat Android apps according to the disallowed permissions. However, for large and complex applications, the debloating process could take hours, typically due the long time that may be needed to construct …
Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang
Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang
Research Collection School Of Computing and Information Systems
Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …
Stealthy Backdoor Attack For Code Models, Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo
Stealthy Backdoor Attack For Code Models, Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo
Research Collection School Of Computing and Information Systems
Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with backdoor attacks. To this end, we propose A (dversarial eature as daptive Back). A achieves stealthiness …
Teaching Software Development For Real-World Problems Using A Microservice-Based Collaborative Problem-Solving Approach, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang
Teaching Software Development For Real-World Problems Using A Microservice-Based Collaborative Problem-Solving Approach, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Experienced and skillful software developers are needed in organizations to develop software products effective for their business with shortened time-to-market. Such developers will not only need to code but also be able to work in teams and collaboratively solve real-world problems that organizations arefacing. It is challenging for educators to nurture students to become such developers with strong technical, social, and cognitive skills. Towards addressing the challenge, this study presents a Collaborative Software Development Project Framework for a course that focuses on learning microservices architectures anddeveloping a software application for a real-world business. Students get to work in teams to …
W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy
W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy
Research Collection School Of Computing and Information Systems
Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …
Improving Automated Code Reviews: Learning From Experience, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet
Improving Automated Code Reviews: Learning From Experience, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet
Research Collection School Of Computing and Information Systems
Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to automate the process, teaching large language models to provide reviews on submitted code, just as a human would. A recent approach pre-trained and fine-tuned the code intelligent language model on a large-scale code review corpus. However, such techniques did not fully utilise quality reviews …
Encoding Version History Context For Better Code Representation, Huy Nguyen, Christoph Treude, Patanamon Thongtanunam
Encoding Version History Context For Better Code Representation, Huy Nguyen, Christoph Treude, Patanamon Thongtanunam
Research Collection School Of Computing and Information Systems
With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit code representation for deep learning. Recent papers incorporate contextual data (e.g. call hierarchy) into vector representation to address program comprehension problems. This motivates further studies to explore additional contexts, such as version history, to enhance models' understanding of programs. That is, insights from version history enable recognition of patterns in …
Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen
Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen
Research Collection School Of Computing and Information Systems
Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …
Experience Report: Identifying Common Misconceptions And Errors Of Novice Programmers With Chatgpt, Hua Leong Fwa
Experience Report: Identifying Common Misconceptions And Errors Of Novice Programmers With Chatgpt, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Identifying the misconceptions of novice programmers is pertinent for informing instructors of the challenges faced by their students in learning computer programming. In the current literature, custom tools, test scripts were developed and, in most cases, manual effort to go through the individual codes were required to identify and categorize the errors latent within the students' code submissions. This entails investment of substantial effort and time from the instructors. In this study, we thus propose the use of ChatGPT in identifying and categorizing the errors. Using prompts that were seeded only with the student's code and the model code solution …
Dronlomaly: Runtime Log-Based Anomaly Detector For Dji Drones, Wei Minn, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Dronlomaly: Runtime Log-Based Anomaly Detector For Dji Drones, Wei Minn, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
We present an automated tool for realtime detection of anomalous behaviors while a DJI drone is executing a flight mission. The tool takes sensor data logged by drone at fixed time intervals and performs anomaly detection using a Bi-LSTM model. The model is trained on baseline flight logs from a successful mission physically or via a simulator. The tool has two modules --- the first module is responsible for sending the log data to the remote controller station, and the second module is run as a service in the remote controller station powered by a Bi-LSTM model, which receives the …
Githubinclusifier: Finding And Fixing Non-Inclusive Language In Github Repositories, Liam Todd, John Grundy, Christoph Treude
Githubinclusifier: Finding And Fixing Non-Inclusive Language In Github Repositories, Liam Todd, John Grundy, Christoph Treude
Research Collection School Of Computing and Information Systems
Non-inclusive language in software artefacts has been recognised as a serious problem. We describe a tool to find and fix non-inclusive language in a variety of GitHub repository artefacts. These include various README files, PDFs, code comments, and code. A wide variety of non-inclusive language including racist, ageist, ableist, violent and others are located and issues created, tagging the artefacts for checking. Suggested fixes can be generated using third-party LLM APIs, and approved changes made to documents, including code refactorings, and committed to the repository. The tool and evaluation data are available from: https://github. com/LiamTodd/github-inclusifier
My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
GitHub Sponsors was launched in 2019, enabling donations to opensource software developers to provide financial support, as per GitHub’s slogan: “Invest in the projects you depend on”. However, a 2022 study on GitHub Sponsors found that only two-fifths of developers who were seeking sponsorship received a donation. The study found that, other than internal actions (such as offering perks to sponsors), developers had advertised their GitHub Sponsors profiles on social media, such as Twitter (also known as X). Therefore, in this work, we investigate the impact of tweets that contain links to GitHub Sponsors profiles on sponsorship, as well as …
Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Research Collection School Of Computing and Information Systems
This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.
Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo
Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language …
Unveiling Memorization In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, Donggyun Han, David Lo
Unveiling Memorization In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of lines of code from both open-source and private repositories. A code model memorizes and produces source code verbatim, which potentially contains vulnerabilities, sensitive information, or code with strict licenses, leading to potential security and privacy issues.This paper investigates an important problem: to what extent do code models memorize their training data? We conduct an empirical study to explore memorization in large pre-trained code models. Our study highlights that simply extracting …
Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li
Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which …
Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen
Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen
Research Collection School Of Computing and Information Systems
The rapid progress of autonomous vehicles (AVs) has brought the prospect of a driverless future closer than ever. Recent fatalities, however, have emphasized the importance of safety validation through large-scale testing. Multiple approaches achieve this fully automatically using high-fidelity simulators, i.e., by generating diverse driving scenarios and evaluating autonomous driving systems (ADSs) against different test oracles. While effective at finding violations, these approaches do not identify the decisions and actions that caused them -- information that is critical for improving the safety of ADSs. To address this challenge, we propose ACAV, an automated framework designed to conduct causality analysis for …
Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo
Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo
Research Collection School Of Computing and Information Systems
Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.
Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo
Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present …